Ventana Research Analyst Perspectives

Zendesk held its customer conference – Relate – along with a session for industry analysts in May. Zendesk develops software primarily for customer service and support and automating aspects of the sales process through customer relationship management. The conference presented an opportunity for Zendesk to showcase several new technology developments that it hopes will solidify its position in the customer experience marketplace.

Ventana Research expanded its core Office of Finance focus areas this year to reflect new and important opportunities to use technology to gain effectiveness through greater efficiency. The focus area we caption as “Purchasing, Sourcing and Payments” also includes receivables. Doing a better job of record-keeping and organizing paperwork, especially in a minutiae-laden process like order-to-cash, may seem trivial. Yet, when the pandemic required organizations to lock down in early 2020, digitally transforming core business processes became essential, and the need to operate remotely presented a new set of challenges best addressed by software. Ventana Research asserts that by 2026, only one-third of larger organizations will consistently manage order-to-cash end-to-end, but those that do will outperform competitors.

In early 2019, I first wrote about the escalating strategic challenges facing organizations resulting from the decade-long dismantling of the liberal global trade environment and the shift to more restrictive regimes. Supply chain management was upended in 2020 as a series of extraordinary events, especially the pandemic-induced supply chain shocks, compounded the tactical challenges facing supply chain managers and increased awareness of the brittle nature of lowest-cost supply chain structures. The impact of this shift has been accentuated by the recent fracturing of the world’s political order, which is reshaping strategies about where goods are sourced and manufactured. Consequently, supply chains designed for the lowest possible cost had to be restructured for resiliency. However, minimizing costs remains a core objective, so organizations must manage their supply chains and logistics more intelligently, using technology to better match supply and demand while finding other ways to reduce working capital and their associated costs.

MicroStrategy is a long-standing business intelligence and analytics vendor that operates worldwide. Founded in 1989, this publicly traded company with hundreds of millions of dollars in revenue recently held its first in-person conference since prior to the pandemic. Similar to previous in-person events, the event was well attended by about 2,000 attendees and exhibitors. The theme, “MicroStrategy One,” is a way to explain the breadth of capabilities the company offers. The breadth of the product offering is one of the company’s greatest strengths, but also one of its biggest challenges.

Master data management may not attract the same level of excitement as fashionable topics such as DataOps or Data Platforms, but it remains one of the most significant aspects of an organization’s strategic approach to data management. Having trust in data is critical to the ability of an organization to make data-driven business decisions. Along with data quality, MDM enables organizations to ensure data is accurate, complete and consistent to fulfill operational business objectives.

Artificial intelligence (AI) has evolved from a highly specialized niche technology to a worldwide phenomenon. Nearly 9 in 10 organizations use or plan to adopt AI technology. Several factors have contributed to this evolution. First, the amount of data they can collect and store has increased dramatically while the cost of analyzing these large amounts of data has decreased dramatically. Data-driven organizations need to process data in real time which requires AI. In addition, analytics vendors have been augmenting business intelligence (BI) products with AI. And recently, ChatGPT has raised awareness of AI and instigated research and experimentation into new ways in which AI can be applied. This perspective, the second in a series on generative AI, introduces some of the concepts behind ChatGPT, including large language models and transformers. Understanding how these models work can help provide a better understanding of how they should be applied and what cautions are necessary.

Artificial intelligence (AI) has become ubiquitous in discussions of contact center technology. Vendors are rushing to incorporate it into platforms and applications. And end users have understandably mixed feelings about where it makes sense to use and what its impacts will be. No one should be surprised that AI has arrived, especially for customer support: Software companies have been working on customer experience (CX)- -related AI applications for many years, and the fruits of those efforts have been gradually working their way into real-world tools, particularly chatbots. Solutions that help automate the self-service gateway to customer support are able to boost speed and efficiency in customer interactions, and this can lead to faster response times, reduced wait times, and improved first-contact resolution rates, all of which can have a positive impact on customer satisfaction and loyalty.

The past five years have witnessed unprecedented innovation and investment in HCM technology. As quickly as the world of work has changed, so too have new technologies emerged to support those changes to optimize human engagement and business outcomes. Point solutions and platform providers alike have made headlines with advances in talent intelligence, personalization within the flow of work, pay equity analysis, skills gap identification and AI-powered career pathing, to name a few. But one thing that people often overlook is that all this new data and information still needs a central repository; a single source of truth for each worker that provides a complete picture of an individual from hire to exit, and that is found in an organization’s core HCM platform. These platforms have historically been focused on process and workflow automation and compliance, a digital replacement for the personnel folder in a file cabinet and the in-and-out box on the corner of a desk. But in order to support the influx of new data and information from adjacent systems, core HCM technology providers have been evolving as well. The result is a new breed of HCM platform, as accessible and valuable to employees and managers as they are to human resources teams, engineered with user experience as an equal priority to workflow automation and compliance.

The data platforms market has traditionally been divided between products specifically designed to support operational or analytic workloads, with other market segments having emerged in recent years for data platforms targeted specifically at data science and machine learning (ML), as well as real-time analytics. More recently, we have seen vendor strategies evolving to provide a more consolidated approach, with data platforms designed to address a combination of analytics and data science, as well as hybrid operational and analytic processing. Snowflake, which has been hugely successful in recent years with its cloud-based analytic data platform, is a prime example. The company has expanded its purview to address data engineering and data science, as well as transactional data. Additionally, it now provides users with the ability to access and process data in on-premises environments as part of its strategy to address an increasing range of use cases.

We’ve experienced an unusual, decades-long monetary policy in the developed world that emphasized interest rate repression as well as supply chain practices designed for lowest cost. This has produced a generation of chief financial officers likely unprepared to address the impact of fundamental changes now shaping a new, technology-driven approach to working capital optimization. Today’s CFOs must assess their working capital management processes and the systems that support them to balance the need to minimize the cost of working capital against customer requirements such as delivery times and order fulfillment. Technology can minimize frictions – and therefore costs – associated with managing inventories, receivables and payables balanced against customer satisfaction requirements and legal and regulatory considerations.

Organizations are constantly striving for agility and efficiency to stay competitive. From a human-capital standpoint, a logical way to achieve this is by moving away from a job title and education-based workforce and transitioning to one that is skills-based. This approach emphasizes the skills and capabilities of individuals rather than their prior job titles or education, enabling organizations to tap into a broader talent pool and adapt to changing business needs. However, creating a skills-based workforce requires overcoming challenges such as defining and measuring skills, changing workforce architecture and gaining the buy-in of human resources and business leaders to an entirely new way of viewing talent. Organizational structures today are largely built around functions with jobs that have titles and tasks to be completed. Individuals are evaluated for these jobs based on education, prior job titles and, sometimes, references, all used as a means of verifying that what is listed on a resume is true. In a skills-based workforce, the value of a resume is voided, as prior job titles and degrees would no longer be the pillars of measurement for a candidate’s qualifications, which I have previously addressed in this analysis as well as offering guidance in this publication on five strategies that should be embraced. Skills, though, cannot be verified by phoning a university or verifying employment tenure and titles.

Organizations are increasingly seeing the strategic benefits of enhanced customer engagement. But many are struggling to understand how to put a broader customer experience strategy into practice amid siloed processes and departments. Particularly in contact centers, new technology has to be melded onto existing platforms in ways that enhance experiences without disrupting operations.

OneStream offers a platform designed to serve the needs of accounting and financial planning and analysis organizations. The software handles financial close and consolidation, planning and budgeting, analysis and reporting. The most notable part of the company’s presentations at its annual user group meeting – Splash – was the strategy and roadmap for its two artificial intelligence initiatives, Sensible ML and Sensible GPT. The former, unveiled last year, is a platform approach to applying machine learning to business forecasting, while the latter harnesses the power of large language models such as ChatGPT to streamline the performance of almost any business process.

I am happy to share insights gleaned from our latest Value Index research, an assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Subscription Management is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to Office of Revenue vendors supporting the spectrum of subscription management. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the product experience ﹘ Adaptability, Capability, Manageability, Reliability and Usability ﹘ and two related to the customer experience ﹘ TCO/ROI and vendor Validation.

Having just completed the 2023 Ventana Research Value Index for Subscription Management, I want to share some of my observations about an important component of subscription management: Contract management. 

Having just completed the 2023 Ventana Research Value Index for Subscription Management, I want to share some of my observations about how the market is evolving. The era of subscriptions has transformed the way businesses operate and consumers engage with services across every industry. Organizations are increasingly adopting a digital approach to selling goods and services, wherein customers have the option to purchase through periodic subscription pricing or based on consumption, rather than traditional one-time ownership. Add to this an increasing desire for customers to purchase when, how and where they want, whether via digital commerce, self-service portals by and by voice and text and the landscape looks very different from even just five years ago. 

In 2015, I began using the term continuous accounting to call attention to technology advances that enable finance and accounting departments to improve performance. These advances have continued, making an even more compelling case for adoption of continuous accounting.

Generative AI is a class of artificial intelligence used to generate new, seemingly real content. Broadly speaking, AI has traditionally been used to identify patterns in data and apply those patterns to categorize and predict behaviors. For instance, it can organize customers into groups (or clusters) with similar characteristics, or predict which customers are most likely to respond to certain offers.

The world is witnessing a long overdue and significant shift in the realm of pay equity and transparency. On March 30, the European Parliament passed the Pay Transparency Directive, mandating pay transparency and equity within organizations in an effort to close the gender pay gap and ensure equal pay for equal work. This legislation comes with teeth, providing strict mechanisms for enforcement including shifting the burden of proof from the worker to the employer in the event of legal challenges, and stiff fines and direct remuneration for non-compliance. Although not yet passed, similar legislation is under consideration in the U.S. Congress in the form of the Salary Transparency Act and the Pay Equity for All Act of 2023. Combined, these laws and proposals are signaling that this trend is likely to become a global standard. Many organizations have already adopted progressive pay transparency and equity practices, but for those that have taken a wait-and-see approach, preparing for the inevitability of pay equity and transparency mandates could prove challenging.

Conga is part of our Office of Revenue market coverage and I recently attended the 2023 Conga Connect conference held in Orlando during April of 2023. The conference was the public reveal of a new Conga, with significant announcements in three main areas. First was the overall positioning within a self-designated category of revenue life cycle management. Secondly was the first public revealing of a new Conga platform. And finally, announcements of new partnerships were headlined by a significant relationship with Accenture.

The recent publication of our Value Index research highlights the impact of intelligent applications on the operational data platforms sector. While we continue to believe that, for most use cases, there is a clear, functional requirement for either analytic or operational data platforms, recent growth in the development of intelligent applications infused with the results of analytic processes, such as personalization and artificial intelligence (AI)-driven recommendations, has increasing influence over requirements for operational data platforms to support real-time analytic functionality. Operational data platform vendors, including MongoDB, are responding to these evolving requirements with new functionality to support the development and deployment of intelligent applications. 

In recent years, organizations have struggled to find and retain the right talent to fill critical roles. This can be attributed to a combination of factors, including an aging workforce, a lack of critical skills needed for current and future organizational needs, and increased competition for talent from other organizations seeking similar skills. Our research shows that, within the next five years, one-half of all employees will require significant reskilling and upskilling in order to keep pace with the changing job market. As a result, organizations are facing a talent shortage and are struggling to fill their open roles with qualified candidates. This poses a clear threat to the ability to execute critical business initiatives. In response, we assert that by 2025, one-half of organizations will implement a more comprehensive approach to determining organizational readiness, including sentiment analysis, skills gaps identification and adjacencies assessments to optimize employee potential.

In my perspective on decision intelligence, I lamented the fact that business intelligence technologies have left the rest of the exercise to the reader for too long. Making a decision is a process that involves many steps and many people. Decision-making is so complicated and divorced from day-to-day business processes that organizations have had to create entirely separate teams to focus on the analytics and data to support it. One aspect of the decision-making process that can be enhanced by technology is collaboration.

As engagement with customers, suppliers and partners is increasingly conducted through digital channels, ensuring that infrastructure and applications are performing as expected is not just important but mission critical. My colleague, David Menninger, recently explained the increasing importance of observability to enable organizations to ensure that their systems and applications are operating efficiently. Observability has previously been the domain of the IT department but is increasingly important for business decision-makers as organizations combine machine-generated telemetry data with business event data to understand the impact of a system outage or application performance degradation on their ability to conduct digital business. Companies such as Mezmo are responding with observability platforms designed to facilitate the integration of machine and business data and encourage collaboration between business and IT professionals.

Contact centers have long collected feedback from customers, usually through short surveys. It is very common for an agent or an automated system to ask for an assessment of the interaction that just occurred, hoping to get the customer's candid, instant view of whether they were satisfied. For the most part, what's learned in those short engagements is very narrow. It can be used for a customer satisfaction snapshot, and it can be used to find out if a particular agent is running into trouble. That is useful, but extremely limited in scope.

Organizations are becoming increasingly aware of the potential value that can be gained by processing big data. As data sources grow, it becomes important to have tools and methods to effectively process, analyze and visualize this information from disparate systems and warehouses.

I have written recently about the increasing importance of managing data in motion and at rest as the use of streaming data by enterprise organizations becomes more mainstream. While batch-based processing of application data has been a core component of enterprise IT architecture for decades, streaming data and event processing have often been niche disciplines typically reserved for organizations with the highest-level performance requirements. That has changed in recent years, driven by an increased reliance on streaming data and events to identify trends and anomalies and enable real-time responses through uninterrupted processing and analysis of data generated by applications, systems and devices on a continuous basis. Companies like Solace have been integral to the development of event-driven applications and increased adoption of event-driven architecture (EDA).

People analytics is the application of data and statistical methods to better understand and optimize the human capital within an organization. It has become a crucial aspect of business strategy as organizations seek to make data-driven decisions about the workforce. In the past decade, the people analytics market has experienced substantial growth, as businesses look for ways to gain insight into the effectiveness and efficiency of human capital investments. Most HCM technology platforms today boast native capabilities.

To execute more data-driven business strategies, organizations need linked and comprehensive data that is available in real time. By consistently managing data across siloed systems and ensuring that data definitions are agreed and current, organizations can overcome the challenges presented by data being distributed across an increasingly disparate range of applications and data-processing locations. Maintaining data quality is a perennial data management challenge, often preventing organizations from operating at the speed of business. Our Analytics and Data Benchmark Research shows that almost two-thirds of participants (64%) cited reviewing data for quality issues as being the most time-consuming aspect of analytics initiatives, second only to preparing data for analysis. This is where master data management (MDM) becomes critical, to ensure that organizations have the clean, consistent data needed to operate efficiently and effectively. When organizations control master data, they gain visibility into their overall operations and can provide proper governance, while also having access to reliable, accurate and timely data about customers, products, assets and employees. Reltio offers MDM products designed to help customers improve trust in data by unifying and cleansing complex data from multiple sources in real time.

The chief financial officer of a midsize organization faces a different set of challenges than those in larger or smaller enterprises. These organizations have grown to the point of requiring capabilities similar to larger businesses, but typically lack the staff or financial resources afforded to bigger organizations. The past decade of IT innovation – especially the expansion of cloud computing – has brought substantial benefits to midsize finance and accounting operations. Rapidly growing midsize organizations in particular should make investments in information technology that allow the business to scale without having to increase administrative head count, and focus resources on areas that accelerate growth, such as sales, logistics, R&D or customer support.

Data Operations (DataOps) has been part of the lexicon of the data market for almost a decade, with the term used to describe products, practices and processes designed to support agile and continuous delivery of data analytics. DataOps takes inspiration from DevOps, which describes a set of tools, practices and philosophy used to support the continuous delivery of software applications in the face of constant changes. DataOps describes a set of tools, practices and philosophy used to ensure the quality, flexibility and reliability of data and analytics initiatives, with an emphasis on continuous measurable improvement, as well as agility, collaboration and automation. Interest in products and services that support DataOps is growing. I assert that by 2025, one-half of organizations will have adopted a DataOps approach to their data engineering processes, enabling them to be more flexible and agile.

I am happy to share insights gleaned from our latest Value Index research, an assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Total Compensation Management 2023 is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to human capital management vendors supporting the spectrum of total compensation management. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the product experience ﹘ Adaptability, Capability, Manageability, Reliability and Usability ﹘ and two related to the customer experience ﹘ TCO/ROI and vendor Validation.

Having just completed the 2023 Ventana Research Value Index for Total Compensation Management, I want to share some of my observations about how the market has advanced since our assessment three years ago. For more than two decades, Ventana Research has conducted market research in a spectrum of related areas including Total Compensation Management (TCM) as well as broader HCM areas ranging from employee experience, learning management, workforce management (WFM) to payroll management and learning. We have also done research on the employee experience and candidate engagement. Our continuous research and analysis of the market for business applications and technologies guide our comprehensive approach to this Value Index.

Early last December, just before ChatGPT became the new, bright, shiny object, The Economist magazine ran a story proclaiming that we had finally arrived at the age of boring artificial intelligence (AI). From my perspective, it’s unfortunate that didn’t last and that AI has been relegated back to the buzzword league. AI will be an increasingly important feature of business software through the end of this decade. Ventana Research asserts that by 2026, almost all vendors of software designed for finance organizations will have incorporated some AI capabilities to reduce workloads and improve performance. The same observation applies, to some significant degree, to other parts of an enterprise, so it’s important for people in operational roles to understand what AI can and cannot do. It’s also important for vendors to clearly and concretely communicate what they mean when they say, “AI-enabled.” Moreover, I prefer the alternative term “augmented intelligence” because it emphasizes that these systems enhance — rather than replace — the capabilities of the humans employing them, especially through improved decision-making and by eliminating the need to perform repetitive work.

Organizations looking to improve the customer experience and contact center operations should closely examine the processes at work in the front-end self-service environment. Technology advancements are converging at this gateway, including artificial intelligence, chatbots and knowledge management. The front end of the customer interaction offers a greater opportunity for customer engagement than most organizations realize.

As data continues to grow and evolve, organizations seek better tools and technologies to employ data faster and more efficiently. Finding and managing data remains a perennial challenge for most organizations, and is exacerbated by increasing volumes of data and an expanding array of data formats. At the same time, organizations must comply with a growing list of national and regional rules and regulations, such as General Data Protection Regulation and the California Consumer Privacy Act. While these regulations protect consumers, they increase complexity for governing and providing access to data.

As a rule, I dislike terms like “digital finance transformation” because there’s a wave-the -magic-wand quality to it that obscures the not-so-simple people and process elements necessary for true transformation. Six of the most common – and expensive – words used in an accounting department are “we’ve always done it this way.” Persuading staff to change can be a struggle, even if change makes their jobs easier and more rewarding. Moreover, digital transformation must cover the data elements as well as the process because manual data management is not only time-consuming at the data preparation stage, but it also creates the need for downstream reconciliations and other tasks to ensure accuracy.

Modern business relies on having a clear, continuous understanding of customer perceptions and intentions. That insight is increasingly used to deliver them experiences that they would consider excellent, in turn leading to higher satisfaction and more profitable relationships. Qualtrics, a provider of Experience Management (XM) software, focuses on creating applications that analyze customer interactions and feedback so that different teams across the enterprise can better identify opportunities and challenges.

At its March 2023 analyst conference, 8x8 described a significant pivot in its focus. Going forward the company will be emphasizing the Contact Center (CCaaS) side of its portfolio, which to this point has represented a small portion of its overall sales. It made several product announcements to bolster the argument that it can effectively meet the needs of complex contact center operations while continuing to provide business users with a UCaaS solution. The overall XCaaS platform is aimed at both markets, which have been converging for several years.  

Organizations are continuously searching for new business opportunities hidden in their data. They are using various technologies including artificial intelligence and machine learning (AI/ML) to uncover granular insights that can support decision-making. Existing tools and dashboards are effective for observing standard metrics; however, they do not address follow-up questions, such as why things are happening or how those events impact performance. Organizations also struggle to derive complete value from big data. Our Analytics and Data Benchmark Research shows that only 1 in 5 organizations are very confident in their ability to analyze large volumes of data.

Success with streaming data and events requires a more holistic approach to managing and governing data in motion and data at rest. The use of streaming data and event processing has been part of the data landscape for many decades. For much of that time, data streaming was a niche activity, however, with standalone data streaming and event-processing projects run in parallel with existing batch-processing initiatives, utilizing operational and analytic data platforms. I noted that there has been an increased focus on unified approaches that enable the holistic management and governance of data in motion alongside data at rest. One example is the recent emergence of streaming databases designed to combine the incremental processing capabilities of stream-processing engines with the SQL-based analysis and persistence capabilities of traditional databases.

An external audit examines an organization’s performance of accounting tasks. It validates the soundness of accounting systems and policies and compliance with generally accepted accounting principles in preparing financial statements – chiefly the income statement, balance sheet and statements of cash flow and equity. The auditor certifies that there are no material errors or omissions, and that the financial statements are compliant with requirements. Technology can transform this process to provide multiple benefits to audit firms and workers, making accounting cool again by using a continuous audit approach.

I have previously written about the importance of data democratization as a key element of a data-driven agenda. Removing barriers that prevent or delay users from gaining access to data enables it to be treated as a product that is generated and consumed, either internally by employees or externally by partners and customers. This is particularly important for organizations adopting the data mesh approach to data ownership, access and governance. Data mesh is an organizational and cultural approach to data, rather than a technology platform. Nevertheless, multiple vendors are increasingly focused on providing products that facilitate adoption of data mesh and promote data democratization. Amazon Web Services is one such vendor, thanks to the recent launch of Amazon DataZone, one of the figurehead analytics and data announcements made during the company’s recent re:Invent customer event.

Now more than ever, effective data management is crucial to enable decision-makers to better assess information and take calculated actions. It is also important to keep up with the latest trends and technologies to derive higher value from data and analytics and maintain a competitive edge in the market. However, every organization faces challenges with data management and analytics. And as organizations scale, the complexity only increases, creating a need for better data governance, data quality and streamlined and automated processes. DataOps can help solve many of the challenges organizations encounter when trying to unlock the power of data by expanding data use to various parts of an organization. Hitachi Vantara offers DataOps technology that enables organizations to improve data agility and automation. It provides cloud-ready infrastructure, advanced data management software and a broad range of support services.

I am happy to share insights gleaned from our latest Value Index research, an assessment of how well vendors’ offerings meet buyers’ requirements. The Learning Management Value Index 2023 is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to learning management vendors supporting the spectrum of organizational learning. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the product experience ﹘ Adaptability, Capability, Manageability, Reliability and Usability ﹘ and two related to the customer experience ﹘ TCO/ROI and Validation.

Having recently completed the 2023 Ventana Research Value Index for Learning Management Systems (LMS), I’d like to share some of my observations about how the market has advanced since our assessment three years ago. 

A decade ago, organizations were sold on best-of-breed technology stacks. In the world of human capital management, that meant a separate system for each of the myriad processes that make up a worker's life span with a company. The approach seemed to make sense because a single system cannot be the best at everything, can it? Is it realistic for a single provider to create the most advanced applicant tracking, onboarding, learning, payroll, compensation, performance management, succession planning, benefits management and off-boarding system – all in one platform. And so, individual process owners purchased the software that would perform its single task better than every other system, and the Monster Stack was born.

Data analytics provide valuable insights and enable organizations to make better decisions, improve performance and gain a competitive advantage in the marketplace. Analytics can change frequently depending on the data being analyzed and the methods used to gather and process it. Factors such as new data, changes in the underlying systems or updates to algorithms can all contribute to differences in an analysis. AnalyticOps helps ensure data is accurate, up-to-date and consistent across different systems and teams, and that analytical models are robust, reliable and continuously improved.

I recently wrote about the potential use cases for distributed SQL databases as well as techniques being employed by vendors to accelerate adoption. Distributed SQL is a term that is used by several vendors to describe operational data platform products that combine the benefits of the relational database model and native support for distributed cloud architecture, including resilience that spans multiple data centers and/or cloud regions. I noted that compatibility with existing database tools and skills was a key factor for these vendors as they lower barriers to developer adoption. A prime example is Cockroach Labs, which highlighted the importance of compatibility and developer efficiency with the recent launch of CockroachDB 22.2.

I am happy to share insights gleaned from our latest Value Index research, an assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Product Experience Management 2023 is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to vendors supporting the spectrum of product experience management and supporting product information management. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the product experience ﹘ Adaptability, Capability, Manageability, Reliability and Usability ﹘ and two related to the customer experience ﹘ TCO/ROI and Validation. 

For 2023, the Office of Finance practice of Ventana Research introduced Close, Consolidate and Report as one of its six focus areas. The change reflects the recent evolution of technology that supports this part of the finance department calendar. Ventana Research expects that the increasing investment in software to streamline these processes will, by 2026, result in two-thirds of finance and accounting departments improving their use of readily available technology to close quarterly books within six business days, up from one-half that can do it today. Our research coverage in this area includes dedicated consolidation software and close management systems as well as applications for intercompany financial management (IFM), automated reconciliations and disclosure management. Software for the close-to-report cycle also helps achieve objectives in digitally transforming how these processes are executed. Beyond efficiency gains, using automation enables organizations to have management and financial information sooner, provides more time for analysis and crafting performance narratives, achieves superior control of processes, and supports accurate accounting with less effort. It also enables departments to attract and retain the best talent because it minimizes time spent on tedious, repetitive tasks best left to computers. Accountants can then focus on more rewarding work that takes advantage of their skills, experience and expertise.

We added purchasing, sourcing and payments to our core Office of Finance focus areas this year to reflect new and important opportunities to use technology to gain effectiveness through greater efficiency. Doing a better job of record-keeping and organizing paperwork – especially in a minutiae-laden process like source-to-pay – may seem trivial. Yet digitally transforming core business processes became essential as the pandemic required organizations to lock down in early 2020, and the need to operate remotely presented a new set of challenges best addressed by software. Moreover, accounts payable work can be done remotely, and organizations that go this route find that digitizing the process works best.

Having just completed the 2023 Ventana Research Value Index for Product Experience Management, I want to share some of my observations about how the market has advanced over the last two years. The product information management software market is quite mature and products from any of the vendors we assess could be used to effectively deliver information to help your organization improve its operations. But as we established the definition of product experience management (PXM), the requirements for experiences to external parties—from consumers to customers and from partners to suppliers—places new requirements on vendors to meet an expanded set of requirements for organizations and expectations. 

Having just completed the 2023 Ventana Research Value Index for Product Experience Management, I want to share some of my observations about how the market has advanced over the last two years. The product information management software (PIM) market is quite mature and products from any of the vendors we assessed can be used to effectively deliver information to help your organization improve its operations. But as we established the definition of product experience management (PXM), the requirements for experiences to a range of external parties including consumers, customers, partners and suppliers, places new requirements on vendors to meet an expanded set of needs for organizations and expectations.

For years various types of systems have produced log files to help with monitoring, debugging and performance management. Often, this information was used in forensic analyses of why interruptions in service or other problems occurred. In many cases, log files are still used this way. But systems have grown more complicated, and many more devices are instrumented. Systems have been decomposed into much finer-grained, interdependent services. Infrastructure is now distributed between on-premises and multiple cloud providers. In addition, expectations now include 24x7 operation and real-time responsiveness. All of these factors combine to create challenges with volume and velocity of data that is collected and analyzed.

Digitally transforming core business processes became essential as organizations locked down in early 2020 because of the pandemic and the need to operate remotely presented a set of new challenges that were best addressed by software. Especially in the areas of sourcing, purchasing and payments, software has the ability to streamline processes, shorten process cycles, reduce unnecessary costs, provide greater visibility into cash flows, increase control and improve results. Digitizing operations helps attract and retain the best talent because professionals spend less time on mechanical, repetitive tasks. For all these reasons, beginning this year, Ventana Research’s Office of Finance has established a Purchasing, Sourcing and Payments focus area. One reason is that our research suggests that this transformation is more talked about than done. Ventana Research asserts that by 2027, only one-fourth of larger organizations will consistently manage source-to-pay end-to-end, but those that do will outperform competitors. 

Organizations require faster analytics to continuously improve business operations and stay competitive in today’s market. However, many struggle with slow analytics due to a variety of factors such as slow databases, insufficient data storage capacity, poor data quality, lack of proper data cleansing and inadequate IT infrastructure. Challenges such as data silos can also decrease operational efficiency. And as the data grows, performing complex data modelling becomes challenging for users as they spend more time managing data rather than identifying insights.

Contact centers are very often keen on optimizing their costs through automation. Technology vendors supplying this market have recognized this need, and in the past few years, they have been putting workflow and automation tools front and center in their offerings. This has raised some questions among practitioners about the most effective strategies for implementing automated processes.

I have been involved with customer relationship management for over 15 years, and although that is not as long as the category has been around, it does coincide with an era where companies and commentators are calling into question the application’s effectiveness. To counter this perception, both CRM vendors and complementary third-party “add-ons” have developed new functionality that aims to improve the “R” in CRM, especially as it relates to sales engagement. But much of this additional functionality is dependent on the quality of the original base data – whether it’s facts about a customer, prospect of products or, if not more important, when, how and what activities were performed in furtherance of this relationship.

Maersk is closing its TradeLens joint venture with IBM that began in 2018. It was intended to enable port operators, customs authorities, freight forwarders and importers to track containers and their contents. The system was designed to substantially reduce the inefficiencies and delays created by paper-based systems used for customs, trade finance, tariffs and taxes as well as supply chain and logistics management. The initiative was designed to cut supply chain latency by reducing the time for cargo to clear customs from hours to seconds or minutes. Plus, providing deeper visibility and simpler access to accurate shipping data would make supply chain management easier. Our Dynamic Insights research into Sales and Operations Planning found that 38% of organizations identified an inability to access data as an important issue hampering sales and operations planning.

Recruiting is hard. The best candidate often isn’t even actively looking for a new role, and yet recruiters are expected to find the perfect match of skills, experience and fit at exactly the right time and somehow convince that individual to leave a familiar role and work community to join a new organization. Those who are already in the market have gone to considerable lengths to ensure their credentials stand out from the competition, from creating new resumes to forging new professional contacts to honing their interview skills. Organizations would do well to remove as many barriers as possible to ensure the top end of the candidate funnel is open and welcoming to as many qualified applicants as possible, and yet many times the process itself is so daunting that candidates will self-select out in favor of easier paths. Creating a positive and engaging candidate experience is critical to hiring success. The following are five strategies employers can use to ensure they don’t lose the best talent to a competitor’s simpler, more engaging recruiting process.

I have previously written about the functional evolution and emerging use cases for NoSQL databases, a category of non-relational databases that first emerged 15 or so years ago and are now well established as potential alternatives to relational databases. NoSQL is a term that is used to describe a variety of databases that fall into four primary functional categories: key-value stores, wide column stores, document-oriented databases and graph databases. Each is worthy of further exploration, which is why I am examining them over a series of analyst perspectives, starting with graph databases.

We live in a time of uncertainty, not unpredictability. Managing an organization in uncertain times is always hard, but tools are available to improve the odds for success by making it easier and faster to plan for contingencies and scenarios. Software makes it possible to quickly consider the impact of a range of events or assumptions and devise a set of plans to deal with them. Dedicated planning and budgeting software has been around for decades but is about to become all the more useful as vendors increasingly incorporate artificial intelligence using machine learning to assist in scenario planning. Organizations can quickly investigate the impact of different contingencies and the consequences of a range of reactions to them.

The market for data and analytics products is constantly evolving, with the emergence of new approaches to data persistence, data processing and analytics. This enables organizations to constantly adapt data analytics architecture in response to emerging functional capabilities and business requirements. It can, however, also be a challenge. Investments in data platforms cannot be constantly written-off as organizations adopt new products for new approaches. Too little change can lead to stagnation, but too much change can be chaotic, leading to silos of data and data integration complexity. This is one reason why there is growing interest in the concept of data fabric for managing and governing data across distributed environments. In addition to supporting hybrid and multi-cloud strategies, data fabric enables organizations to manage and generate insight from data spread across a combination of long-standing and new data platforms. Promethium focuses on automating data management and data governance across a distributed architecture with a combination of data fabric and self-service augmented analytics capabilities.

The cloud has come to dominate many business software categories, but until recently, enterprise resource planning for manufacturing and product-focused organizations has been a notable laggard. Cloud-based systems can be less costly to operate, perform better, be more secure and shift maintenance and update chores from the IT department to the vendor, freeing IT teams to concentrate on more strategic needs.

I’ve previously written about the analytics continuum, which spans a range of capabilities including reporting, visualization, planning, real-time processes, natural language processing, artificial intelligence and machine learning. I’ve also written about the analysis that goes into making intelligent decisions with decision intelligence. In this perspective, I’d like to focus on one end of the analytics continuum, which I’ll label advanced analytics.

Data observability was a hot topic in 2022 and looks likely to be a continued area of focus for innovation in 2023 and beyond. As I have previously described, data observability software is designed to automate the monitoring of data platforms and data pipelines, as well as the detection and remediation of data quality and data reliability issues. There has been a Cambrian explosion of data observability software vendors in recent years, and while they have fundamental capabilities in common, there is also room for differentiation. One such vendor is Soda Data, which offers an open-source platform for self-service data observability that is focused on facilitating collaboration between business decision-makers and data teams responsible for generating and managing data to improve trust in data.

Compensation management is the practice of strategically aligning worker pay with job roles, responsibilities and organizational objectives. It involves setting pay levels to ensure employees are adequately rewarded for their work while considering factors such as external economic trends, internal equity, budget constraints, tax regulations and compliance requirements.

I have written about the increased demand for data-intensive operational applications infused with the results of analytic processes, such as personalization and artificial intelligence-driven recommendations. I previously described the use of hybrid data processing to enable analytics on application data within operational data platforms. As is often the case in the data platforms sector, however, there is more than one way to peel an orange. Recent years have also seen the emergence of several analytic data platforms that deliver real-time analytic processing suitable for data-intensive operational applications.

insightsoftware provides applications for finance departments and other business users in midsize and larger organizations, offering a broad range of functions including analysis, internal and regulatory reporting, planning, consolidation, tax provision and treasury. The software brings together applications that enable business users to maximize data collected in existing systems and streamline the performance of a range of office of finance functions, all while limiting or eliminating the involvement of IT professionals.

Trintech provides finance departments in midsize and larger organizations with software to accelerate their accounting close processes. This record-to-report (R2R) cycle is complex because it involves the coordination of numerous people, many systems and multiple sources of data. There are iterative portions that involve cycles of reviews and multiple levels of approvals. These include reconciliations and adjusting of entries during the close as well as authoring, editing and updating narratives in the creation of disclosure documents. All of these must adhere to internal controls that require defined processes including reviews and approvals by multiple individuals. One key objective within the R2R process is to minimize the time it takes to complete the accounting close, with the generally accepted norm being one business week. Shortening the close means that all downstream tasks can be finished sooner, providing executives, managers, directors and shareholders with information about the organization’s financial performance and health sooner. Ventana Research asserts that by 2026, two-thirds of finance and accounting departments will improve their use of readily available technology to close their quarterly books within six business days, up from one-half that can do it today.

Organizations across various industries collect multiple types of data from disparate systems to answer key business questions and deliver personalized experiences for customers. The expanding volume of data increases complexity, and data management becomes a challenge if the process is manual and rules-based. There can be numerous siloed, incomplete and outdated data sources that result in inaccurate results. Organizations must also deal with concurrent errors – from customers to products to suppliers – to create a complete view of the data. Many vendors, including Tamr, have turned to artificial intelligence and machine learning to overcome the challenges associated with maintaining data quality amid the growing volume and variety of data. I assert that by 2026, more than three-quarters of organizations’ data management processes will be enhanced with artificial intelligence and machine learning to increase automation, accuracy, agility and speed.

Ventana Research coined the term intercompany financial management (IFM) to define a discipline for structuring and handling transactions within a corporation and between its legal entities designed to maximize staff efficiency and accounting accuracy while optimizing tax exposure, minimizing tax leakage and ensuring consistent tax and regulatory compliance. Technology has advanced to a point where this approach is feasible and cost effective. For that reason, Ventana Research asserts that by 2026, one-half of organizations with 10,000 or more employees will have implemented IFM to achieve tax, risk management and accelerated financial close benefits.

The Internet of Things describes machines and objects that are enhanced with sensors and communication connections, enabling them to report on their operational status, including outages, faults or threshold conditions that require attention. The technology has gradually made its way into business and consumer systems over the past decade. Today, these connected devices are playing an important role in customer experience processes such as field service.

In January of 2020, I was head of product innovation for a newly launched product in the human capital management technology space, targeting high-volume hiring. We had big ambitions for that year, tied to product development and sales, all documented during our annual goal-setting and performance review process. And then the pandemic hit, and everything changed overnight. Everything, that is, except for my annual goals, or those of my team, which had already been set in stone. When the annual review process came around, nothing we’d set forth earlier in the year was even applicable. How is a leader to evaluate and make compensation recommendations for a team member based on completely outdated criteria?

Despite the emphasis on organizations being more data-driven and making an increasing proportion of business decisions based on data and analytics, it remains the case that some of the most fundamental questions about an organization are difficult to answer using data and analytics. Ostensibly simple questions such as, “how many customers does the organization have?” can be fiendishly difficult to answer, especially for organizations with multiple business entities, regions, departments and applications. Increasing volumes and sources of data can hinder, rather than help. Only 1 in 5 participants (20%) in Ventana Research’s Analytics and Data Benchmark research are very confident in their organization’s ability to analyze the overall quantity of data. This is a perennial issue that data and application integration vendors, such as SnapLogic, are aiming to address – increasingly through automation and products for business users as well as data management professionals.

FinancialForce offers cloud-based ERP and professional services automation (PSA) software. The company targets midsize and larger services companies, especially those that provide professional services (such as consultants or field service organizations) as well as those that offer subscription-based or recurring revenue services and products. FinancialForce’s key point of differentiation is that it is built natively on the Salesforce platform, ensuring that CRM data is already located on the same platform as accounting and back-office data so organizations can orchestrate end-to-end, front-office to back-office processes without having to integrate different systems.

Ventana Research recently announced its 2023 Market Agenda for Human Capital Management, continuing the guidance we’ve offered for two decades to help organizations derive maximum potential from workforce- and people-related technology investments and initiatives. In crafting this Market Agenda, we focused on three critical themes top-of-mind for both HCM vendors and buyers: Organizational resiliency, employee engagement and utilizing digital technology to derive deeper insights into the state of the workforce so that leadership can take action to promote retention and growth.

I am happy to share insights from our latest Ventana Research Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements. The 2023 Analytic Data Platforms Value Index is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect real-world criteria incorporated in a request for proposal to data platform vendors supporting the spectrum of analytic use-cases. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the Product Experience: Adaptability, Capability, Manageability, Reliability and Usability, and two related to the Customer Experience: Total Cost of Ownership/Return on Investment and Validation. This research-based index evaluates the full business and information technology value of analytic data platforms offerings. I encourage you to learn more about our Value Index and its effectiveness as a vendor selection and request for information/requestion for proposal tool.

I am happy to share insights gleaned from our latest Value Index research, an assessment of how well vendors’ offerings meet buyers’ requirements. The 2023 Ventana Research Value Index: Customer Experience Management is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories derived from RFP responses submitted by customer experience (CX) vendors. These categories reflect the real-world criteria required by organizations for CX software procurement. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the Product Experience — Adaptability, Capability, Manageability, Reliability and Usability — and two related to the Customer Experience — TCO/ROI and Vendor Validation.

With Ventana Research’s 2023 Market Agenda, we continue the guidance we’ve offered for two decades to help organizations derive maximum value from digital business technology investments. Through our market research and expertise, we identify trends and best practices and share insights on how to achieve technological effectiveness, particularly in key processes and systems to engage the workforce.

Markets have been more volatile than ever. It creates a need for decision makers to utilize technologies such as artificial intelligence and machine learning (AI/ML) to better understand the external factors that impact their business. By identifying these factors, organizations can better plan for changing market environments and seize market opportunities. However, manual modeling is a time-consuming process and results in a limited number of models and tests. Also, updating those models is slow and laborious. With the addition of market volatility, it creates multiple challenges for CFOs, managers and financial planning specialists. With limited exposure to external drivers of demand and delivery, the process becomes very costly. Developing accurate forecasts requires integrating exogenous data with the internal performance data, but it’s challenging to find quality external data and then get that raw data clean enough to input into any model. My colleague, Robert Kugel, recently shared his perspective on using external data for forecasting, budgeting and planning to enhance predictive capabilities.

Ventana Research recently announced its 2023 Market Agenda in the expertise area of Marketing, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value from business technology and improve outcomes.

The subscription and recurring revenue business models became a significant part of the economy this century with the advent of streaming services for entertainment and software as a service. They have grown in popularity because they enhance customer lifetime value by evolving what had previously been a one-time-sale relationship into a delivery of ongoing services which can create a more loyal customer relationship as well as provide a regular, more predictable revenue stream. I recommend that corporations that have adopted or are planning to adopt either of these business models take a continuous accounting approach to managing their record keeping. Ventana Research asserts that by 2026, one-half of subscription organizations will use continuous accounting to remove constraints limiting sales and marketing flexibility, streamline back-office processes, shorten the accounting close and improve customer satisfaction.

Ventana Research recently published the 2023 Analytic Data Platforms Value Index. As organizations strive to be more data-driven, increasing reliance on data as a fundamental factor in business decision-making, the importance of the analytic data platform has never been greater. In this post, I’ll share some of my observations about how the analytic data platforms market is evolving.

Ventana Research recently announced its 2023 research agenda for Operations and Supply Chain, continuing the guidance we’ve offered for nearly two decades to help organizations across industries derive optimal value and improved outcomes from business technology.

Having just completed the 2023 Ventana Research Value Index for Customer Experience Management, I want to share some of my observations about how the market has developed. We found that there are many tools available for various needs related to customer management and communication, ranging from marketing tools to contact center systems to data and analytics applications. It is rare to find all the components fully integrated into a single platform, although that appears to be where the industry is headed. We found that despite the lack of clarity in the marketplace, technology is moving quickly to provide users with more extensive tools that work better together. The vendor landscape is fractured, but most are taking advantage of developments in artificial intelligence (AI), machine learning (ML) and workflow automation to deliver functionality that, in some cases, was simply not possible as recently as five years ago.

I am happy to share insights from our latest Ventana Research Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements. The 2023 Operational Data Platforms Value Index is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect real-world criteria incorporated in a request for proposal to data platform vendors supporting the spectrum of operational use cases. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the Product Experience: Adaptability, Capability, Manageability, Reliability and Usability, and two related to the Customer Experience: Total Cost of Ownership/Return on Investment and Validation.

Ventana Research recently announced its 2023 Market Agenda for the Office of Finance, continuing the guidance we have provided since 2003 on the practical use of technology for the finance and accounting department. Our insights and best practices aim to enable organizations to operate with agility and resiliency, improving performance and delivering greater value as a strategic partner.

Configure, price and quote (CPQ) software has been traditionally viewed as a specialist outpost of software, almost exclusively B2B and predominately used in manufacturing or specialty chemicals or other industries where there were large variants of similar products that required careful configuration, pricing and ultimately generating a quote that could be forwarded to a prospective customer for consideration. But in recent years, this has changed, both in the number of vendors offering CPQ products, either standalone or more commonly as part of a broader offering, and the number of selling organizations recognizing the value of either the “C,” the “P,” or the “Q” or some combination of these.

Ventana Research recently announced its 2023 Market Agenda for Data, continuing the guidance we have offered for two decades to help organizations derive optimal value and improve business outcomes.

Ventana Research recently announced its 2023 Market Agenda for Analytics, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value from technology investments to improve business outcomes.

Ventana Research recently published the 2023 Operational Data Platforms Value Index. The importance of the operational data platform has never been greater as organizations strive to be more data-driven, incorporating intelligence into operational applications via personalization and recommendations for workers, partners and customers. In this post, I’ll share some of my observations on how the operational data platforms market is evolving.

A professional services automation application marries front- and back-office functions, helping services organizations address core business challenges by ensuring that:

Ventana Research recently announced its 2023 research agenda for the Office of Revenue, continuing the guidance we’ve offered for nearly two decades to help organizations realize their optimal value from applying technology to improve business outcomes. Chief Sales and Revenue Officers face an imperative to manage their sales and revenue organizations, but they don’t always have the guidance they need to embrace technology to achieve the best possible outcomes. As we look forward to 2023, we are focusing on the entire selling and buying journey, and in addition focusing on those activities that ensure renewal and expansion as well as newer digital engagement and selling channels. We are looking at applications that simplify processes and tasks across the customer experience, from beginning to end.

Ventana Research recently announced its Market Agenda in the expertise area of Customer Experience. CX has emerged as a way for organizations to demonstrate value and stand out in the marketplace. The technology underlying modern CX is transitioning from tools that are based on communication to those centered on data analysis and process automation. This allows organizations to build great experiences and reap the benefits in customer loyalty and value. It also forces companies to reckon with the complexity and disruption that technologies like artificial intelligence and automation bring to an organization.

I am happy to share insights from our latest Ventana Research Value Index, which assesses how well vendors’ offerings meet buyers’ requirements. The 2023 Data Platforms Value Index is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect real-world criteria incorporated in a request for proposal to data platform vendors that support the spectrum of operational and analytic use cases. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the Product Experience: Adaptability, Capability, Manageability, Reliability and Usability, and two related to the Customer Experience: Total Cost of Ownership/Return on Investment and Validation.

Since 2021, Verint’s message to the customer experience community has focused on the “engagement capacity gap,” a way of describing how the available resources for CX clash with the high level of customer expectations. The argument is that efforts to close that gap require a rethink of how contact centers (and their parent organizations) operate and plan.

I’m proud to share Ventana Research’s 2023 Market Agenda for Digital Technology. Our focus in this agenda is to deliver expertise to help organizations prioritize technology investments that improve customer, partner and workforce experiences while also increasing organizational effectiveness and agility.

Vertical strategies for enterprise resource planning systems are not new. They emerged more than two decades ago as vendors looked for ways to reduce costs and shorten time-to-value in a software category that was notorious for high costs and extended timelines. A vertical-plus strategy – the plus means it’s a platform, not just an application – takes advantage of recently available technology to extend the ease of implementation and maintenance of the system by having deeper integration with complementary applications, available low-code/no-code customization capabilities and a data pantry that enables the amalgamation of data from multiple sources for situational awareness and decision support. Moreover, the ongoing shift from on-premises to cloud-based ERP systems, especially those designed to address specific types of businesses, will accelerate over the next five years as more configurable and customizable systems designed for specific business verticals become available. A cloud-based platform facilitates the creation of a digital ecosystem that can enable a software vendor’s users to enhance customer experiences.

Data observability is a hot topic and trend. I have written about the importance of data observability for ensuring healthy data pipelines, and have covered multiple vendors with data observability capabilities, offered both as standalone and part of a larger data engineering system. Data observability software provides an environment that takes advantage of machine learning and DataOps to automate the monitoring of data quality and reliability. The term has been adopted by multiple vendors across the industry, and while they all have key functionality in common – including collecting and measuring metrics related to data quality and data lineage – there is also room for differentiation. A prime example is Acceldata, which takes a position that data observability requires monitoring not only data and data pipelines but also the underlying data processing compute infrastructure as well as data access and usage.

Having recently completed the 2023 Data Platforms Value Index, I want to share some of my observations about how the market is evolving. Although this is our inaugural assessment of the market for data platforms, the sector is mature and products from many of the vendors we assess can be used to effectively support operational and analytic use cases.

Ventana Research has announced its market agenda for 2023, continuing a 20-year tradition of credibility and trust in our objective efforts to educate and guide the technology market. Our research and insights are backed by our expertise and independence, as we do not share our Market Agenda or our market research – including analyst and market perspectives – with any external party before it is published. We continuously refine our Market Agenda throughout the year to ensure we offer the expertise and insights organizations rely on to better assess and navigate the direction of the technology industry.

As the world of work continues to evolve, so too must the way organizations evaluate talent. Traditional evaluation criteria like education and prior job titles still have a place in hiring, promotion and succession-planning processes. However, organizations that consider transferrable skills first have the opportunity to screen in talent that they may not have otherwise considered, creating a substantial advantage in the proverbial war for talent.

Consumer and mobile applications have influenced our expectations. Nearly all of us carry a smartphone, and we interact with a variety of applications on our devices. Those applications have forever influenced what we expect from computing systems. When I search the web for a gas station, I’m not searching for all gas stations. I’m searching for those stations that are near me. Not just near my regular location, but near my current location. We expect personalized interactions, not generic, one-size-fits-all interactions. These expectations have spilled over from consumer applications to enterprise applications. Unfortunately, enterprise IT applications have not fully met these expectations yet.

Those of us who have worked in or alongside sales teams have observed that many sales fundamentals have changed over the years. Yet, in many ways, they have not. One essential that has not changed is sales enablement. How do you onboard salespeople and quickly bring them up to speed? Or, how do you introduce new products and services so the sales team is conversant and knowledgeable about them? In years past, this would have happened at the head office or a regional office, with videos and internally produced documentation and product fact sheets. But thanks to the internet, this has all changed. Digitization has improved – and will continue to enhance – sales enablement.

In today’s organization, the myriad of analytics and permutations of dashboards challenge workers’ ability to take contextual actions efficiently. Unfortunately, conventional wisdom for investing in analytics does not recognize the benefits of empowering the workforce to understand the situation, examine options and work together to make the best possible decision.

Since its inception, HCM software has upended how people interact with their workplace. Paper resumes have given way to online applications. Physical time clocks have largely disappeared in favor of apps or clocks housed within a point-of-sale system. Even benefits enrollment has entered the digital age, adding tools like decision-support modeling to help enrollees determine which of the myriad offerings will better fit their specific circumstances. This digital transformation does more than just improve productivity and drive compliance. It helps to create an experience for workers that can build trust and engagement. In our research, 64% of organizations indicated that delivering a superior employee experience is a top priority. We assert that by 2025, two-thirds of organizations will require artificial intelligence (AI) in all HCM and adjacent systems to curate personalized experiences for all workers to drive engagement, productivity and retention. UKG, a company founded in 2021 by the merger of two HCM technology giants, Ultimate Software and Kronos, excels in this arena.

The management of work is a focal point for every organization that has people and resources directed to accomplish the smallest to largest of tasks. But many organizations are not easily able to manage complex activities because the details of how people are assigned and complete work are not as simple as they should be. Traditional project management methods and technologies have failed to work in an enterprise manner, so new approaches have emerged to meet today’s challenges. The essence of work management is to ensure the automation and intelligence afforded by the technology industry is infused in how organizations operate, as outlined in my perspective.

Organizations conduct data analysis in many ways. The process can include multiple spreadsheets, applications, desktop tools, disparate data systems, data warehouses and analytics solutions. This creates difficulties for management to provide and maintain updated information across multiple departments. Our Analytics and Data Benchmark Research shows that organizations face a variety of challenges with analytics and business intelligence. One-third of participants find it difficult to integrate analytics and BI with other business processes. Participants also find that not all software is flexible enough for the constantly changing business environment, and that it is hard to access all data sources.

Workiva offers an environmental, social and governance application that enables organizations to manage the highly distributed tasks necessary for reporting to regulators and stakeholders on ESG matters. ESG issues have grown increasingly pressing over the past few years as investors and government entities urge organizations to measure and disclose relevant metrics. I’ve already covered the broader topic as it relates to external reporting and how financial planning and analysis groups are likely to own this mandate going forward. I’ve also addressed the data strategy that finance organizations should adopt to meet regulatory compliance requirements. Notably, I assert that by 2025, more than one-half of corporations required to comply with ESG reporting will centralize responsibility for preparing reports and filings with financial planning and analysis to achieve accuracy, control and efficiency objectives.

For far too long, business intelligence technologies have left the rest of the exercise to the reader. Many of these tools do an excellent job providing information in an interactive way that lets organizations dive into the data and learn a lot about what has happened across all aspects of the business. More recently, many of these tools have added augmented intelligence capabilities that help explain why things happened. But rarely did any of these tools provide information about what to do or how to evaluate the alternative ways in which you might respond.

The shift from on-premises server infrastructure to cloud-based and software-as-a-service (SaaS) models has had a profound impact on the data and analytics architecture of many organizations in recent years. More than one-half of participants (59%) in Ventana Research’s Analytics and Data Benchmark research are deploying data and analytics workloads in the cloud, and a further 30% plan to do so. Customer demand for cloud-based consumption models has also had a significant impact on the products and services that are available from data and analytics vendors. Data platform providers, both operational and analytic, have had to adapt to changing customer demand. The initial response — making existing products available for deployment on cloud infrastructure — only scratched the surface in terms of responding to emerging expectations. We now see the next generation of products, designed specifically to deliver innovation by taking advantage of cloud-native architecture, being brought to market both by emerging startups, and established vendors, including InterSystems.

Analytics processes are all about how organizations use data to create metrics that help manage and improve operations. Yet, the discipline applied to analytics processes seems to be lacking compared to data processes. I’ve pointed out that the weak link in data governance is often analytics. Organizations can also do a better job tying AnalyticOps to DataOps and do more to define and manage metrics. Our research has shown that creating and managing metrics in a semantic model improves analytics processes.

There is always space for innovation in the data platforms sector, and new vendors continue to emerge at regular intervals with new approaches designed to serve specialist data storage and processing requirements. Factors including performance, reliability, security and scalability provide a focal point for new vendors to differentiate from established vendors, especially for the most demanding operational or analytic data platform requirements. It is never easy, however, for developers of new data platform products to gain significant market traction, given the dominance of the established relational database vendors and cloud providers. Targeting requirements that are not well-served by general purpose data platforms can help new vendors get a toe in the door of customer accounts. The challenge to gaining further market traction is for new vendors to avoid having products become pigeon-holed as only being suitable for a niche set of requirements. This is precisely the problem facing the various distributed SQL database providers.

Ventana Research uses the term “data pantry” to describe a method of data storage (and the technology and process blueprint for its construction) created for a specific set of users and use cases in business-focused software. It’s a pantry because all the data one needs is readily available and easily accessible, with labels that are immediately recognized and understood by the users of the application. In tech speak, this means the semantic layer is optimized for the intended audience. It is stocked with data gathered from multiple sources and immediately available for analysis, forecasting, planning and reporting. This does away with the need for analysts to repeatedly perform data extraction, enrichment or transformation motions from the required source systems, all but eliminating the substantial amount of time analysts and business users routinely spend on data preparation.

In previous perspectives in this series, I’ve discussed some of the realities of cloud computing including costs, hybrid and multi-cloud configurations and business continuity. This perspective examines the realities of security and regulatory concerns associated with cloud computing. These issues are often cited by our research participants as reasons they are not embracing the cloud. To be fair, the majority of our research participants are embracing the cloud. However, among those that have not yet made the transition to the cloud, security and regulatory concerns are among the most common issues cited across the various studies we have conducted.

In the face of a very uncertain future, companies have been discovering the value of rapid planning and budgeting cycles. As events unfold, they’re changing expectations for the future significantly on a daily or weekly basis. However, even when the world returns to a steadier state, companies will benefit from making their planning and budgeting processes faster, easier, more relevant, more strategic, more agile and more accurate.

Recently, I suggested you need to “mind the gap” between data and analytics. This perspective addresses another gap — the gap in skills between business intelligence (BI) and artificial intelligence/machine learning (AI/ML).

After decades of overpromising and underdelivering, technology has now evolved to the point where it is fundamentally changing how accountants work – for the better. The pandemic and resulting support of remote work set the stage for a transformation of how accounting efforts are structured and performed, all for the better. Remote audits that became routine during lockdowns are evolving into virtual ones, where auditors take full advantage of advanced software to achieve dependably higher audit quality with less effort, while improving working conditions for auditors and staff accountants. Although discussions I’ve had with practitioners over the past two years indicate that organizations are using this approach to some extent, widespread use has become practical only recently.

The market and buyer landscape for contact center operating services has changed significantly since the onset of the pandemic, now almost three years ago. Three years would have been enough time for some significant shifts, even without the pressure the pandemic put on service operations. Nevertheless, with on-premises systems now taking a backseat industrywide, it’s fair to say that CCaaS, which typically refers to cloud-based systems, now represents the lions’ share of spending and therefore stands as a proxy for the industry as a whole. Ventana Research predicts that by 2026, 7 in ten organizations will have moved all or part of their contact center technology into the cloud to attain greater flexibility and scalability.

The technology industry has established itself as a pivotal force in its ability to help organizations become more intelligent and automated. But doing so has required a journey of epic proportions for most organizations that have had to endure a transition of competencies and skills that was, in many places, transitioned to consulting firms who were hired appropriately to manage changes. Unfortunately, this step led, in many cases, to an extended focus on digital transformation rather than the necessary modernization of business processes and technology. Through 2024, after concerted investment into digital transformation, one-half of organizations will require a new digital business and technology agenda for organizational resilience.

Earlier this year, I wrote about the increasing importance of data observability, an emerging product category that takes advantage of machine learning (ML) and Data Operations (DataOps) to automate the monitoring of data used for analytics projects to ensure its quality and lineage. Monitoring the quality and lineage of data is nothing new. Manual tools exist to ensure that it is complete, valid and consistent, as well as relevant and free from duplication. Data observability vendors, including Monte Carlo Data, have emerged in recent years with the goal of increasing the productivity of data teams and improving organizations’ trust in data using automation and artificial intelligence and machine learning (AI/ML).

Emburse offers a single platform that enables organizations — small, midsize and larger —to manage their travel and related expenses, pay invoices and handle their corporate spend. Today, technology has the ability to significantly increase the efficiency with which organizations handle expenditures while simultaneously containing costs, increasing controls and improving visibility into where the money is going. This is part of a broader trend toward digitizing outlays: I assert that by 2025, more than two-thirds of organizations will be using spend management software and corporate cards to achieve greater control and increased efficiency.

One of the most significant considerations when choosing an analytic data platform is performance. As organizations compete to benefit most from being data-driven, the lower the time to insight the better. As data practitioners have learnt over time, however, lowering time to insight is about more than just high-performance queries. There are opportunities to improve time to insight throughout the analytics life cycle, which starts with data ingestion and integration, includes data preparation and data management, as well as data storage and processing, and ends with data visualization and analysis. Vendors focused on delivering the highest levels of analytic performance, such as SQream, understand that lowering time to insight relies on accelerating every aspect of that life cycle.

Embedded business intelligence (BI) continues to transform the business landscape, enabling organizations to quickly interpret data and convert it into actionable insights. It allows organizations to extract information in real time and answer wide-ranging business questions. Embedding analytics helps tackle the issue of extracting information from data which is a time-consuming process. Our research shows organizations spend more time cleaning and optimizing data for analysis rather than creating insights. On top of that, they are adding more data sources and information systems which in turn introduces more complexity. Our Analytics and Data Benchmark Research shows that organizations face various challenges with analytics and BI. More than one-third of participants (35%) responded that they find it hard to integrate analytics and BI with business processes and connect to multiple data sources. By embedding analytics and BI into business processes and workflows, organizations can enable users to make critical decisions fast, enhancing overall business agility.

Managing corporate income taxes is a challenge for chief financial officers and their tax department professionals. Tax codes are often complex, so tax accounting as well as the data required for tax provisions and tax compliance are different enough from statutory accounting to create significant workloads for the tax department. The provision for income tax expense and, for public companies, the assembly of information related to tax-related disclosures, can be a factor holding up the completion of the accounting close.

Organizations are increasingly utilizing cloud object storage as the foundation for analytic initiatives. There are multiple advantages to this approach, not least of which is enabling organizations to keep higher volumes of data relatively inexpensively, increasing the amount of data queried in analytics initiatives. I assert that by 2024, 6 in ten organizations will use cloud-based technology as the primary analytics data platform, making it easier to adopt and scale operations as necessary.

Vena Solutions offers organizations a platform for financial planning, analysis and reporting as well as software to manage accounting consolidation and close processes. From the start, Vena has designed its applications to meet the needs of midsize organizations, which typically have the same requirements as large enterprises but with significantly fewer resources to acquire, manage and maintain technology. Ventana Research named Vena a Value Index Leader in Adaptability and a Vendor of Merit in its 2022 Value Index on Business Planning.

In today’s data-driven world, organizations need real-time access to up-to-date, high-quality data and analysis to keep pace with changing market dynamics and make better strategic decisions. By mining meaningful insights from enterprise data quickly, they gain a competitive advantage in the market. Yet, organizations face a multitude of challenges when transitioning into an analytics-driven enterprise. Our Analytics and Data Benchmark Research shows that more than one-quarter of organizations find it challenging to access data sources and integrate data and analytics in business processes. Vendors such as IBM offer a broad set of analytics tools with self-service capabilities that allows organizations to reduce IT dependencies and enables decision-makers to recognize performance gaps, market trends and new revenue opportunities. Its technology can simplify data access for self-service applications, enabling users to make business decisions informed by insights and take the guesswork out of decision-making.

The theme of this year’s Oracle NetSuite SuiteWorld was “Full Suite Ahead,” with content aimed at demonstrating to customers (and prospective buyers) the value of using more of what NetSuite has to offer. The business logic behind this concept goes beyond the obvious objective of upselling existing customers to increase the average annual recurring revenue. As is often the case with subscription businesses, customers fail to take advantage of what’s already included in their service. Ensuring that customers are achieving full value is essential to retaining them, and almost always a precondition to selling them more. For a cloud software vendor, this translates to having an effective customer success organization backed by a customer-centric product strategy and a product management organization that delivers on the strategy. All of this was on display at the event.

The pandemic years saw an exponential rise in organizational investment in digital learning, and for good reason. With in-person learning no longer an option, organizations were forced to quickly adapt to the changing world of work in order to ensure workers were prepared with the knowledge and training necessary to operate in ways they had never before seen. Beyond operational imperatives, organizations have turned to digital learning systems to find new ways to track and bolster productivity, sentiment and engagement of the workforce. This serves to protect the investments made in hiring, onboarding and upskilling or reskilling talent by mitigating risk and regrettable attrition as well as helping employees along a career path that is achievable and desirable for the employee and strategically advantageous to the employer.

Almost all organizations are investing in data science, or planning to, as they seek to encourage experimentation and exploration to identify new business challenges and opportunities as part of the drive toward creating a more data-driven culture. My colleague, David Menninger, has written about how organizations using artificial intelligence and machine learning (AI/ML) report gaining competitive advantage, improving customer experiences, responding faster to opportunities and threats, and improving the bottom line with increased sales and lower costs. One-quarter of participants (25%) in Ventana Research’s Analytics and Data Benchmark Research are already using AI/ML, while more than one-third (34%) plan to do so in the next year, and more than one-quarter (28%) plan to do so eventually. As organizations adopt data science and expand their analytics initiatives, they face no shortage of options for AI/ML capabilities. Understanding which is the most appropriate approach to take could be the difference between success and failure. The cloud providers all offer services, including general-purpose ML environments, as well as dedicated services for specific use cases, such as image detection or language translation. Software vendors also provide a range of products, both on-premises and in the cloud, including general-purpose ML platforms and specialist applications. Meanwhile, analytic data platform providers are increasingly adding ML capabilities to their offerings to provide additional value to customers and differentiate themselves from their competitors. There is no simple answer as to which is the best approach, but it is worth weighing the relative benefits and challenges. Looking at the options from the perspective of our analytic data platform expertise, the key choice is between AI/ML capabilities provided on a standalone basis or integrated into a larger data platform.

In my previous perspectives on cloud computing, I addressed some of the realities of cloud costs as well as hybrid and multi-cloud architectures. In the midst of the pandemic, my colleague, Mark Smith, authored a series of perspectives on considerations for business continuity in general, beginning with this look at some of the investments organizations must make to mitigate the risk of business disruptions. In this perspective, I’d like to address some of the realities of business continuity and cloud computing and how they impact the digital technologies of an organization. The cloud can be both advantageous and disadvantageous when it comes to providing business continuity.

The starting point of an era is never precise and rarely conforms to neat calendar delineations. For example, the start of the 20th century is associated with the outbreak of war in 1914. So I expect that decades from now, the consensus will hold that what became known as the 21st century began in the year 2020, with the pandemic serving as a catalyst that accelerated already existing trends and forced changes to prevailing norms and practices. This and other disruptive events that have followed are reverberating through economic and social networks and will ultimately result in some new equilibrium, but the ructions on the way there will be sharp and ever-present. Large-scale disruptions in most aspects of doing business have forced change on organizations. In this climate, the financial planning and analysis group can play a far more important role by using technology to enhance organizational agility and improve performance.

IBM Planning Analytics with Watson is a comprehensive, cloud-based business planning application that supports what Ventana Research calls integrated business planning. We coined this term in 2007 to describe a high-participation approach to business planning that integrates strategy, operations and finance. Our Next Generation Business Planning Benchmark Research demonstrated the value of IBP: Organizations that link planning processes get better results. Sixty-six percent of organizations that have an integrated method say it works well or very well, compared to only 25% that have little or no connection between plans.

The idea of partnerships in business is most definitely not new. Wholesaling through distributors and retailers is centuries old. For some industries, their entire model is selling and servicing through partners. Think auto parts, and the auto part stores visible in most neighborhoods. But what is new is that partnerships are moving beyond this reseller model towards product partnerships, where a seller’s products and services are supplemented by other vendors’ offerings from adjacent and complementary markets.

It has been three years since Oracle hosted a CloudWorld event live in Las Vegas, and it is clear the time has not been wasted. With 12 new releases since the last event, the Oracle human capital management product team has made significant advances in both product and service. Three areas of note: Oracle HCM’s continued focus on personalization of experience, meeting the HCM needs of the healthcare industry and advancing the Oracle Cloud Recruiting offering.

If you’ve ever been to London, you are probably familiar with the announcements on the London Underground to “mind the gap” between the trains and the platform. I suggest we also need to mind the gap between data and analytics. These worlds are often disconnected in organizations and, as a result, it limits their effectiveness and agility.

I have previously written about growing interest in the data lakehouse as one of the design patterns for delivering hydroanalytics analysis of data in a data lake. Many organizations have invested in data lakes as a relatively inexpensive way of storing large volumes of data from multiple enterprise applications and workloads, especially semi- and unstructured data that is unsuitable for storing and processing in a data warehouse. However, early data lake projects lacked structured data management and processing functionality to support multiple business intelligence efforts as well as data science and even operational applications.

The door opened to a new world in 2020, one that renders old assumptions suspect and future outcomes more varied and uncertain. It’s likely that the transition to what’s next will be bumpy, which makes planning more effectively that much more strategic.

Prophix offers cloud financial software for planning, budgeting, reporting and statutory financial consolidation designed to meet the requirements of midsize organizations and divisions of larger corporations. The company was one of the first to offer a planning platform capable of bringing together a company’s diverse planning processes and financial planning and budgeting. Its consolidation and close automation enable a shorter close and improved accounting staff productivity for midsize corporations that have even moderately complex legal entity structures that operate in multiple currencies. Increasingly, organizations are finding that having the right finance and accounting department software tools helps attract and retain the best talent.

For quite a few years now, two trends have put the contact center on a collision course. First, the technology used to handle customer inquiries has been evolving quickly, moving organizations farther and farther away from the traditional mode of primarily answering voice calls. At the same time, consumers have become much more demanding. There’s no doubt that customers are more likely to use quality of service as a gauge for whether they should continue doing business with an organization. They’re more willing to bolt for a competitor if they have a bad experience. In short, they want more of everything, and contact centers have been trying to accommodate these expectations.

I have written recently about the similarities and differences between data mesh and data fabric. The two are potentially complementary. Data mesh is an organizational and cultural approach to data ownership, access and governance. Data fabric is a technical approach to automating data management and data governance in a distributed architecture. There are various definitions of data fabric, but key elements include a data catalog for metadata-driven data governance and self-service, agile data integration.

The worldwide market for software to manage indirect income taxes, which includes sales and use, goods and services (GST) and value-added taxes (VAT), has been growing because of recent compliance mandates, the growth of e-commerce as well as a desire to accelerate business processes by reducing friction in areas such as tax compliance, cutting administration costs and lowering risk. Vertex provides businesses with cloud-based software that manages indirect tax processes for midsize and larger companies, especially for those with complex tax profiles. Vertex enables local and worldwide compliance backed by its ongoing tax research that continually compiles tax rules for over 19,000 jurisdictions. Because links with core financials is an essential capability for organizations of any size, Vertex maintains pre-built integrations with the leading ERP and financial management systems. Cloud-based systems are now the norm to support teams that are geographically dispersed and enable hybrid work environments. Ventana Research asserts that by 2026, a majority of midsize and larger companies will have digitized their indirect tax compliance to ensure accuracy as jurisdictions step up audits to increase revenues.

ERP systems have been a fixture of organizational process management and record keeping for so long (more than three decades) that it is likely that few who use the software are aware that ERP is an acronym for Enterprise Resources Planning. Its smooth and uninterrupted functioning is essential to an organization’s accounting and finance processes. In manufacturing and distribution, ERP manages inventory and logistics. Some organizations use it to handle human resources functions like tracking workers, payroll and related costs. Its initial introduction represented a major advance, but its subsequent evolution has been slow and mainly a series of incremental refinements.

Pressures to engage consumers through every interaction and provide a delightful customer experience are influencing advancements in business and technology. Organizations are challenged to manage friction points experienced by billions of consumers amid expanding digital channels. These issues must be addressed to engage and respond to customers every second of the day.

Traditional key performance indicators used for performance measurement in contact centers are no longer sufficient. These outdated standards don’t reliably inform mid- and upper-level leadership about the true impact of agent work and behavior. Organizations should begin to expand the notion of what’s important in order to make the contact center a stronger organizational institution, more closely tied to others who impact the customer experience. Outside the contact center, people are keen to understand the relationship between what’s being spent and what’s coming in: revenue and growth.

Some weeks back I published my thoughts about the traditional applicant tracking system and how that technology is no longer sufficient to support organizations’ complex recruiting needs. This is particularly true when trying to take a one-size-fits-all approach to hiring processes. Having identical hiring processes and requirements for professional hires as for low-complexity, low barrier-to-entry roles will inevitably result in lost candidates, frustrated recruiters and hiring managers, and low application-to-hire conversion rates. It doesn’t have to be that way. The right technology can support a hiring process that is purpose built for the segment it is intended to attract and hire.

In their pursuit to be data-driven, organizations are collecting and managing more data than ever before as they attempt to gain competitive advantage and respond faster to worker and customer demands for more innovative, data-rich applications and personalized experiences. As data is increasingly spread across multiple data centers, clouds and regions, organizations need to manage data on multiple systems in different locations and bring it together for analysis. As the data volumes increase and more data sources and data types are introduced in the organization, it creates challenges to storing, managing, connecting and analyzing the huge set of information that is spread across multiple locations. Having a strong foundation and scalable data management architecture in place can help alleviate many of the challenges organizations face when they are scaling and adding more infrastructure. We have written about the potential for hybrid and multi-cloud platforms to safeguard data across heterogenous environments, which plays to the strengths of companies, such as Actian, that provide a single environment with the ability to integrate, manage and process data across multiple locations.

Sensitivity to environmental, social and governance issues – or ESG – has grown over the years and with it, increasing attention by some investors and government entities urging organizations to measure and disclose ESG metrics. 

The topic of revenue operations has been extensively covered recently, not least by vendors extolling the virtues of their particular offering. But as with much of the software industry, vendors often see the market through the lens of their current product capabilities rather than what is necessarily needed. With the rise of the mixed-revenue model that includes subscription and usage pricing as well as one-time sales, combined with the growth in self-service commerce, the result is more teams within an organization being directly involved with supporting revenue generation. In response, many organizations have appointed a Chief Revenue Officer (CRO) who is responsible and accountable for all sources of revenue for an organization. And with the rise of the role of the CRO, combined with an increasing adoption of mixed-revenue models, we see this as an increasingly necessary shift. We believe that leadership will need to drive this change in approach, recognizing that this will require a shift in responsibilities and, as importantly, accountability.

It’s likely that finance analytics trace back to when people first began to record transactions on clay tablets. Financial analytics were given a boost with the codification of double-entry bookkeeping, an elegant system for recording transactions that facilitate the assessment of the performance and health of an organization. Further advances were achieved with the first mechanical – and then digital system – for automating computations, while personal computing devices made the numbers accessible to all.

I have written a few times in recent months about vendors offering functionality that addresses data orchestration. This is a concept that has been growing in popularity in the past five years amid the rise of Data Operations (DataOps), which describes more agile approaches to data integration and data management. In a nutshell, data orchestration is the process of combining data from multiple operational data sources and preparing and transforming it for analysis. To those unfamiliar with the term, this may sound very much like the tasks that data management practitioners having been undertaking for decades. As such, it is fair to ask what separates data orchestration from traditional approaches to data management. Is it really something new that can deliver innovation and business value, or just the rebranding of existing practices designed to drive demand for products and services?

Artificial intelligence and machine learning are valuable to data and analytics activities. Our research shows that organizations using AI/ML report gaining competitive advantage, improving customer experiences, responding faster to opportunities and threats and improving the bottom line with increased sales and lower costs. No wonder nearly 9 in 10 (87%) research participants report using AI/ML or planning to do so.

Planful recently acquired Plannuh, a marketing-performance management application, to integrate into the Planful platform so that organizations can connect their marketing planning and analysis group with the finance department. There’s the old story of a CEO who said, “I know half my marketing spend is wasted, I just don’t know which half.” Plannuh is designed to answer that question.

Ventana Research’s Data Lakes Dynamics Insights research illustrates that while data lakes are fulfilling their promise of enabling organizations to economically store and process large volumes of raw data, data lake environments continue to evolve. Data lakes were initially based primarily on Apache Hadoop deployed on-premises but are now increasingly based on cloud object storage. Adopters are also shifting from data lakes based on homegrown scripts and code to open standards and open formats, and they are beginning to embrace the structured data-processing functionality that supports data lakehouse capabilities. These trends are driving the evolution of vendor product offerings and strategies, as typified by Cloudera’s recent launch of Cloudera Data Platform (CDP) One, described as a data lakehouse software-as-a-service (SaaS) offering.

Much has been written in recent years on the emergence of subscription management as a new revenue model that both vendors and buyers are embracing as the future. The benefits speak to the value of a predictable revenue stream for the vendor, but more importantly, the advantages to the customer who needs a lower initial outlay, predetermined expense over the lifetime of usage and the ability to cancel or suspend on demand.

As I recently pointed out, process mining has emerged as a pivotal technology for data-driven organizations to discover, monitor and improve processes through use of real-time event data, transactional data and log files. With recent advancements, process mining has become more efficient at discovering insights in complex processes using algorithms and visualizations. Organizations use it to better understand the current state of systems and business processes. It is also used to enable business process intelligence and improvement in any function or industry using events and activity models for data-driven decision-making. We assert that through 2024, 1 in 4 organizations will look to streamline their operations by exploring process mining to optimize workflow and business processes.

I have written before about the continued use of specialist operational and analytic data platforms. Most database products can be used for operational or analytic workloads, and the number of use cases for hybrid data processing is growing. However, a general-purpose database is unlikely to meet the most demanding operational or analytic data platform requirements. Factors including performance, reliability, security and scalability necessitate the use of specialist data platforms. I assert that through 2026, and despite increased demand for hybrid operational and analytic processing, more than three-quarters of data platform use cases will have functional requirements that encourage the use of specialized analytic or operational data platforms. It is for that reason that specialist database providers, including Ocient, continue to emerge with new and innovative approaches targeted at specific data-processing requirements.

A year ago, I wrote about how technology could be useful in an inflationary period, correctly anticipating the world we live in now. Responding effectively to changes in costs is always challenging, but even more so because of the choppy and chaotic nature of the current environment. Many organizations have a limited or no ability to raise prices, and are forced to find ways to minimize the impact of rising costs. And while it’s true that some organizations have a degree of pricing power, behind this generalization there is a more complex reality because this ability to raise prices often varies depending on specific products, customers and channels. Companies can best address the challenges of inflation by adopting a technique that Ventana Research calls “profitability management.”

Through 2025, establishing customer experience application suites on a common platform will be the focal point of the drive to optimize customer and organizational engagement. Organizations that are passionate about improving the customer experience are choosing to empower processes and people with intelligence through smarter applications that embrace analytics, artificial intelligence and automation to personalize and optimize the customer journey, whatever the channel of customer choice.

Earlier this year I described the growing use-cases for hybrid data processing. Although it is anticipated that the majority of database workloads will continue to be served by specialist data platforms targeting operational and analytic workloads respectively, there is increased demand for intelligent operational applications infused with the results of analytic processes, such as personalization and artificial intelligence-driven recommendations. There are multiple data platform approaches to delivering real-time data processing and analytics, including the use of streaming data and event processing and specialist, real-time analytic data platforms. We also see operational data platform providers, such as Aerospike, adding analytic processing capabilities to support these application requirements via hybrid operational and analytic processing.

A predictive finance department is one that can command technology to be more forward-looking and action-oriented while still fulfilling its core role of handling the financial elements of its organization including accounting, treasury and corporate finance. Beyond just automating rote tasks, technology also facilitates a shift toward becoming a predictive finance organization. Greater amounts of information, now available in near real time, and the increasing use of artificial intelligence (AI), enable more immediate analyses and assessments of possible courses of action, providing executives and managers the ability to better anticipate change and the agility to adapt quickly to unexpected circumstances.

Process mining is defined as the analysis of application telemetry including log files, transaction data and other instrumentation to understand and improve operational processes. Log data provides an abundance of information about what operations are occurring, the sequences involved in the processes, how long the processes are taking and whether or not the processes are completed successfully. As computing power has increased and storage costs have decreased, the economics of collecting and analyzing large amounts of log data have become much more attractive.

The learning management system technology market has evolved dramatically over the past two decades. Learning management systems, now commonly referred to as learning experience platforms, are an integral resource for any organization concerned about productivity, organizational agility and operational excellence. These technologies enable organizations to demonstrate an investment in people, as the LMS not only facilitates regulatory and legal compliance and other forms of cost and risk avoidance but also improves internal mobility, career growth and the employee experience, leading to improved employee productivity, engagement and retention.

I have recently written about the organizational and cultural aspects of being data-driven, and the potential advantages data-driven organizations stand to gain by responding faster to worker and customer demands for more innovative, data-rich applications and personalized experiences. I have also explained that data-driven processes require more agile, continuous data processing, with an increased focus on extract, load and transform processes — as well as change data capture and automation and orchestration — as part of a DataOps approach to data management. Safeguarding the health of data pipelines is fundamental to ensuring data is integrated and processed in the sequence required to generate business intelligence. The significance of these data pipelines to delivering data-driven business strategies has led to the emergence of vendors, such as Astronomer, focused on enabling organizations to orchestrate data engineering pipelines and workflows.

Workday held its first in-person Rising user group meeting since 2019 in Orlando. Three topics are worth commenting on: Workday’s Extend offering, its industry accelerators and its progress with the Workday Adaptive Planning offering.

In my first perspective on cloud computing realities, I covered some of the cost considerations associated with cloud computing and how the cloud costing model may be different enough from on-premises models that some organizations are taken by surprise. In this perspective. I’d like to focus on realities of hybrid and multi-cloud deployments.

Organizations are collecting data from multiple data sources and a variety of systems to enrich their analytics and business intelligence (BI). But collecting data is only half of the equation. As the data grows, it becomes challenging to find the right data at the right time. Many organizations can’t take full advantage of their data lakes because they don’t know what data actually exists. Also, there are more regulations and compliance requirements than ever before. It is critical for organizations to understand the kind of data they have, who is handling it, what it is being used for and how it needs to be protected. They also have to avoid putting too many layers and wrappers around the data as it can make the data difficult to access. These challenges create a need for more automated ways to discover, track, research and govern the data.

Kinaxis recently announced it has acquired a Netherlands-based company, MPO, a cloud-based software offering that orchestrates multiparty supply chain execution. The combination is designed to enable Kinaxis to extend its concurrent planning platform to handle core elements of supply chain execution. Kinaxis acquired all the shares of MPO for approximately US$45 million, with some of the final consideration dependent on performance. MPO will continue to operate as a standalone business, but will be increasingly integrated into Kinaxis’ operations worldwide.

The data catalog has become an integral component of organizational data strategies over the past decade, serving as a conduit for good data governance and facilitating self-service analytics initiatives. The data catalog has become so important, in fact, that it is easy to forget that just 10 years ago it did not exist in terms of a standalone product category. Metadata-based data management functionality has had a role to play within products for data governance and business intelligence for much longer than that, of course, but the emergence of the data catalog as a product category provided a platform for metadata-based data inventory and discovery that could span an entire organization, serving multiple departments, use cases and initiatives.

I have written about vendor efforts to use artificial intelligence (AI) and advanced analytics in their applications targeted at sales and revenue teams to improve focus and prioritize activities, both for pipeline management as well as individual opportunities. Since then, vendors have continued to innovate, and there have been more releases showcasing efforts to aid sales and revenue. And with this continuing innovation, we believe that by 2026, two-thirds of revenue leaders will begin considering a new generation of revenue analytics and data-driven applications designed to improve performance and productivity.

Today’s contact centers need to revisit core assumptions around measuring agent performance. Changes in business conditions influencing agent engagement raise new questions about whether traditional performance models are sufficient to address the more complex customer needs that have taken center stage in recent years.

Business intelligence has evolved. It now includes a spectrum of analytics, one of the most promising of which has been described as augmented intelligence. Some organizations have used the term to describe the practical reality that artificial intelligence with machine learning is not replacing human intelligence, but augmenting it. The term also represents the application of AI/ML to make business intelligence and analytics tools more powerful and easier to use. It’s this latter usage that I prefer and I’d like to explore in this perspective.

Organizations do not live in a vacuum and things happening outside their walls have a direct impact on how they perform. So, it is essential for them to incorporate external data in their forecasting, planning and budgeting, especially for predictive analytics and machine learning (ML) to support artificial intelligence (AI). I use the term external data to include any information about the world outside an organization (including economic and market statistics), competitors (such as pricing and locations), and customers. Until recently, it was adequate for organizations to regard external data is a “nice to have” item, but that is no longer the case. External data is necessary for many functions, including useful and accurate competitive intelligence used by sales and marketing groups. It is also essential for the effective applications of AI using ML for business-focused planning and budgeting and predictive analytics.

Payroll Management is one of the six major focus areas in the Human Capital Management research and advisory practice at Ventana Research. “Continuous payroll” is a hot topic in this area, with much discussion about the always-on nature of this enhanced payroll function and its related demands for supporting technologies. Advancements in payroll technologies and practices have paved the way for off-cycle payroll transactions and pay modes – like earned wage access, for example – to become the new normal. Injecting continuous payroll practices into global organizations operating in multiple countries, each with its own pay-related laws, regulations and customs, requires complex functionality that many payroll systems simply do not have. Managing global payroll requires expertly maximizing human knowledge and intelligent systems to meet both international and regional requirements.

Outbound communication is used in a number of different contexts. For potential customers, traditional telemarketing still exists, though it is limited these days due to its minimal effectiveness. Instead, many customer-experience planners have substituted digital outbound over voice for lead generation and nurturing campaigns. Customers find text messages in the channel of their choice to be much less intrusive, and they are considerably less expensive than having contact center agents reach out.

I recently wrote about the need for organizations to take a holistic approach to the management and governance of data in motion alongside data at rest. As adoption of streaming data and event processing increases, it is no longer sufficient for streaming data projects to exist in isolation. Data needs to be managed and governed regardless of whether it is processed in batch or as a stream of events. This requirement has resulted in established data management vendors increasing their focus on streaming data and event processing through product development as well as acquisitions. It has also resulted in streaming and event specialists, such as Confluent, adding centralized management and governance capabilities to their existing offerings as they seek to establish or reinforce the strategic importance of streaming data as part of a modern approach to data management.

General Omar Bradley is credited with saying, “Amateurs study strategy, professionals study logistics.” This is a battlefield commander’s perspective on the often-overlooked importance of mastering the nitty-gritty in achieving military objectives. I think the same is true when it comes to data in business computing because, in my experience, it is often an overlooked or secondary consideration.

You would be forgiven for thinking that no one buys anything in person any more given the pages of digital ink spilled over the rise of digital commerce led by the rise and rise of Amazon. However, one quick errand run on a Saturday morning would easily give lie to this, as parking lots are full, not just at grocery stores but for everyday retail as well as big box stores. Likewise, in business-to-business (B2B) commerce, despite the advertised demise, person-to-person sales are still a major part of B2B purchases.

People analytics enable organizations to gain data-driven insights that optimize the impact and value of the workforce. For decades, human capital management (HCM) leaders have been sold tools marketed as analytics that were no more than dashboards filled with nice visualizations of historic data with no context as to what each individual data point meant to their strategic objectives and initiatives. And yet, our recent Analytics and Data Benchmark Research shows that 83% of organizations indicate that dashboards are very important or are currently in use for analytics. A dashboard, while important for a snapshot view of key metrics, is not an analytics tool. Today, advances in technology allow systems to provide actionable insights into potential people risks or opportunities before it’s too late.

Zoho presented analysts with a deep look at its strategy and roadmap at its July analyst conference, describing how it intends to meld its many business applications together through integration at the level of the platform. The company, which is privately owned and funded, has generally sought to build its own tools rather than buy or partner. This approach has allowed the firm to create a suite of tightly linked tools that share a common interface.

The migration to cloud is obvious. Organizations are adopting cloud computing for all variety of applications and use cases. Managed cloud services, commonly referred to as software as a service (SaaS), offer many benefits to organizations including significantly reduced labor costs for system administration and maintenance, as many of these costs are shifted to the software vendor. SaaS also provides organizations with faster time to value as they adopt new technologies by eliminating the need to acquire and configure hardware, and it also eliminates the need to install software. In fact, we assert that by 2025, nine in 10 organizations will be using multiple cloud applications in order to minimize the costs of administration and maintenance. Yet, there are some challenges associated with cloud computing I’d like to address in a series of Analyst Perspectives:

The lockdowns of 2020 forced accounting departments to adapt to managing their close-to-report cycle without face-to-face contact, prompting many to adopt digital technologies to facilitate the process. It gave further impetus to the digital transformation of the department, which aims to eliminate unnecessary manual tasks such as consolidations and reconciliations using software automation. And, rather than looking at the close as a set of discrete tasks, Controllers and CFOs increasingly are managing the process as a connected stream of responsibilities from pre-close activities to creating and publishing financial, management and external reports. This approach is consistent with what Ventana Research calls continuous accounting.

I have written recently about increased demand for data-intensive applications infused with the results of analytic processes, such as personalization and artificial intelligence (AI)-driven recommendations. Almost one-quarter of respondents (22%) to Ventana Research’s Analytics and Data Benchmark Research are currently analyzing data in real time, with an additional 10% analyzing data every hour. There are multiple data platform approaches to delivering real-time data processing and analytics and more agile data pipelines. These include the use of streaming and event data processing, as well as the use of hybrid data processing to enable analytics to be performed on application data within operational data platforms. Another approach, favored by a group of emerging vendors such as Rockset, is to develop these data-intensive applications on a specialist, real-time analytic data platform specifically designed to meet the performance and agility requirements of data-intensive applications.

Organizations are managing and analyzing large datasets every day, identifying patterns and generating insights to inform decisions. This can provide numerous benefits for an organization, such as improved operational efficiency, cost optimization, fraud detection, competitive advantage and enhanced business processes. By bringing the right, actionable data to the right user, organizations can potentially speed up processes and make more effective operational decisions.

Especially in the United States, baby boomer retirements and fewer graduates with accounting degrees is posing a growing challenge to finance department executives in attracting and retaining the best accounting talent. The solution, which may not seem obvious, is to make accounting cool, again.

It has been nearly two and a half years since the world was thrust into one of the most dramatic eras of workforce transformation in the modern era. Organizations have been forced to reevaluate everything about the workforce, from the physical spaces in which work is done, to compensation, to non-traditional benefits and work/life enhancement offerings. Even so, many continue to struggle to attract and retain the right talent to support operational needs. As leaders continue to redesign how they look at their talent pools, they have come to rely on their technology stacks more than ever to inform and enable new processes and experiences for candidates and employees, and recruiters and managers, throughout the entire talent life cycle. We at Ventana Research assert that by 2025, two-thirds of organizations will expect full talent life cycle support from their talent platform to bolster and unify the experience for candidates, recruiters, employees and managers.

When I looked at the state of analytics recently, it was clear that analytics are not as widely deployed within organizations as they should be. Only 23% of participants in our Analytics and Data Benchmark Research reported that more than one-half of their organization’s workforce are using analytics. There are many elements to becoming a data-driven organization, as my colleague Matt Aslett points out, but analytics are a necessary component. Our research shows that organizations recognize the importance of embedded analytics, ranking it the second most important digital technology in their analytics and data efforts behind big data and ahead of artificial intelligence and machine learning (AI/ML).

“Digital finance transformation” became an even more important topic over the past two years as finance and accounting departments have had to cope with an unrelenting set of new challenges that have had a profound impact on business operations, financial markets and regulatory environments. Digital technologies enable organizations to cope with change and improve performance by increasing efficiency, reducing risk, achieving greater visibility into opportunities, shortening process cycles and completing core processes. Digitizing department operations helps attract and retain the best talent because professionals spend less time on mechanical, repetitive tasks. Unfortunately, our research suggests that transformation is more talked about than done. I assert that by 2025, only one-third of finance departments will have achieved a level of technology competence to be described as digitally transformed while the CFOs of those that do will have greater influence in their organization's management.

I recently noted that as demand for real-time interactive applications becomes more pervasive, the use of streaming data is becoming more mainstream. Streaming data and event processing has been part of the data landscape for many decades, but for much of that time, data streaming was a niche activity. Although adopted in industry segments with high-performance, real-time data processing and analytics requirements such as financial services and telecommunications, data streaming was far less common elsewhere. That has changed significantly in recent years, fueled by the proliferation of open-source and cloud-based streaming data and event technologies that have lowered the cost and technical barriers to developing new applications able to take advantage of data in-motion. This is a trend we expect to continue, to the extent that streaming data and event processing becomes an integral part of mainstream data-processing architectures.

The analytics and business intelligence market landscape continues to grow as more organizations seek robust tools and capabilities to visualize and better understand data. BI systems are used to perform data analysis, identify market trends and opportunities and streamline business processes. They can collect and combine data from internal and external systems to present a holistic view.

The applicant tracking system, for all its shortcomings, revolutionized the way people found and applied for jobs when it first hit the market in the mid-1990s. Electronic applications quickly became the norm, resume or application review became more accessible for hiring teams and compliance was much more trackable and achievable, thanks to streamlined application processes. Today, tracking and compliance aren’t enough to power the complex world of recruitment. The Great Resignation has made it abundantly clear that candidates expect the same type of consumerized experience in the hiring process as they do when buying anything at all. To win or keep the best talent, organizations must make the hiring process personalized and enjoyable, and a traditional ATS simply cannot support that mandate.

Anaplan offers a cloud-based business planning platform that incorporates a modeling and calculation engine. The tool makes it relatively easy to add or expand the scope of plans that can be connected and monitored on a single platform. This Integrated Business Planning (IBP) approach enables organizations to use the software for financial planning or budgeting, sales, supply chain, workforce, marketing and IT planning. These are the types of plans in which companies often need to create models that incorporate their specific requirements, business systems and strategy. I expect that by 2025, one-fourth of financial planning and analysis (FP&A) groups will have implemented IBP.

I have recently written about the importance of healthy data pipelines to ensure data is integrated and processed in the sequence required to generate business intelligence, and the need for data pipelines to be agile in the context of real-time data processing requirements. Data engineers, who are responsible for monitoring, managing and maintaining data pipelines, are under increasing pressure to deliver high-performance and flexible data integration and processing pipelines that are capable of handling the rising volume and frequency of data. Automation is a potential solution to this challenge, and several vendors, such as Ascend.io, have emerged in recent years to reduce the manual effort involved in data engineering.

The contact center industry is reexamining how organizations engage with contact center agents. One thing that we learned from the forced movement to work-from-home was that organizations have to provide agents with appropriate tools to collaborate and communicate with peers and supervisors as well as workers in the back office who participate in all sorts of customer-facing or customer-adjacent processes. It is also important to provide supervisors with visibility into agent activity. That means extending existing coaching and evaluation methods. Ventana Research believes that by 2025, nearly every organization will have dedicated systems or processes that help supervisors manage remotely.

I recently explained how emerging application requirements were expanding the range of use cases for NoSQL databases, increasing adoption based on the availability of enhanced functionality. These intelligent applications require a close relationship between operational data platforms and the output of data science and machine learning projects. This ensures that machine learning and predictive analytics initiatives are not only developed and trained based on the relationships inherent in operational applications, but also that the resulting intelligence is incorporated into the operational application in real time to support capabilities such as personalization, recommendations and fraud detection. Graph databases already support operational use cases such as social media, fraud detection, customer experience management and recommendation engines. Graph database vendors such as Neo4j are increasingly focused on the role that graph databases can play in supporting data scientists, enabling them to develop, train and run algorithms and machine learning models on graph data in the graph database, rather than extracting it into a separate environment.

Environmental, social and governance issues have grown increasingly pressing over the past few years as investors and government entities urge organizations to measure and disclose ESG metrics. I’ve already covered the broader topic as it relates to external reporting and how financial planning and analysis groups are likely to own this mandate going forward. (It’s mainly been a marketing and public relations effort up to now.) FP&A departments are also likely to be charged with responsibility for internal ESG analysis and reporting, because to achieve environmental and social goals, organizations will need to assign specific objectives to individual business units and their responsible parties. I assert that by 2025 more than one-half of corporations required to comply with ESG reporting will centralize responsibility for preparing related reports and filings with FP&A to achieve accuracy, control and efficiency objectives. To do so, FP&A groups must immediately establish a data management strategy consistent with its targeted ESG analysis and reporting approach.

I often use the term “analytics” to refer to a broad set of capabilities, deliberately broader than business intelligence. In this Perspective, I’d like to share what decision-makers should consider as they evaluate the range of analytics requirements for their organization.

I spent years in the talent acquisition space, and I think that at least several months of that time – cumulatively – was spent just trying to get people to calm down. Talent acquisition is a critically important business process, but if I had a dollar for every time I had to remind someone that there really are no recruiting emergencies, I’d be a wealthy woman.

Streaming data has been part of the industry landscape for decades but has largely been focused on niche applications in segments with the highest real-time data processing and analytics performance requirements, such as financial services and telecommunications. As demand for real-time interactive applications becomes more pervasive, streaming data is becoming a more mainstream pursuit, aided by the proliferation of open-source streaming data and event technologies, which have lowered the cost and technical barriers to developing new applications that take advantage of data in motion. Ventana Research’s Streaming Data Dynamic Insights enables an organization to assess its relative maturity in achieving value from streaming data. I assert that by 2024, more than one-half of all organizations’ standard information architectures will include streaming data and event processing, allowing organizations to be more responsive and provide better customer experiences.

Field service is a segment of customer experience that is dominated by two elements: the complexity of the issues handled, and the high cost of providing on-site services. It is recognized as a critical component of the service experience, especially when managing the condition of high-precision equipment in the medical, manufacturing and utility industries. It is also a high-risk moment in the customer life cycle. Consumers often experience the process as a series of disconnected visits and handoffs that fail to resolve issues the first time.

Organizations are collecting vast amounts of data every day, utilizing business intelligence software and data visualization to gain insights and identify patterns and errors in the data. Making sense of these patterns can enable an organization to gain an edge in the marketplace and plan more strategically.

Although the digital transformation of the finance department was a topic of discussion before 2020, it became a front-and-center issue as organizations locked down and in-office interactions became impossible. Finance and accounting departments were immediately confronted with a challenge because of their limited adoption of technology that would support a virtual working environment. As our 2019 Office of Finance Benchmark Research found, they are technological laggards: 45% are at the tactical or lowest level of competence in using technology across multiple processes and functions, while only 12% are at the highest. In my experience, many finance and accounting professionals and those running the department do not necessarily think that such competence is necessary, but this thinking is outdated because, increasingly, technology is the only practical way to address the department’s responsibilities (for example, the new revenue recognition for contracts accounting standards). To gain full advantage of technology, finance and accounting organizations must become “fast followers,” avoiding the bleeding edge but breaking the habit of waiting until the last possible moment before adopting proven advances.

When joining Ventana Research, I noted that the need to be more data-driven has become a mantra among large and small organizations alike. Data-driven organizations stand to gain competitive advantage, responding faster to worker and customer demands for more innovative, data-rich applications and personalized experiences. Being data-driven is clearly something to aspire to. However, it is also a somewhat vague concept without clear definition. We know data-driven organizations when we see them — the likes of Airbnb, DoorDash, ING Bank, Netflix, Spotify, and Uber are often cited as examples — but it is not necessarily clear what separates the data-driven from the rest. Data has been used in decision-making processes for thousands of years, and no business operates without some form of data processing and analytics. As such, although many organizations may aspire to be more data-driven, identifying and defining the steps required to achieve that goal are not necessarily easy. In this Analyst Perspective, I will outline the four key traits that I believe are required for a company to be considered data-driven.

Since its inception 20 years ago, Ventana Research has advocated for a shorter accounting close because it can improve the performance of the entire organization, not just finance and accounting. An important benefit of a shorter close is increased staff time for analysis and the preparation of reports and narratives that improve communications with the board and outside investors. Similarly, the department can provide those in operating roles the financial and managerial accounting results to highlight opportunities and issues they must address.

There are more digital channels in the commerce space than ever before: the web, mobile apps, text, voice-activated “agents,” video and social channels. Conversational computing and hyper-personalization are transforming customer engagement, and organizations may need to undergo a digital platform renovation to optimize customer and product experiences or risk lagging behind competitors. B2B selling and buying are increasingly using methods similar to B2C digital approaches to mirror the digital commerce experience that has grown substantially within the last few years. Salesforce Commerce Cloud is one of the platforms utilizing this approach.

We’ve recently published our latest Benchmark Research on Data Governance and it’s fair to say, “you’ve come a long way, baby.” Many of you reading this weren’t around when that phrase was introduced in 1968 to promote Virginia Slims cigarettes, but you may have heard the phrase because it went on to become a part of popular culture. We’ve learned a lot about cigarettes since then, and we’ve learned a lot about data governance, too.

In my more than two decades in the world of human resources and human capital management technology, I have never seen a topic become so completely ubiquitous so quickly as has employee experience. This is great news from my perspective. As I addressed in this recent analyst perspective, market factors have forced organizations to acknowledge the tremendous bottom-line value of an engaged workforce, and that engagement is wholly dependent upon an employer's commitment to providing a personalized, well-rounded experience.

We conducted our recent Smart Close Dynamic Insights Research in part to assess to what extent the substantial disruptions of the pandemic have impacted the accounting close. When office lockdowns began in the first quarter of 2020, many finance departments were challenged by having to do their quarterly close remotely without their normal face-to-face interactions. In the United States, the Securities and Exchange Commission was so concerned that corporations would be unable to meet their filing deadlines that they gave registrants carte blanche to extend their filing if necessary. As it turned out, only a relative handful did, and all but one of those was based in China; but for many, that first calendar close required a heroic effort. Since then, organizations have made concerted efforts to adopt and use technology to enable them to operate resiliently under any conditions. Our research finds that while organizations have to some extent adapted to operating a more remote working environment, progress toward a faster close has been elusive. The research also confirms that organizations that use technology effectively to automate processes are better able to complete their close sooner.

OneStream offers a platform designed to serve the needs of accounting and financial planning and analysis organizations. The software handles financial close and consolidation, planning and budgeting, analysis and reporting. For me, the most significant announcement at the company’s recent user conference was the unveiling of its Sensible ML (Machine Learning) offering, which is in limited general release. I’ve commented on the importance of artificial intelligence in business applications, and Sensible ML is a promising and important step in that direction.

I recently wrote about the growing range of use cases for which NoSQL databases can be considered, given increased breadth and depth of functionality available from providers of the various non-relational data platforms. As I noted, one category of NoSQL databases — graph databases — are inherently suitable for use cases that rely on relationships, such as social media, fraud detection and recommendation engines, since the graph data model represents the entities and values and also the relationships between them. The native representation of relationships can also be significant in surfacing “features” for use in machine learning modeling. There has been a concerted effort in recent years by graph database providers, including TigerGraph, to encourage and facilitate the use of graph databases by data scientists to support the development, testing and deployment of machine learning models.

“Lead to cash” is an often-used term and is a companion to “quote to cash” and “order to cash”. What they all represent is an approach which recognizes that there is a process designed to convert a lead from a qualified interest to an active sale, through quote and contract negotiation, to order or contract, invoice and payment. “Quote to cash” and “order to cash” are subsets of this process, with different starting places, but ultimately end in the same place: with a payment for a delivered product or service.

A few years ago – somewhat tongue in cheek – I began using the term “data pantry” to describe a type of data store that’s part of a business application platform, created for a specific set of users and use cases. It’s a data pantry because, unlike a general-purpose data store such as a data warehouse, everything the user needs is readily available and easily accessible, with labels that are immediately recognized and understood.

Organizations are continuously increasing the use of analytics and business intelligence to turn data into meaningful and actionable insights. Our Analytics and Data Benchmark Research shows some of the benefits of using analytics: Improved efficiency in business processes, improved communication and gaining a competitive edge in the market top the list. With a unified BI system, organizations can have a comprehensive view of all organizational data to better manage processes and identify opportunities.

I previously described the concept of hydroanalytic data platforms, which combine the structured data processing and analytics acceleration capabilities associated with data warehousing with the low-cost and multi-structured data storage advantages of the data lake. One of the key enablers of this approach is interactive SQL query engine functionality, which facilitates the use of existing business intelligence (BI) and data science tools to analyze data in data lakes. Interactive SQL query engines have been in use for several years — many of the capabilities were initially used to accelerate analytics on Hadoop — but have evolved along with data lake initiatives to enable analysis of data in cloud object storage. The open source Presto project is one of the most prominent interactive SQL query engines and has been adopted by some of the largest digital-native organizations. Presto managed-services provider Ahana is on a mission to bring the advantages of Presto to the masses.