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.
Topics: Analytics, Data Governance, Data Management, Data, data operations, Analytic Data Platforms
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.
Topics: embedded analytics, Analytics, Business Intelligence
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.
Topics: Analytics, Digital Technology, AI and Machine Learning, Analytics & Data
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.
Topics: embedded analytics, Analytics, Business Intelligence, AI and Machine Learning
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.
Topics: embedded analytics, Analytics, Business Intelligence, Data, Digital Technology, natural language processing, Process Mining, Analytics and Data, Collaborative & Conversational Computing
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.
Topics: Sales, Analytics, Internet of Things, Data, Sales Performance Management, Digital Technology, Digital Commerce, Conversational Computing, AI and Machine Learning, mobile computing, Subscription Management, extended reality, intelligent sales, partner management, Sales Engagement
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.
Topics: Customer Experience, Voice of the Customer, CEM, Self-service, Analytics, Contact Center, AI and Machine Learning, agent management, Customer Experience Management, Field Service
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.
Topics: Analytics, Cloud Computing, Internet of Things, Data, Digital Technology, blockchain, AI and Machine Learning, mobile computing, extended reality, robotic automation, Collaborative & Conversational Computing
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.
Topics: Customer Experience, Human Capital Management, Marketing, Office of Finance, Analytics, Data, Digital Technology, Operations & Supply Chain, Digital Business, Office of Revenue
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.
Topics: business intelligence, Analytics, Data, Digital Technology, AI and Machine Learning, Digital Business, Analytics and Data, Analytic Data Platforms
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.
Topics: embedded analytics, Analytics, Business Intelligence, natural language processing, AI and Machine Learning
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.
Topics: Analytics, Business Intelligence, Digital Technology, AI and Machine Learning, Analytics & Data
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.
Topics: Analytics, Business Intelligence, Data Governance, Data, Digital Technology, Analytics & Data
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.
Topics: Analytics, Business Intelligence, Cloud Computing, Data Governance, Digital Technology, AI and Machine Learning, Analytics & Data, Governance & Risk
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).
Topics: Analytics, Business Intelligence, Digital Technology, AI and Machine Learning, Analytics & Data
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.
Topics: Customer Experience, Human Capital Management, Marketing, Office of Finance, Analytics, Data, Digital Technology, Operations & Supply Chain, Digital Business, Office of Revenue
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.
Topics: embedded analytics, Analytics, Business Intelligence, natural language processing, AI and Machine Learning, Streaming Analytics
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.
Topics: embedded analytics, Analytics, Business Intelligence, IBM, IBM Watson, AI and Machine Learning
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.
Topics: embedded analytics, Analytics, Business Intelligence, Data Governance, Data Management, data operations, Analytics & Data
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.
Topics: Analytics, AI and Machine Learning
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.
Topics: Analytics, Business Intelligence, AI and Machine Learning, Process Mining, Streaming Analytics
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.
Topics: Analytics, Business Intelligence, AI and Machine Learning, Process Mining
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.
Topics: Analytics, Business Intelligence, natural language processing, AI and Machine Learning, Analytics & Data, Collaborative & Conversational Computing
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).
Topics: embedded analytics, Analytics, Analytics & Data
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.
Topics: Analytics, Business Intelligence, Data Governance, Data Management, AI and Machine Learning, Analytics & Data
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.
Topics: embedded analytics, Analytics, Business Intelligence, AI and Machine Learning
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.
Topics: embedded analytics, Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, natural language processing, data lakes, AI and Machine Learning, data operations, Digital Business, Streaming Analytics, data platforms, Analytics & Data, Streaming Data & Events
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.
Topics: business intelligence, Analytics, Cloud Computing, Data, Digital Technology, AI and Machine Learning, data platforms, Analytics & Data
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.
Topics: Analytics, Business Intelligence, Cloud Computing, Data, Digital Technology, data lakes, AI and Machine Learning, data operations, data platforms, Analytics & Data
I previously explained how the data lakehouse is one of two primary approaches being adopted to deliver what I have called a hydroanalytic data platform. Hydroanalytics involves the combination of data warehouse and data lake functionality to enable and accelerate analysis of data in cloud storage services. The term data lakehouse has been rapidly adopted by several vendors in recent years to describe an environment in which data warehousing functionality is integrated into the data lake environment, rather than coexisting alongside. One of the vendors that has embraced the data lakehouse concept and terminology is Dremio, which recently launched the general availability of its Dremio Cloud data lakehouse platform.
Topics: business intelligence, Analytics, Data, data lakes, data platforms
As I recently described, it is anticipated that the majority of database workloads will continue to be served by specialist data platforms targeting operational and analytic workloads, albeit with growing demand for hybrid data processing use-cases and functionality. Specialist operational and analytic data platforms have historically been the since preferred option, but there have always been general-purpose databases that could be used for both analytic and operational workloads, with tuning and extensions to meet the specific requirements of each.
Topics: Analytics, Business Intelligence, Cloud Computing, Data, Digital Technology, data platforms, Analytics & Data
I recently wrote about the potential benefits of data mesh. As I noted, data mesh is not a product that can be acquired, or even a technical architecture that can be built. It’s an organizational and cultural approach to data ownership, access and governance. While the concept of data mesh is agnostic to the technology used to implement it, technology is clearly an enabler for data mesh. For many organizations, new technological investment and evolution will be required to facilitate adoption of data mesh. Meanwhile, the concept of the data fabric, a technology-driven approach to managing and governing data across distributed environments, is rising in popularity. Although I previously touched on some of the technologies that might be applicable to data mesh, it is worth diving deeper into the data architecture implications of data mesh, and the potential overlap with data fabric.
Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data, AI and Machine Learning, data operations, data platforms, Streaming Data & Events
I recently described the use cases driving interest in hybrid data processing capabilities that enable analysis of data in an operational data platform without impacting operational application performance or requiring data to be extracted to an external analytic data platform. Hybrid data processing functionality is becoming increasingly attractive to aid the development of intelligent applications infused with personalization and artificial intelligence-driven recommendations. These applications can be used to improve customer service; engagement, detect and prevent fraud; and increase operational efficiency. Several database providers now offer hybrid data processing capabilities to support these application requirements. One of the vendors addressing this opportunity is SingleStore.
Topics: Analytics, Business Intelligence, Cloud Computing, Data, Digital Technology, AI and Machine Learning, data platforms, Analytics & Data
I recently described how the operational data platforms sector is in a state of flux. There are multiple trends at play, including the increasing need for hybrid and multicloud data platforms, the evolution of NoSQL database functionality and applicable use-cases, and the drivers for hybrid data processing. The past decade has seen significant change in the emergence of new vendors, data models and architectures as well as new deployment and consumption approaches. As organizations adopted strategies to address these new options, a few things remained constant – one being the influence and importance of Oracle. The company’s database business continues to be a core focus of innovation, evolution and differentiation, even as it expanded its portfolio to address cloud applications and infrastructure.
Topics: business intelligence, Analytics, Data Integration, Data, AI and Machine Learning, data platforms
Organizations have been using data virtualization to collect and integrate data from various sources, and in different formats, to create a single source of truth without redundancy or overlap, thus improving and accelerating decision-making giving them a competitive advantage in the market. Our research shows that data virtualization is popular in the big data world. One-quarter (27%) of participants in our Data Lake Dynamic Insights Research reported they were currently using data virtualization, and another two-quarters (46%) planned to include data virtualization in the future. Even more interesting, those who are using data virtualization reported higher rates of satisfaction (79%) with their data lake than those who are not (36%). Our Analytics and Data Benchmark Research shows more than one-third of organizations (37%) are using data virtualization in that context. Here, too, those using data virtualization reported higher levels of satisfaction (88%) than those that are not (66%).
Topics: embedded analytics, Analytics, Business Intelligence, AI and Machine Learning, Streaming Analytics
I recently wrote about the importance of data pipelines and the role they play in transporting data between the stages of data processing and analytics. Healthy data pipelines are necessary to ensure data is integrated and processed in the sequence required to generate business intelligence. The concept of the data pipeline is nothing new of course, but it is becoming increasingly important as organizations adapt data management processes to be more data driven.
Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, Digital transformation, data lakes, AI and Machine Learning, data operations, Digital Business, data platforms, Analytics & Data, Streaming Data & Events
Data governance is an issue that impacts all organizations large and small, new and old, in every industry, and every region of the world. Data governance ensures that an organization’s data can be cataloged, trusted and protected, improving business processes to accelerate analytics initiatives and support compliance with regulatory requirements. Not all data governance initiatives will be driven by regulatory compliance; however, the risk of falling foul of privacy (and human rights) laws ensures that regulatory compliance influences data-processing requirements and all data governance projects. Multinational organizations must be cognizant of the wide variety of regional data security and privacy requirements, not least the European Union’s General Data Protection Regulation (GDPR). The GDPR became enforceable in 2018, protects the privacy of personal or professional data, and carries with it the threat of fines of up to 20 million euros ($22 million) or 4% of a company’s global revenue. Europe is not alone in regulating against the use of personally identifiable information (other similar regulations include The California Consumer Privacy Act) but Ventana Research’s Data Governance Benchmark Research illustrates that there are differing attitudes and approaches to data governance on either side of the Atlantic.
Topics: Analytics, Data Governance, Data
I recently described the growing level of interest in data mesh which provides an organizational and cultural approach to data ownership, access and governance that facilitates distributed data processing. As I stated in my Analyst Perspective, data mesh is not a product that can be acquired or even a technical architecture that can be built. Adopting the data mesh approach is dependent on people and process change to overcome traditional reliance on centralized ownership of data and infrastructure and adapt to its principles of domain-oriented ownership, data as a product, self-serve data infrastructure and federated governance. Many organizations will need to make technological changes to facilitate adoption of data mesh, however. Starburst Data is associated with accelerating analysis of data in data lakes but is also one of several vendors aligning their products with data mesh.
Topics: Business Continuity, Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, data lakes, Digital Business, data platforms, Analytics & Data
I have written previously that the world of data and analytics will become more and more centered around real-time, streaming data. Data is created constantly and increasingly is being collected simultaneously. Technology advances now enable organizations to process and analyze information as it is being collected to respond in real time to opportunities and threats. Not all use cases require real-time analysis and response, but many do, including multiple use cases that can improve customer experiences. For example, best-in-class e-commerce interactions should provide real-time updates on inventory status to avoid stock-out or back-order situations. Customer service interactions should provide real-time recommendations that minimize the time to resolution. Location-based offers should be targeted at the customer’s current location, not their location several minutes ago. Another domain where real-time analyses are critical is internet of things (IoT) applications. Additionally, use cases like predictive maintenance require timely information to prevent equipment failures that help avoid additional costs and damage.
Topics: business intelligence, Analytics, Internet of Things, Data, Digital Technology, AI and Machine Learning, Streaming Analytics, Analytics & Data, Streaming Data & Events
Data mesh is the latest trend to grip the data and analytics sector. The term has been rapidly adopted by numerous vendors — as well as a growing number of organizations —as a means of embracing distributed data processing. Understanding and adopting data mesh remains a challenge, however. Data mesh is not a product that can be acquired, or even a technical architecture that can be built. It is an organizational and cultural approach to data ownership, access and governance. Adopting data mesh requires cultural and organizational change. Data mesh promises multiple benefits to organizations that embrace this change, but doing so may be far from easy.
Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, Digital transformation, data lakes, data operations, Digital Business, data platforms, Analytics & Data, Streaming Data & Events
For years, maybe decades, we have heard about the struggles between IT and line-of-business functions. In this perspective, we will look at some of the data from our Analytics and Data Benchmark Research about the roles of IT and line-of-business teams in analytics and data processes. We will also look at some of the disconnects between these two groups. And, by looking at how organizations are operating today and the results they are achieving, we can discern some of the best practices for improving the outcomes of analytics and data processes.
Topics: Analytics, Business Intelligence, Data, Digital Technology, AI and Machine Learning, Analytics & Data
Despite widespread and increasing use of the cloud for data and analytics workloads, it has become clear in recent years that, for most organizations, a proportion of data-processing workloads will remain on-premises in centralized data centers or distributed-edge processing infrastructure. As we recently noted, as compute and storage are distributed across a hybrid and multi-cloud architecture, so, too, is the data it stores and relies upon. This presents challenges for organizations to identify, manage and analyze all the data that is available to them. It also presents opportunities for vendors to help alleviate that challenge. In particular, it provides a gap in the market for data-platform vendors to distinguish themselves from the various cloud providers with cloud-agnostic data platforms that can support data processing across hybrid IT, multi-cloud and edge environments (including Internet of Things devices, as well as servers and local data centers located close to the source of the data). Yellowbrick Data is one vendor that has seized upon that opportunity with its cloud Data Warehouse offering.
Topics: Analytics, Business Intelligence, Data Governance, Data, AI and Machine Learning, data operations, data platforms
Organizations face various challenges with analytics and business intelligence processes, including data curation and modeling across disparate sources and data warehouses, maintaining data quality and ensuring security and governance. Traditional processes are slow when transforming large and diverse datasets into something which is easily consumable in BI. And, it can take days or weeks to create reports and dashboards — maybe longer if processes change and new data sources are introduced. Our Analytics and Data Benchmark Research shows that the most time-consuming processes are preparing data, reviewing it for quality issues and preparing reports for presentation and distribution.
Topics: Big Data, Analytics, Business Intelligence, Cloud
The various NoSQL databases have become a staple of the data platforms landscape since the term entered the IT industry lexicon in 2009 to describe a new generation of non-relational databases. While NoSQL began as a ragtag collection of loosely affiliated, open-source database projects, several commercial NoSQL database providers are now established as credible alternatives to the various relational database providers, while all the major cloud providers and relational database giants now also have NoSQL database offerings. Almost one-quarter (22%) of respondents to Ventana Research’s Analytics and Data Benchmark Research are using NoSQL databases in production today, and adoption is likely to continue to grow. More than one-third (34%) of respondents are planning to adopt NoSQL databases within two years (21%) or are evaluating (14%) their potential use. Adoption has been accelerated by the evolving functionality offered by NoSQL products and services, the growing maturity of specialist NoSQL vendors, and new commercial offerings from cloud providers and established database providers alike. This evolution is exemplified by the changing meaning of the term NoSQL itself. While it was initially associated with a rejection of the relational database hegemony, it has retroactively been reinterpreted to mean “Not Only SQL,” reflecting the potential for these new databases to coexist with and complement established approaches.
Topics: Analytics, Data, AI and Machine Learning, data platforms
Natural language processing (NLP) is a field that combines artificial intelligence (AI), data science and linguistics that enables computers to understand, interpret and manipulate text or spoken words. NLP includes generating narratives based on a set of data values, using text or speech as inputs to access information, and analysing text or speech, for instance, to determine its sentiment. There are various techniques for interpreting human language, ranging from statistical and machine learning (ML) methods to rules-based and algorithmic approaches. In this perspective, we will focus on two aspects of NLP: natural language query (NLQ), which offers the ability to use natural language expressions to discover and understand data, and natural language generation (NLG), which uses AI to produce written or spoken narratives from a dataset. NLQ and NLG enable business personnel to communicate information needs with business intelligence (BI) systems more easily.
Topics: Big Data, Analytics, natural language processing, NLP
Despite all the advances organizations have made with respect to analytics, our most recent research shows the majority of the workforce in the majority of organizations are not using analytics and business intelligence (BI). Less than one-quarter (23%) report that one-half or more of their workforce is using analytics and BI. This is a problem. It means organizations are not enabling their workforce to perform at peak efficiency and effectiveness. It means the workforce in many organizations does not have access to the same information by which they are being measured. It means organizations must find other ways to communicate with, and manage, the workforce.
Topics: Sales, business intelligence, embedded analytics, Analytics, Data, Sales Performance Management, Digital Technology, Digital Commerce, natural language processing, Subscription Management, partner management, Revenue Management, Sales Engagement, Collaborative & Conversational Computing
I recently described the emergence of hydroanalytic data platforms, outlining how the processes involved in generating energy from a lake or reservoir were analogous to those required to generate intelligence from a data lake. I explained how structured data processing and analytics acceleration capabilities are the equivalent of turbines, generators and transformers in a hydroelectric power station. While these capabilities are more typically associated with data warehousing, they are now being applied to data lake environments as well. Structured data processing and analytics acceleration capabilities are not the only things required to generate insights from data, however, and the hydroelectric power station analogy further illustrates this. For example, generating hydroelectric power also relies on pipelines to ensure that the water is transported from the lake or reservoir at the appropriate volume to drive the turbines. Ensuring that a hydroelectric power station is operating efficiently also requires the collection, monitoring and analysis of telemetry data to confirm that the turbines, generators, transformers and pipelines are functioning correctly. Similarly, generating intelligence from data relies on data pipelines that ensure the data is integrated and processed in the correct sequence to generate the required intelligence, while the need to monitor the pipelines and processes in data-processing and analytics environments has driven the emergence of a new category of software: data observability.
Topics: Analytics, Data Governance, Data, Digital Technology, data lakes, AI and Machine Learning, data operations, data platforms, Streaming Data & Events
Many organizations invest in data governance out of concern over misuse of data or potential data breaches. These are important considerations and valid aspects of data governance programs. However, good data governance also has positive impacts on organizations. For example, I have previously written about the valuable connection between the use of data catalogs and satisfaction with an organization’s data lake. Our most recent Analytics and Data Benchmark Research demonstrates some of the beneficial links between data governance and analytics. In this Perspective, I’ll share some of the correlations identified in our research.
Topics: embedded analytics, Analytics, Data Governance, Data, Digital Technology
As I stated when joining Ventana Research, the socioeconomic impacts of the pandemic and its aftereffects have highlighted more than ever the differences between organizations that can turn data into insights and are agile enough to act upon it and those that are incapable of seeing or responding to the need for change. Data-driven organizations stand to gain competitive advantage, responding faster to worker and customer demands for more innovative, data-rich applications and personalized experiences. One of the key methods that accelerates business decision-making is reducing the lag between data collection and data analysis.
Topics: Analytics, Business Intelligence, Data Integration, Data, data lakes, AI and Machine Learning, data operations, data platforms, Streaming Data & Events
I recently described how the data platforms landscape will remain divided between analytic and operational workloads for the foreseeable future. Analytic data platforms are designed to store, manage, process and analyze data, enabling organizations to maximize data to operate with greater efficiency, while operational data platforms are designed to store, manage and process data to support worker-, customer- and partner-facing operational applications. At the same time, however, we see increased demand for intelligent applications infused with the results of analytic processes, such as personalization and artificial intelligence-driven recommendations. The need for real-time interactivity means that these applications cannot be served by traditional processes that rely on the batch extraction, transformation and loading of data from operational data platforms into analytic data platforms for analysis. Instead, they rely on analysis of data in the operational data platform itself via hybrid data processing capabilities to accelerate worker decision-making or improve customer experience.
Topics: embedded analytics, Analytics, Business Intelligence, Data, Digital Technology, AI and Machine Learning, data platforms, Analytics & Data, Streaming Data & Events
Organizations of all sizes are dealing with exponentially increasing data volume and data sources, which creates challenges such as siloed information, increased technical complexities across various systems and slow reporting of important business metrics. Migrating to the cloud does not solve the problems associated with performing analytics and business intelligence on data stored in disparate systems. Also, the computing power needed to process large volumes of data consists of clusters of servers with hundreds or thousands of nodes that can be difficult to administer. Our Analytics and Data Benchmark Research shows that organizations have concerns about current analytics and BI technology. Findings include difficulty integrating data with other business processes, systems that are not flexible enough to scale operations and trouble accessing data from various data sources.
Topics: Analytics, Business Intelligence, Data Integration, Data, data lakes, AI and Machine Learning, data operations, Streaming Analytics
Ventana Research recently announced its 2022 Market Agenda for the Office of Revenue, continuing the guidance we have offered for nearly two decades to help organizations realize optimal value from applying technology to improve business outcomes. Chief sales and revenue officers and their associated operations teams are experts in their respective fields but may not have the guidance needed to employ technology effectively. As we look to 2022, we are focusing on the entire selling and buying life cycle and the applications that simplify and improve interactions throughout the customer experience.
Topics: Sales, Analytics, Internet of Things, Data, Sales Performance Management, Digital Technology, Digital Commerce, Conversational Computing, AI and Machine Learning, mobile computing, Subscription Management, extended reality, intelligent sales, partner management, Sales Engagement
Ventana Research recently announced its 2022 Market Agenda for Analytics, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value from technology investments in order to improve business outcomes.
Topics: embedded analytics, Analytics, Business Intelligence, Digital Technology, natural language processing, Process Mining, Analytics & Data, Collaborative & Conversational Computing
I’m proud to share Ventana Research’s 2022 Market Agenda for Digital Technology. Our focus in this agenda is to deliver expertise to help organizations prioritize technology investments that increase workforce effectiveness and organizational agility, ensuring ongoing operations during any type of disruption.
Topics: Analytics, Cloud Computing, Internet of Things, Data, Digital Technology
Ventana Research has announced its market agenda for 2022, continuing the tradition of reliability in our efforts to educate and guide the technology market. Our assessments are backed by our expertise and independence, as we do not share our market agenda or our research – including analyst and market perspectives or our Value Index – with any external party until it is published. We review and 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.
Topics: Customer Experience, Human Capital Management, Marketing, Office of Finance, Analytics, Data, Digital Technology, Operations & Supply Chain, Digital Business, Office of Revenue, Market Agenda
Organizations today have huge volumes of data across various cloud and on-premises systems which keep growing by the second. To derive value from this data, organizations must query the data regularly and share insights with relevant teams and departments. Automating this process using natural language processing (NLP) and artificial intelligence and machine learning (AI/ML) enables line-of-business personnel to query the data faster, generate reports themselves without depending on IT, and make quick decisions. Some organizations have started using NLP in self-service analytics to quickly identify patterns and simplify data visualization. Our Analytics and Data Benchmark Research finds that about 81% of organizations expect to use natural language search for analytics to make timely and informed decisions.
Topics: embedded analytics, Analytics, Business Intelligence, Data Integration, Data, natural language processing, data lakes, AI and Machine Learning, data operations, data platforms
Few trends have had a bigger impact on the data platforms landscape than the emergence of cloud computing. The adoption of cloud computing infrastructure as an alternative to on-premises datacenters has resulted in significant workloads being migrated to the cloud, displacing traditional server and storage vendors. Almost one-half (49%) of respondents to Ventana Research’s Analytics and Data Benchmark Research currently use cloud computing products for analytics and data, and a further one-quarter plan to do so. In addition to deploying data workloads on cloud infrastructure, many organizations have also adopted cloud data and analytics services offered by the same cloud providers, displacing traditional data platform vendors. Organizations now have greater choice in relation to potential products and providers for data and analytics workloads, but also need to think about integrating services offered by cloud providers with established technology and processes. Having pioneered the concept, Amazon Web Services has arguably benefitted more than most from adoption of cloud computing, and is also in the process of expanding and adjusting its portfolio to alleviate challenges and encourage even greater adoption.
Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data
Organizations today are working with multiple applications and systems, including enterprise resource planning (ERP), customer relationship management (CRM), supply chain management (SCM) and other systems, where data can easily become fragmented and siloed. And as the organization increases its data sources and adds more systems and custom applications, it becomes challenging to manage the data consistently and keep data definitions up to date. This increases the need to use master data management (MDM) software that can provide a single source of truth to drive accurate analytics and business operations.
Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, Analytics & Data
Pricing is an issue that almost every for-profit company confronts – and usually agonizes over. Chief financial officers must play a part in setting the strategic direction of pricing in their organization. They should not be involved in tactical pricing decisions because they are not close enough to markets and customers, but they should be part of the strategic design of pricing, especially as part of a profitability management effort, which I’ve discussed before.
Topics: Office of Finance, Analytics, Financial Performance Management, Digital Commerce, Digital Business, Revenue Management, Sales Engagement
The need for data-driven decision-making requires organizations to transform not only the approach to business intelligence and data science but also accelerate the development of new operational applications that support greater business agility, enable cloud- and mobile-based consumption, and deliver more interactive and personalized experiences. To stay competitive, organizations need to prioritize the development of new, data-driven applications. As a result, many have been encouraged to invest in new data platforms designed to support agile development and cloud-based delivery. This is one of the factors driving the growth of MongoDB, and continues to drive the evolution of its document database into what is now described as a cloud-based application data platform.
Topics: Analytics, Cloud Computing, Analytics & Data
The term NoSQL has been a misnomer ever since it appeared in 2009 to describe a group of emerging databases. It was true that a lack of support for Structured Query Language (SQL) was common to the various databases referred to as NoSQL. However, it was always one of a number of common characteristics, including flexible schema, distributed data processing, open source licensing, and the use of non-relational data models (key value, document, graph) rather than relational tables. As the various NoSQL databases have matured and evolved, many of them have added support for SQL terms and concepts, as well as the ability to support SQL format queries. Couchbase has been at the forefront of this effort, recognizing that to drive greater adoption of NoSQL databases in general (and its distributed document database in particular) it was wise to increase compatibility with the concepts, tools and skills that have dominated the database market for the past 50 years.
Topics: Business Continuity, Analytics, Data, Digital Technology, Digital Business, data platforms
Data lakes have enormous potential as a source of business intelligence. However, many early adopters of data lakes have found that simply storing large amounts of data in a data lake environment is not enough to generate business intelligence from that data. Similarly, lakes and reservoirs have enormous potential as sources of energy. However, simply storing large amounts of water in a lake is not enough to generate energy from that water. A hydroelectric power station is required to harness and unleash the power-generating potential of a lake or reservoir, utilizing a combination of turbines, generators and transformers to convert the energy of the flowing water into electricity. A hydroanalytic data platform, the data equivalent of a hydroelectric power station, is required to harness and unleash the intelligence-generating potential of a data lake.
Topics: Analytics, Business Intelligence, Cloud Computing, Data Governance, Data Integration, Data, Digital Technology, data lakes, AI and Machine Learning, data operations, data platforms
As I noted when joining Ventana Research, the range of options faced by organizations in relation to data processing and analytics can be bewildering. When it comes to data platforms, however, there is one fundamental consideration that comes before all others: Is the workload primarily operational or analytic? Although most database products can be used for operational or analytic workloads, the market has been segmented between products targeting operational workloads, and those targeting analytic workloads for almost as long as there has been a database market.
Topics: business intelligence, Analytics, Data, data lakes, AI and Machine Learning, data operations, data platforms
Any organization that relies heavily on a large labor force looks to automation to reduce costs, and contact centers are no exception. They handle interactions at such large scale that almost any effort to automate some part of the process can deliver measurable efficiencies. Two factors have ratcheted up attention on automating customer experience workflows: the dramatic expansion of digital interaction channels, and the development of artificial intelligence and machine learning tools to facilitate workflow deployment.
Topics: Customer Experience, Voice of the Customer, Analytics, Data Integration, Contact Center, Data, AI and Machine Learning, agent management, data operations, Digital Business, Experience Management, Customer Experience Management, Field Service
TIBCO is a large, independent cloud-computing and data analytics software company that offers integration, analytics, business intelligence and events processing software. It enables organizations to analyze streaming data in real time and provides the capability to automate analytics processes. It offers more than 200 connectors, more than 200 enterprise cloud computing and application adapters, and more than 30 non-relational structured query language databases, relational database management systems and data warehouses.
Topics: embedded analytics, Analytics, Collaboration, Data Governance, Information Management, Data, Digital Technology, data lakes, AI and Machine Learning
Organizations have become more agile and responsive, in part, as a result of being more agile with their information technology. Adopting a DevOps approach to application deployment has allowed organizations to deploy new and revised applications more quickly. DataOps is enabling organizations to be more agile in their data processes. As organizations are embracing artificial intelligence (AI) and machine learning (ML), they are recognizing the need to adopt MLOps. The same desire for agility suggests that organizations need to adopt AnalyticOps.
Topics: business intelligence, Analytics, Data Governance, Data, Digital Technology, data operations, data platforms
The emergence of the Chief Revenue Officer (CRO) has mirrored the adoption of the subscription model and the development of multiple selling and buying channels over and above the traditional direct sales model, referred to as Revenue Management. Supporting the traditional sales team and management was the sales operations team with responsibilities around incentive compensation, territory and quota planning, sales metrics and reporting and sales forecasting as well as sales engagement and enablement tools and applications. Aligned with this functional area under the CRO is another set of roles and functions called revenue operations or RevOps.
Topics: Sales, Customer Experience, Analytics, Sales Performance Management, Digital Commerce, Subscription Management, partner management, Revenue Management, Sales Engagement
Talend is a data integration and management software company that offers applications for cloud computing, big data integration, application integration, data quality and master data management. The platform enables personnel to work with relational databases, Apache Hadoop, Spark and NoSQL databases for cloud or on-premises jobs. Talend data integration software offers an open and scalable architecture and can be integrated with multiple data warehouses, systems and applications to provide a unified view of all data. Its code generation architecture uses a visual interface to create Java or SQL code.
Topics: Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, Digital Technology, data lakes, AI and Machine Learning
Enterprises looking to adopt cloud-based data processing and analytics face a disorienting array of data storage, data processing, data management and analytics offerings. Departmental autonomy, shadow IT, mergers and acquisitions, and strategic choices mean that most enterprises now have the need to manage data across multiple locations, while each of the major cloud providers and data and analytics vendors has a portfolio of offerings that may or may not be available in any given location. As such, the ability to manage and process data across multiple clouds and data centers is a growing concern for large and small enterprises alike. Almost one-half (49%) of respondents to Ventana Research’s Analytics and Data Benchmark Research study are using cloud computing for analytics and data, of which 42% are currently using more than one cloud provider.
Topics: Analytics, Cloud Computing, Data Governance, Data Integration, Data, Digital Technology, data lakes, AI and Machine Learning, data operations, data platforms
How does your organization define and display its metrics? I believe many organizations are not defining and displaying metrics in a way that benefits them most. If an organization goes through the trouble of measuring and reporting on a metric, the analysis ought to include all the information needed to evaluate that metric effectively. A number, by itself, does not provide any indication of whether the result is good or bad. Too often, the reader is expected to understand the difference, but why leave this evaluation to chance? Why not be more explicit about what results are expected?
Topics: Analytics, Business Intelligence, Internet of Things, Data, Digital Technology, AI and Machine Learning, Streaming Analytics
When NICE acquired inContact in 2016, it began a transformation that saw it broaden its product offering and positioned itself to play a larger role in the contact center and customer experience industries. It was a prescient move, creating a firm that could supply end-to-end contact center functionality in the cloud. And it anticipated today’s market dynamic, in which NICE and its competitors are racing to define (and capitalize on) the post-contact center future.
Topics: Customer Experience, Voice of the Customer, Business Continuity, Analytics, Contact Center, Data, Digital transformation, AI and Machine Learning, agent management, Digital Business, Experience Management, Customer Experience Management, Field Service, customer service and support
Our research shows that nearly all financial service organizations (97%) consider it important to accelerate the flow of information and improve responsiveness. Even just a few years ago, capturing and evaluating this information quickly was much more challenging, but with the advent of streaming data technologies that capture and process large volumes of data in real time, financial service organizations can quickly turn events into valuable business outcomes in the form of new products and services or revenue.
Topics: Analytics, Internet of Things, Data, Digital Technology, Streaming Analytics
In part one of this Analyst Perspective on the use of artificial intelligence within contact center applications, we focused on the evolution — and resulting benefits — of tools embedded with AI, including ease-of-use for non-data-scientists.
Topics: Customer Experience, Voice of the Customer, Analytics, AI and Machine Learning, agent management, Customer Experience Management, Field Service, customer service and support
Databricks is a data engineering and analytics cloud platform built on top of Apache Spark that processes and transforms huge volumes of data and offers data exploration capabilities through machine learning models. It can enable data engineers, data scientists, analysts and other workers to process big data and unify analytics through a single interface. The platform supports streaming data, SQL queries, graph processing and machine learning. It also offers a collaborative user interface — workspace — where workers can create data pipelines in multiple languages — including Python, R, Scala, and SQL — and train and prototype machine learning models.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Information Management, Data, data lakes, AI and Machine Learning
The work environment today demands that your organization advances the efficiency to execute business processes for continuous operations to have a positive impact on business performance. The capability to be responsive to any range of minor to disruptive business events is required to support business continuity and level of organizational readiness to meet the needs of digital business. Ventana Research asserts that in 2025, one-quarter of organizations will remain digitally ineffective in achieving the business priorities for customer-, product- and people-related processes. It is essential to eliminate bottlenecks and become an organization that places action and decision-making at is center to optimize the execution of business processes.
Topics: Customer Experience, Voice of the Customer, embedded analytics, Analytics, Business Intelligence, Cloud Computing, Contact Center, Data, Digital Technology, Operations & Supply Chain, Enterprise Resource Planning, Digital transformation, natural language processing, AI and Machine Learning, continuous supply chain, agent management, Digital Business, Experience Management, Field Service, Process Mining, Streaming Analytics
When artificial intelligence emerged from the labs and vendors started offering it as a component of their software, many contact-center buyers shied away from it. From their point of view, AI and machine learning tools were new, expensive, relatively untested and had an uncertain use case. This stance was understandable, as contact center professionals are traditionally expected to be risk-averse when deploying technology into their operations. Contact centers are, by design, supposed to be hardened, mission-critical sites of high reliability. There has historically been a bias towards avoiding new technology, deploying only when it has been thoroughly vetted across the industry.
Topics: Customer Experience, Voice of the Customer, Analytics, Contact Center, AI and Machine Learning, agent management, Customer Experience Management, Field Service, customer service and support
Access to external data can provide a competitive advantage. Our research shows that more than three-quarters (77%) of participants consider external data to be an important part of their machine learning (ML) efforts. The most important external data source identified is social media, followed by demographic data from data brokers. Organizations also identified government data, market data, environmental data and location data as important external data sources. External data is not just part of ML analyses though. Our research shows that external data sources are also a routine part of data preparation processes, with 80% of organizations incorporating one or more external data sources. And a similar proportion of participants in our research (84%) include external data in their data lakes.
Topics: Analytics, Business Intelligence, Internet of Things, Data, Digital Technology, Lease Management, AI and Machine Learning, Streaming Data, Streaming Analytics
The technology industry throws around a lot of similar terms with different meanings as well as entirely different terms with similar meanings. In this post, I don’t want to debate the meanings and origins of different terms; rather, I’d like to highlight a technology weapon that you should have in your data management arsenal. We currently refer to this technology as data virtualization. Other similar terms you may have heard include data fabric, data mesh and [data] federation. I’ll briefly discuss these terms and how I see them being used, but ultimately, I’d like to share with you some research that shows why data virtualization can be valuable, regardless of what you call it.
Topics: Analytics, Data Governance, Data Integration, Data, Digital Technology, data lakes
Alteryx is a data analytics software company that offers data preparation and analytics tools to simplify and automate data wrangling, data cleaning and modeling processes, enabling line-of-business personnel to quickly access, manipulate, analyze and output data. The platform features tools to run a variety of analytic functions such as diagnostic, predictive, prescriptive and geospatial analytics in a unified platform, and can connect to various data warehouses, cloud applications, spreadsheets and other sources.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Preparation, Data, AI and Machine Learning
Data governance is a hot topic these days. In fact, we are conducting benchmark research on the subject here. With increasing regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), organizations face more external oversight of their data governance practices. The risk of significant fines associated with these and other regulations, coupled with organizations’ internal compliance requirements, has brought more attention to data governance practices. We anticipate through 2023, three-quarters of Chief Data Officers’ primary concerns will be governing the privacy and security of their organization’s data.
Topics: Analytics, Data Governance, Data, Digital Technology
Collibra is a data governance software company that offers tools for metadata management and data cataloging. The software enables organizations to find data quickly, identify its source and assure its integrity. Line-of-business workers can use it to create, review and update the organization's policies on different data assets. Collibra’s software uses a microservice architecture and open application programming interfaces to connect to various data ecosystems. Its data intelligence cloud platform can automatically classify data from various sources such as online transaction processing databases, master repositories and Excel files without moving the data, so the information assets stay protected.
Topics: Analytics, Business Intelligence, Data Governance, Data Preparation, Information Management, Data, data lakes, AI and Machine Learning
Sisu Data is an analytics platform for structured data that uses machine learning and statistical analysis to automatically monitor changes in data sets and surface explanations. It can prioritize facts based on their impact and provide a detailed, interpretable context to refine and support conclusions. The product features fact boards, annotations and the ability to share facts and analysis across teams. Data teams and analysts start by creating common definitions of key performance indicators, which Sisu then utilizes to automatically test thousands of hypotheses to identify differences between groups.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data, AI and Machine Learning
Subscription management and billing services help organizations offer unique benefits and enhance delivery to customers. By making services more personalized, organizations can acquire – and retain – more customers.
Topics: Sales, Office of Finance, Continuous Planning, embedded analytics, Analytics, Business Intelligence, Business Planning, Product Information Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, ERP and Continuous Accounting, natural language processing, AI and Machine Learning, revenue and lease accounting, continuous supply chain, Subscription Management, partner management, digital finance, Process Mining, Streaming Analytics, supplier relationship management
Customer support operations increasingly rely on automation and complex workflow processes to reduce costs and improve experiences. Automation also allows organizations to make their service processes richer, incorporating information and staff from back offices, for example, or embedding conversational tools into contact center processes.
Topics: Customer Experience, embedded analytics, Analytics, Contact Center, natural language processing, AI and Machine Learning, agent management, Customer Experience Management, Field Service, Process Mining, Streaming Analytics, customer service and support
Rapidminer is a visual enterprise data science platform that includes data extraction, data mining, deep learning, artificial intelligence and machine learning (AI/ML) and predictive analytics. It can support AI/ML processes with data preparation, model validation, results visualization and model optimization. Rapidminer Studio is its visual workflow designer for the creation of predictive models. It offers more than 1,500 algorithms and functions in their library, along with templates, for common use cases including customer churn, predictive maintenance and fraud detection. It has a drag and drop visual interface and can connect to databases, enterprise data warehouses, data lakes, cloud storage, business applications and social media. The platform also supports push-down processing for data prep and ETL inside databases to minimize data movement and optimize performance.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Preparation, Data, data lakes, AI and Machine Learning
Confluent Platform is a streaming platform built by the original creators of Apache Kafka. It enables organizations to organize and manage streaming data from various sources. Confluent launched its IPO in June this year and raised $828 million to further expand its business. Confluent Platform was brought to several public cloud vendor marketplaces last year as Confluent Cloud. The offering is currently available in Azure, AWS, and GCP marketplaces. Furthermore, the company strengthened its partnership with Microsoft at the beginning of this year, establishing Confluent Cloud as a fully managed Apache Kafka service directly available on Microsoft Azure. Azure customers can access the extensive library of pre-built connectors, a unified billing model with options to use Azure committed spend on Confluent Cloud, and deeper integrations with Azure services.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data, AI and Machine Learning
The annual Ventana Research Digital Innovation Awards showcase advances in the productivity and potential of business applications, as well as technology that contributes significantly to the improved processes and performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations to advance business and IT.
Topics: Customer Experience, Analytics, Internet of Things, Digital Technology, blockchain, natural language processing, Awards, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing, extended reality
The annual Ventana Research Digital Innovation Awards showcase advances in the productivity and potential of business applications, as well as technology that contributes significantly to the improved processes and performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations to advance business and IT.
Topics: Continuous Planning, Analytics, Product Information Management, Price and Revenue Management, Digital Technology, Operations & Supply Chain, Enterprise Resource Planning, Conversational Computing, AI and Machine Learning, collaborative computing, continuous supply chain, work experience management
The annual Ventana Research Digital Innovation Awards showcase advances in the productivity and potential of business applications, as well as technology that contributes significantly to the improved processes and performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations to advance business and IT.
Topics: Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, Digital Technology, blockchain, data lakes, AI and Machine Learning
Dialpad provides contact center and business phone services, a market that is in transition due to a convergence of technologies and business conditions.
Topics: Customer Experience, Voice of the Customer, Analytics, Collaboration, Contact Center, natural language processing, AI and Machine Learning, agent management
The annual Ventana Research Digital Innovation Awards showcase advances in the productivity and potential of business applications, as well as technology that contributes significantly to the improved processes and performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations to advance business and IT.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, Digital Technology, natural language processing, AI and Machine Learning
The annual Ventana Research Digital Innovation Awards showcase advances in the productivity and potential of business applications, as well as technology that contributes significantly to the improved processes and performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations to advance business and IT.
Topics: Sales, Analytics, Product Information Management, Digital Commerce, AI and Machine Learning
The annual Ventana Research Digital Innovation Awards showcase advances in the productivity and potential of business applications, as well as technology that contributes significantly to the improved processes and performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations to advance business and IT.
Topics: Customer Experience, Human Capital Management, Marketing, Office of Finance, Voice of the Customer, Continuous Planning, embedded analytics, Learning Management, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Internet of Things, Business Planning, Contact Center, Data, Product Information Management, Sales Performance Management, Workforce Management, Financial Performance Management, Price and Revenue Management, Digital Technology, Digital Marketing, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, ERP and Continuous Accounting, Revenue, blockchain, natural language processing, data lakes, Total Compensation Management, robotic finance, Predictive Planning, employee experience, candidate engagement, Conversational Computing, Continuous Payroll, AI and Machine Learning, collaborative computing, mobile computing, continuous supply chain, Subscription Management, agent management, extended reality, intelligent marketing, sales enablement, work experience management, lease and tax accounting, robotic automation
I am happy to share insights gleaned from our latest Value Index research, an assessment of how well vendors’ offerings meet buyers’ requirements. The 2021 Ventana Research Value Index: Collaborative Analytics and Data is the distillation of a year of market and product research by Ventana Research. See our prior post for a description of our methodology and included vendors.
Topics: business intelligence, Analytics, Collaboration, Data Preparation, Information Management, Data, Digital Technology, natural language processing, mobile computing
As mentioned in my Analyst Perspective, Revenue Performance Management: Leadership and Operations for Optimal Outcomes, there is continuing pressure on sales leaders to deliver against sales targets in increasingly competitive markets. Among the various levers that sales leadership can use to support these efforts, are applications and processes that best position sales teams to achieve targets, such as planning and allocating territories, establishing quotas and devising incentive compensation plans supportive of organizational revenue goals. Once in place, continuous monitoring of lead-to-opportunity progress and pipeline health can aid in identifying areas for improvement as well as solidifying sales forecasts to better indicate gap-to-target issues and necessary adjustments to territories and compensation incentives.
Topics: Sales, Analytics, Sales Performance Management, Price and Revenue Management, AI and Machine Learning, sales enablement
I am happy to share insights gleaned from our latest Value Index research, an assessment of how well vendors’ offerings meet buyers’ requirements. The 2021 Ventana Research Value Index: Embedded Analytics and Data is the distillation of a year of market and product research by Ventana Research. See our prior post for a description of our methodology and included vendors.
Topics: Analytics, Business Intelligence, Data Preparation, Information Management, Data, natural language processing
Customer Service & Support (CSS) is a software segment that provides tools for tracking and resolving customer problems, primarily through contact centers. The segment has been mature for decades but today is reinvigorated by a new emphasis on workflows and automation. Vendors, like ServiceNow, have been innovative in developing new technologies for managing self-service and field service, and providing agents with contextually relevant information during interactions. The new technologies brought to bear on this include artificial intelligence (AI) for knowledge search and delivery; agent assist and guidance tools; and SMS-centric customer messaging.
Topics: Customer Experience, Voice of the Customer, Analytics, Contact Center, Product Information Management, Digital Commerce, AI and Machine Learning, Subscription Management, agent management
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: Mobile Analytics and Data 2021 is the distillation of a year of market and product research by Ventana Research. See our prior post for a description of our methodology and which vendors are included.
Topics: business intelligence, Analytics, Collaboration, Data Governance, Information Management, Data, Digital Technology, natural language processing, mobile computing
We are happy to share some insights about Amazon QuickSight drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about Google Looker drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about ThoughtSpot drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about TIBCO Spotfire drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, AI and Machine Learning
We are happy to share some insights about Sisense drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about Infor Birst drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Preparation, Data, Information Management (IM), natural language processing, AI and Machine Learning
We are happy to share some insights about Microsoft Power BI drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about Tableau drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about SAS drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
Teradata introduced some enhancements to its Vantage platform last year in which they expanded its analytics functions and language support, and strengthened tools to improve collaboration between data scientists, business analysts, data engineers and business personnel. Some of the key enhancements included expanding the native support for R and Python, extending the ability to execute a wide range of open-source analytics algorithms, and automatic generation of SQL from R and Python code. These updates are included to reduce data silos, enabling a wide range of data and analytics personas to collaboratively run complex analytics in a self-service manner.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Preparation, Information Management, Data, AI and Machine Learning
We are happy to share some insights about SAP drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Data, natural language processing, AI and Machine Learning
There has been a lot of market activity around vendors offering sales-forecasting products (or functionality to address sales forecasting) as part of a wider technology offering for sales and revenue management. As I have discussed in my Analyst Perspective: The Art and Science of Sales from the Inside Out, the pandemic accelerated the prior trends that are now forcing sales leaders and sales teams to reexamine traditional notions of how B2B sales are conducted. In addition, with the rise of the subscription business model and digital e-commerce, a more holistic approach to identify where revenue is coming from and how to manage and optimize a predictable revenue stream is becoming a pressing need. I cover the basic premise of this management of revenue streams in my Analyst Perspective: Revenue Management: The Opportunity for Innovation and Optimization.
Topics: Sales, Office of Finance, Analytics, Business Planning, Sales Performance Management, Price and Revenue Management, AI and Machine Learning
We are happy to share some insights about Board International drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about Yellowfin drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
As laid out in my recent Analyst Perspective, Revenue Management: The Opportunity for Innovation and Optimization, revenue management is a new way look at generating and managing the top line. It unifies multiple sources: the traditional focus on new customers to existing customers as well as all types of revenue from new, additional channels. This could include customer retention, upsell and cross sell, in addition to other selling channels such as through partners or digital sales channels like e-commerce and subscriptions.
Topics: Sales, Analytics, Sales Performance Management (SPM), Price and Revenue Management, Digital Commerce, AI and Machine Learning, Subscription Management
We are happy to share some insights about Domo drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about Oracle Analytics Cloud drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, AI and Machine Learning
We are happy to share some insights about Qlik drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Data, Information Management (IM), natural language processing, AI and Machine Learning
We are happy to share some insights about Information Builders’ WebFOCUS Business Intelligence and Analytics Platform drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about MicroStrategy drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about IBM drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Information Management, natural language processing, AI and Machine Learning
Customer service and support (CSS) is a term with two meanings. Most generally, it refers to the functions of a contact center in handling post-sales customer inquiries that require some effort or action on the part of the business. More specifically, it refers to the elements of the software stack that facilitate those operations, primarily case tracking and trouble ticketing.
Topics: Customer Experience, Analytics, Contact Center, AI and Machine Learning, agent management
Alation recently announced the release of its 2021.1 version, introducing new data governance capabilities, enhancements in search and discovery through data domains, and extended connector and query coverage for data sources. Alation’s new federated authentication enables users to query cloud services such as Amazon Web Services, Snowflake, Tableau and more, using a single sign-on. The release also includes a Search application programming interface that allows for the integration of Alation Search with third-party tools. And, with the addition of the Open Connector Framework software development kit in the 2021.1 update, Alation enables organizations to create connectors for data sources not already supported by Alation.
Topics: Analytics, Business Intelligence, Collaboration, Data Preparation, Data, Information Management (IM), AI and Machine Learning
Unit4’s Financial Planning and Analysis (formerly Prevero) is a planning and budgeting application designed for the requirements of midsize corporations and the public sector. These organizations are challenged in buying software because they have almost all the requirements of larger enterprises but have a smaller budget and limited technical resources.
Topics: Office of Finance, embedded analytics, Analytics, Business Intelligence, Business Planning, Financial Performance Management, Price and Revenue Management, Digital Technology, ERP and Continuous Accounting, Predictive Planning, AI and Machine Learning, collaborative computing
Observed both here and elsewhere, average sales quota attainments appear to be in an exorable decline. As I discussed in my recent Analyst Perspective, "The Art and Science of Sales from the 'Inside Out'," vendors of sales technology have reacted to this by adding a slew of new functionality including the potential for artificial intelligence (AI) to be a game changer for sales. One can argue that this use of AI is still relatively immature having been generally available only since 2014, but that is still over five years of market availability.
Topics: Sales, Human Capital Management, Analytics, Business Intelligence, Sales Performance Management, candidate engagement, AI and Machine Learning, sales enablement
Machine learning is valuable for organizations, but it can be hard to deploy. Our Machine Learning Dynamic Insights research identifies that not having enough skilled resources and difficulty building and maintaining ML systems are pressing challenges organizations face in applying ML. Traditional ML model development is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models. And as the number of ML models grow, their management becomes difficult. By bringing automation to ML, organizations can reduce the time it takes to create production-ready ML models. AutoML can also enable organizations to make data science initiatives more accessible across the organization.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Data, AI and Machine Learning
As I have discussed in my Analyst Perspective, The Art of Sales, from the Inside Out, the challenges facing direct sales leaders are not going away. Declining quota attainment, lack of visibility into deal health and difficulty in forecasting quarterly sales remain a challenge for sales leaders, resulting in a continuing reduction in duration of tenure.
Topics: Sales, Analytics, Sales Performance Management, Price and Revenue Management, AI and Machine Learning, sales enablement
Voice of the Customer (VoC) is a catch-all term that refers to the collection of customer feedback in various formats. Sometimes this feedback is in the form of quick surveys or reactions to questions like, "Did I resolve your issue today?" or "Would you recommend our service to a friend?" Alternatively, it can be derived from less specific but more numerous data signals that span multiple interactions or across a customer base. Most businesses make an effort to capture some customer feedback.
Topics: Customer Experience, Voice of the Customer, Analytics, Contact Center, AI and Machine Learning, agent management
The amount of data flowing into organizations is growing exponentially, creating a need to process more data more quickly than ever before. Our Data Preparation Benchmark Research shows that accessing and preparing data continues to be the most time-consuming part of making data available for analysis. This can potentially slow down the organizational functions which depend on the analysis results. Trying to get ahead of the backlog with incremental improvements to existing approaches and traditional technologies alone can be frustrating.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Data, Information Management (IM), data lakes
Organizations are becoming more and more data-driven and are looking for ways to accelerate the usage of artificial intelligence and machine learning (AI/ML). Developing and deploying AI/ML models can be complicated in many ways, often involving different tools and services to manage these solutions from end to end. Accessing and preparing data is the most common challenge organizations face in this process, and consequently, AI/ML vendors typically incorporate tools to address this part of the process. But there are many other steps in the process as well, such as coordinating the handoff between data scientists and IT or software engineers for deployment to production. This can potentially slow down the entire data-to-insights process. End-to-end platforms for AI offer the promise of simplifying these processes, allowing teams that work with data to improve organizational results.
Topics: business intelligence, Analytics, Collaboration, Data Governance, Data Preparation, Data, AI and Machine Learning
IBM Planning Analytics, formerly known as TM1, is a comprehensive planning and analytics application designed to integrate and streamline an organization’s planning processes. It can support multiple planning use cases on a single platform, including financial, headcount, sales and demand planning. The software automates enterprise-wide data collection to make it repeatable and scalable across multiple users and departments. It supports sophisticated driver-based modeling that enables rapid what-if or scenario-based planning, while its built-in analytics provide deep business intelligence capabilities. This enables senior executives and managers to work interactively to immediately assess their current position and consider the impact of various options to address opportunities and issues rather than laboring through a lengthy process.
Topics: Office of Finance, embedded analytics, Analytics, Business Intelligence, Collaboration, Business Planning, ERP and Continuous Accounting, Predictive Planning, AI and Machine Learning
Process-mining software isn’t exactly new, but it’s also not widely known in the software technology market. The discipline has been around for at least a decade, but is generating more interest these days with both specialist vendors and major enterprise software vendors offering process-mining products and services. We assert that through 2022, 1 in 4 organizations will look to streamline their operations by exploring process mining.
Topics: business intelligence, Analytics, Digital Technology, AI and Machine Learning
Digital commerce affects almost everyone’s lives. It is hard to remember a time when one could not sign on to a website like Amazon, order a product, pay for it and have it delivered to your front door within days, not weeks. Although catalogues have been around for a century or so, the digital-commerce revolution has changed the way we think about shopping for many of our everyday and special occasion products. Extend this to digital services, such as streaming videos or online games, and there is barely a sector that has not been touched by digital commerce. And, for organizations, it is an essential component of their revenue-management efforts that enables the digital transformation and monetization of goods and services.
Topics: Sales, Customer Experience, Analytics, Business Intelligence, Product Information Management, Price and Revenue Management, Digital Commerce, AI and Machine Learning
Organizations are accelerating their digital transformation and looking for innovative ways to engage with customers in this new digital era of data management. The goal is to understand how to manage the growing volume of data in real time, across all sources and platforms, and use it to inform, streamline and transform internal operations. Over the years, the adoption of cloud computing has gained momentum with more and more organizations trying to make use of applications, data, analytics and self-service business intelligence (BI) tools running on top of cloud-computing infrastructure in order to improve efficiency. However, cloud adoption means living with a mix of on-premises and multiple cloud-based systems in a hybrid computing environment. The challenge is to ensure that processes, applications and data can still be integrated across cloud and on-premises systems. Our research shows that organizations still have a significant requirement for on-premises data management but also have a growing requirement for cloud-based capabilities.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Information Management, Internet of Things, Data, natural language processing, AI and Machine Learning
Having just completed the 2021 Ventana Research Value Index for Analytics and Data, I want to share some of my observations about how the market has advanced since our assessment two years ago. The analytics software market is quite mature and products from any of the vendors we assess can be used to effectively deliver information to help your organization improve its operations. However, it’s also interesting to see how much the market continues to advance and how much investment vendors continue to make.
Topics: Big Data, Key Performance Indictors, embedded analytics, exadata, Analytics, Business Collaboration, Business Intelligence, Collaboration, Data Preparation, Digital Technology, natural language processing, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing
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: Analytics and Data 2021 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 analytics and data vendors supporting the spectrum of business intelligence. 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.
The pandemic accelerated several trends in the contact-center industry that were already underway, chiefly: moving infrastructure and software applications to the cloud, and rethinking the process of managing agents. One byproduct of these trends is a renewed look at the similarities between business-phone systems (also known as unified communications, or UC) and contact center systems (CC).
Topics: Customer Experience, Analytics, Collaboration, Contact Center, AI and Machine Learning, agent management
There is no doubt that the pandemic has accelerated the existing need for new technology that can help sales professionals do their jobs well in this quickly evolving market. In addition, market trends are driving the need for functionality that is aimed at the front-line sales professional and the manager, highlighting the demand for tools that can help arrest the decline in quota attainment, as well as helping salespeople supplement their traditional focus on sales quotas with activities such as prospecting.
Topics: Sales, embedded analytics, Analytics, Internet of Things, Sales Performance Management, natural language processing, AI and Machine Learning, sales enablement
Organizations are increasingly using data as a strategic asset, which makes data services critical. Huge volumes of data need to be stored, managed, discovered and analyzed. Cloud computing and storage approaches provide enterprises with various capabilities to store and process their data in third-party data centers. The advent of data platforms previously discussed here are essential for organizations to effectively manage their data assets.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Lake, Data Preparation, Data, AI and Machine Learning, Microsoft Azure
With modern enterprises adopting and expanding their digital business with subscription and usage business, organizations need to think beyond the primacy of sales as their source of business success. A sustained customer experience is key, representing an organizational and cultural shift from the traditional emphasis on new sales to an equally important focus on customer retention and upsell/cross-sell opportunities. This mindset is also important to organizations involved in digital commerce where the cost of customer acquisition and narrow margins means retaining customers for repeat purchases, which is vital to a sustainable business.
Topics: Sales, Marketing, Analytics, Product Information Management, Sales Performance Management, Financial Performance Management, Price and Revenue Management, Digital Commerce, Subscription Management
Verint has recently separated into two companies, one of which is a dedicated provider of software focused on customer engagement and will keep the Verint name. (The other is called Cognyte and focused on security analytics.)
Topics: Customer Experience, Voice of the Customer, Analytics, Contact Center, agent management
The current pandemic has disrupted many of the traditional sales methods used by field-sales organizations to engage, and sell to, buyers. In an effort to provide help, many vendors have recently announced new features that focus less on the management of sales organizations and more on tools to help salespeople sell. This has been coupled with a renewed interest in using data to help with the science, alongside the art, of selling, as referenced in my AP: The Art and Science of Sales from the “Inside Out". Oracle has called this new emphasis “Responsive Selling,” with an aim to harness data and machine learning (ML) to aid sellers in this new, challenging environment.
Topics: Sales, Analytics, Data, Product Information Management, Sales Performance Management (SPM), Digital Technology, AI and Machine Learning, sales enablement, Sales Engagement
Ventana Research recently announced its 2021 research agenda for the Office of Sales, continuing the guidance we’ve offered for nearly two decades to help organizations realize optimal value from applying technology to improve business outcomes. Chief sales and revenue officers are experts in their respective fields but may not have the guidance needed to employ technology effectively. As we look to 2021, we are focusing on the entire selling and buying journey and the applications that simplify interactions throughout the customer experience.
Topics: Sales, Analytics, Financial Performance, Internet of Things, Data, Sales Performance Management, Digital Technology, Digital Commerce, AI and Machine Learning, mobile computing, Subscription Management, extended reality, intelligent sales, partner management, Office of Sales, Machine Conversational Computing, Sales Engagement
I’m proud to share Ventana Research’s 2021 market agenda for digital technology. Our focus in this agenda is to deliver expertise to help organizations prioritize technology investments that increase workforce effectiveness and organizational agility, ensuring ongoing operation during any type of disruption.
Topics: Big Data, Analytics, Cloud Computing, Internet of Things, Digital Technology, Robotic Process Automation, blockchain, Conversational Computing, AI and Machine Learning, mobile computing, extended reality
Ventana Research recently announced its 2021 market agenda in the expertise area of Customer Experience. Most organizations have some degree of focus on managing how they interact with their customers, but it is often a disjointed and constrained process. Developing an effective customer experience has become an investment priority in recent years as organizations increasingly recognize the importance of good experiences to profitability, customer longevity and advocacy on behalf of brands.
Topics: Sales, Customer Experience, Marketing, Voice of the Customer, Analytics, Customer Service, Contact Center, Workforce Management, Digital Marketing, Digital Commerce, AI and Machine Learning, agent management, Customer Experience Management, Field Service
Ventana Research recently announced its 2021 market agenda for Analytics, continuing the guidance we’ve offered for nearly two decades to help organizations derive optimal value from technology investments to improve business outcomes.
Topics: embedded analytics, Analytics, Business Intelligence, natural language processing, AI and Machine Learning, Process Mining, Streaming Analytics
Ventana Research has announced its market agenda for 2021, continuing the tradition of transparency in our efforts to educate and guide the technology market but also our independence as we do not share our market agenda or analyst perspectives with any external party. Each year, we proudly formulate our market agenda that is not biased by clients or the technology industry, focusing on education rather than the prospect of consulting or software revenue. We review and refine our plan throughout the year to ensure we offer the expertise and insights organizations rely on to better understand – and navigate – the direction of the technology industry.
Topics: Customer Experience, Human Capital Management, Marketing, Office of Finance, Analytics, Data, Digital Technology, Operations & Supply Chain, Office of Sales, Digital Business
The industry is making huge strides with artificial intelligence (AI) and machine learning (ML). There is more data available to analyze. Analytics vendors have made it easier to build and deploy models, and AI/ML is being embedded into many types of applications. Organizations are realizing the value that AI/ML provides and there are now millions of professionals with AI or ML in their title or job description. AI/ML is even being used to make many aspects of itself easier. Organizations that want to build and deploy their own AI/ML models need to be realistic about the capabilities that are available today. As a practical matter, organizations should anticipate that a robust AI/ML deployment in the current environment requires a set of specialized skills and operational processes, including data operations (dataops) and ML operations (MLops). Collaboration across these disciplines and processes is also required.
Topics: Sales, Customer Experience, Marketing, Analytics, Business Intelligence, Data Preparation, Digital Technology, AI and Machine Learning
Data is becoming more valuable and more important to organizations. At the same time, organizations have become more disciplined about the data on which they rely to ensure it is robust, accurate and governed properly. Without data integrity, organizations cannot trust the information produced by their data processes, and will be discouraged from using that data, resulting in inefficiencies and reduced effectiveness.
Topics: business intelligence, Analytics, Data Governance, Data Preparation, Information Management, Data, data lakes
Organizations are dealing with exponentially increasing data that ranges broadly from customer-generated information, financial transactions, edge-generated data and even operational IT server logs. A combination of complex data lake and data warehouse capabilities are required to leverage this data. Our research shows that nearly three-quarters of organizations deploy both data lakes and data warehouses but are using a variety of approaches which can be cumbersome. A single platform that can provide both capabilities will help address organizations’ requirements.
Topics: PROS Pricing, embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, data lakes, AI and Machine Learning
Businesses are transforming their organizations, building a data culture and deploying sophisticated analytics more broadly than ever. However, the process of using data and analytics is not always easy. The necessary tools are often separate, but our research shows organizations prefer an integrated environment. In our Data Preparation Benchmark Research, we found that 41% of participants use Analytics and Business Intelligence tools for data preparation.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Preparation, Information Management, Internet of Things, Data, Digital Technology, natural language processing, Conversational Computing, AI and Machine Learning
Traditional on-premises data processing solutions have led to a hugely complex and expensive set of data silos where IT spends more time managing the infrastructure than extracting value from the data. Big data architectures have attempted to solve the problem with large pools of cost-effective storage, but in doing so have often created on-premises management and administration challenges. These challenges of acquiring, installing and maintaining large clusters of computing resources gave rise to cloud-based implementations as an alternative. Public cloud is becoming the new center for data as organizations migrate from static on-premises IT architectures to global, dynamic and multi-cloud architectures.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Data, data lakes, AI and Machine Learning
Organizations are always looking to improve their ability to use data and AI to gain meaningful and actionable insights into their operations, services and customer needs. But unlocking value from data requires multiple analytics workloads, data science tools and machine learning algorithms to run against the same diverse data sets. Organizations still struggle with limited data visibility and insufficient insights, which are often caused by a multitude of reasons such as analytic workloads running independently, data spread across multiple data centers, data governance, etc. In our ongoing benchmark research project, we are researching the ways in which organizations work with big data and the challenges they face.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Data, Information Management (IM), data lakes, AI and Machine Learning
Teradata is not a name that is commonly associated with the customer experience marketplace, but that is likely to change as customer experience (CX) practitioners wrestle with the problems created by the multiple streams of data thrown off by the many applications and customer touchpoints they have to manage. Teradata’s Vantage CX is a tool for ingesting and managing customer information at great scale, combining the functions of a modern CDP with the analytics that makes customer data actionable.
Topics: Customer Experience, Marketing, Analytics, Business Intelligence, Contact Center, Data, Digital Marketing, Digital Commerce, AI and Machine Learning, intelligent marketing
Although historically there has been a hard divide between what are colloquially called “Inside and Field Sales,” changes over the last 10 years have narrowed the distinction. The pandemic has only accelerated the path to unifying sales activities commonly performed to engage buyers and customers. Characterized by a very disciplined and controlled endeavor, inside sales teams have been heavier users of technology. This has enabled more productive engagement including emails and calls, as well as provided techniques such as gamification to set competitive internal dynamics that help motivate sales professionals.
Topics: Sales, embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, Sales Performance Management (SPM), natural language processing, AI and Machine Learning, intelligent sales, sales enablement
The annual Ventana Research Digital Innovation Awards showcases advances in the productivity and potential of business applications, as well as technology that contributes significantly to improved efficiency and productivity in the processes and the performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations that advance business and IT.
Topics: Analytics, Collaboration, Data Governance, Data Lake, Data Preparation, IOT, Data, Information Management (IM), Digital Technology, blockchain, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing, extended reality
Determining and providing the appropriate compensation for each person — whether it involves base pay, variable pay such as commissions or bonuses or longer-term incentives in the form of cash or equity or other rewards — is critical to being able to attract and retain productive members of the workforce, whether full- or part-time employees, contingent workers or contractors. The complexities of compensation often prove to be a core challenge for human resources departments as they strive to keep the organization productive, satisfied and motivated while ensuring equitable and defensible pay practices across the entire workforce. In today’s age where workers can get an online crowdsourced compensation benchmark for their role, managers and HR must be prepared to respond to comparison questions.
Topics: Analytics, AI and Machine Learning, collaborative computing, total rewards management
Every organization performing analytics with multiple employees needs to collaborate. They should be collaborating in the analytics process and in communicating the results of those analyses. As I continue my evaluation of analytics and data vendors, I have to admit some disappointment at the level of collaborative capabilities some analytics vendors provide. To be fair, the level of capabilities vary widely, but I expected collaborative capabilities to be more uniformly available as a standard feature in analytics technologies by now. I had anticipated that three-quarters of analytics vendors would include collaboration capabilities. More than half the vendors I have evaluated support some comments and discussion in their products, only a few have incorporated social recognition and wall posting as part of their collaborative capabilities. So, what impact does a lack of analytics collaboration have on organizations undergoing digital transformation?
Topics: business intelligence, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, Digital Technology, collaborative computing
The annual Ventana Research Digital Innovation Awards showcases advances in the productivity and potential of business applications, as well as technology that contributes significantly to improved efficiency and productivity in the processes and the performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations that advance business and IT.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Digital Technology, natural language processing, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing
This has been a dramatic year for contact centers. The underlying technology has been changing for some time, but that change is now accelerating because of the urgent operational shifts forced by the pandemic. When you can’t gather dozens or hundreds of people into a single, open-plan site, you must look at alternative models for staffing and interaction handling. You must also work harder to create positive customer experiences across multiple contact channels.
Topics: Customer Experience, Voice of the Customer, Analytics, Contact Center, Product Information Management, Digital Commerce, AI and Machine Learning, Subscription Management, agent management
The annual Ventana Research Digital Innovation Awards showcases advances in the productivity and potential of business applications, as well as technology that contributes significantly to improved efficiency and productivity in the processes and the performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations that advance business and IT.
Topics: Sales, Marketing, embedded analytics, Analytics, Sales Performance Management, Digital Technology, AI and Machine Learning, intelligent marketing, sales enablement
The annual Ventana Research Digital Innovation Awards showcases advances in the productivity and potential of business applications, as well as technology that contributes significantly to improved efficiency and productivity in the processes and performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations that advance business and IT.
Topics: Customer Experience, Human Capital Management, Marketing, Analytics, Internet of Things, Contact Center, Data, Digital Technology, Digital Commerce, Operations & Supply Chain, blockchain, employee experience, candidate engagement, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing, agent management, extended reality, business digital commerce, work experience management
Ventana Research has been evaluating analytics and business intelligence (BI) software for a long time—almost 20 years. Our methodology for these assessments is referred to as a Value Index. We use weightings derived from our benchmark research about how you, as buyers of these technologies, value and evaluate vendors. You can view our 2019 Value Index results here. I am in the process of completing the 2020 evaluation now.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, natural language processing, Conversational Computing, AI and Machine Learning, collaborative computing
The last decade has seen exponential growth amongst subscription-based business models. Pioneered in the B2C market with cloud-based SaaS offerings, the last decade has seen exponential growth in the share of the economy that is now subscription based. Increasingly, this modern business model is permeating throughout more traditional style industries and companies. But regardless of whether a company is natively subscription based, or is transitioning, maintaining this growth requires organizations to foster long-term relationships with customers and deliver products and services that get better over time.
Topics: Sales, Customer Experience, Office of Finance, Voice of the Customer, embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, Contact Center, Product Information Management, Price and Revenue Management, Digital Commerce, Enterprise Resource Planning, ERP and Continuous Accounting, natural language processing, robotic finance, AI and Machine Learning, revenue and lease accounting, Subscription Management, agent management, intelligent sales, sales enablement
An important recent development in software designed for the Office of Finance is the addition of what we’re calling a data aggregation device (DAD) for analytical applications. A DAD automates the collection of data from disparate sources using, for example, application programming interfaces (APIs) and robotic process automation (RPA). With a DAD, users of the analytical application have immediate access to a much broader data set; one that incorporates operational as well as financial data from internal and external sources. The larger data set enables a much more expansive set of analyses than has been feasible in the past because the process of acquiring the data is automated, and the data aggregation is handled in a controlled manner. This control means that data in the system is authoritative, accurate, consistent, complete and secure. The difference between a DAD and a finance data mart is that the former is prebuilt for the specific application, and therefore eliminates this source of implementation costs and offers faster time to value.
Topics: Office of Finance, Analytics, Business Intelligence, Data, Financial Performance Management, Price and Revenue Management, robotic finance, Predictive Planning, AI and Machine Learning
Subscription-based business models have seen exponential growth over the last decade. The growth of this recurring revenue business model, where a subscriber commits to repeatedly pay for a good or device for a fixed or indefinite timeline, has been caused by the shift from the one-time selling of physical products to selling digital services on a subscription basis. The first phase of this transformation was led by “digitally native” organizations, typically B2C, that have only ever offered services via subscription. Although a large market in its own right, it is still dwarfed by businesses selling physical products. But this market is also changing, as more and more traditional organizations transition some or all of their revenue to the subscription economy. Ventana Research asserts that through 2023, fewer than half of organizations will have the correct technology in place to support such a transition. This Analyst Perspective looks at some of the key implications of this transition and what it means for technology choices as companies move toward a subscription management approach to overseeing the subscribers and usage of their products and services.
Topics: Sales, Customer Experience, Office of Finance, Voice of the Customer, embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, Contact Center, Product Information Management, Price and Revenue Management, Digital Commerce, Enterprise Resource Planning, ERP and Continuous Accounting, natural language processing, robotic finance, AI and Machine Learning, revenue and lease accounting, Subscription Management, agent management, intelligent sales, sales enablement
I’m very excited to announce to my network as well as the ever-expanding Ventana Research community that I’m now directing Ventana Research’s Office of Sales practice. The focus is to guide and educate sales and business professionals on the selling applications and technology including digital commerce, price and revenue management, product information management, sales enablement, sales performance management and subscription management. While these are the main topics of our Office of Sales practice, my decades of experience in analytics, artificial intelligence (AI) and planning are part of what I bring to the firm to help advance the science of selling.
Topics: Sales, embedded analytics, Analytics, Business Intelligence, Collaboration, Data, Product Information Management, Sales Performance Management, Price and Revenue Management, Digital Technology, Work and Resource Management, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing, intelligent sales, sales enablement
One of the challenges of being a practically minded technology analyst is squaring the importance of “the next big thing” with the reality of what most organizations are doing. For decades it’s been the case that “the next big thing” in the world of information technology is easily several years ahead of where most organizations are in their use of technology. And before most organizations can realize the benefit of some whiz-bang technology, they frequently need to address a range of more mundane issues, such as data availability and accuracy, employee training and corporate culture, among other impediments. Sometimes, though, advanced technology works to uncomplicate things for organizations.
Topics: Human Capital Management, Marketing, Office of Finance, Analytics, Business Intelligence, Sales Performance Management, Financial Performance Management, Price and Revenue Management, Digital Marketing, Work and Resource Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, ERP and Continuous Accounting, robotic finance, Predictive Planning, AI and Machine Learning, revenue and lease accounting, Subscription Management, intelligent sales
Economic dynamics and market pressures during a black-swan event can wreak havoc on efforts to effectively manage revenue operations and pricing for business continuity. For many organizations, environmental changes disrupt the methods by which these essential business processes are managed can be disrupted, damaging the revenue streams that create profitability. The array of pricing strategies and related promotional tactics across channels for configure, price and quote (CPQ), digital commerce and subscription management can challenge the best of organizations. Leadership must examine the agility of pricing management to determine if they have ability to make and manage changes to determine the effectiveness of decisions. This requires visibility into revenue operations and selling channels, which in turn requires programs, processes and technology designed to meet the needs of what is called price and revenue management (PRM).
Topics: Sales, Customer Experience, Human Capital Management, Marketing, Analytics, Business Intelligence, Collaboration, Internet of Things, Data, Product Information Management, Sales Performance Management, Workforce Management, Workforce Planning, Price and Revenue Management, Total Compensation Management, Conversational Computing
Over the past several months, I have discussed a wide range of topics that organizations must consider and appropriately prioritize to maintain business continuity during periods of upheaval. But sometimes it’s important to take a step back and reflect on a critical and recurring theme: experiences. The array of experiences across the workforce and business processes both inside and outside of the organization are an essential part of an organization’s success. Leadership must give these experiences the attention they deserve, and this requires visibility into operations and the tools to measure effectiveness, especially during black-swan events. Fulfilling this objective requires the programs, processes and technology designed to meet the needs of what is called experience management (XM).
Topics: Sales, Customer Experience, Human Capital Management, Marketing, Office of Finance, Analytics, Business Intelligence, Collaboration, Internet of Things, Data, Sales Performance Management, Workforce Management, Workforce Planning, Operations & Supply Chain, Total Compensation Management, Conversational Computing
Business planning is an essential part of an organization’s focus on its future performance and overall potential because it ensures continuous operations, even in black-swan events. Planning across the entire organization needs to be a critical priority and leadership should give it the attention it deserves. In challenging times, a focus on execution tends to take hold — this is not unreasonable but in focusing on satisfying immediate customer and workforce needs and putting out fires, business leaders too often forget that forward-looking continuous planning is essential to achieving desired outcomes. Fulfilling this objective requires technology designed to meet these needs for every business process in the organization.
Topics: Sales, Human Capital Management, Office of Finance, Continuous Planning, Analytics, Business Intelligence, Collaboration, Internet of Things, Data, Sales Performance Management, Workforce Management, Financial Performance Management, Price and Revenue Management, Operations & Supply Chain, Enterprise Resource Planning, ERP and Continuous Accounting, Total Compensation Management, Predictive Planning, Conversational Computing
The workforce is an essential part of an organization’s overall business potential because it ensures continuous operations, even in black-swan events. The workforce is the core of the organization and should get the attention it deserves. In challenging times, a “customer-first” mentality tends to take hold — this is not unreasonable but in focusing on satisfying customers and opportunities, business leaders too often forget that the workforce experience is essential to achieving desired results. Fulfilling this objective requires technology designed to meet these human capital management (HCM) objectives. An organization’s agility and ability to invest adequate time and resources into the workforce experience is essential to an organization’s sustainability and operational effectiveness.
Topics: Sales, Customer Experience, Human Capital Management, Office of Finance, Voice of the Customer, Continuous Planning, Business Continuity, Analytics, Business Planning, Workforce Analytics, Workforce Management, Digital Technology, Operations & Supply Chain, Robotic Process Automation, employee experience, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing, agent management, People Analytics
Analytics and data provide visibility into an organization’s past, present and potential performance. However, not all organizations are using analytics that provide timely insights — insights that not just reflect what happen but direct a successful course for the future. Demand for personalized and relevant insight only intensifies in a black-swan event. To maintain business continuity in times of pressure, it is critical that organizations not waste any time or resources when using analytics and data to optimize operations and decision-making. Just having an analytics and data-first mentality and operating in the cloud is insufficient for success, as those are just part of an effective data and analytics effort. Organizations also should include data science and machine learning that can provide an excellent digital experience; unfortunately, this is no simple task.
Topics: business intelligence, embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, Data, Digital Technology, natural language processing, Conversational Computing, AI and Machine Learning
Marketing is inextricably linked to business success, and digital technology is essential to an organization’s overall marketing potential because it generates interest and brand awareness. In a black-swan event, the marketing department often is overwhelmed by short-term demands, so in these situations it’s of the essence that digital transformation gets the attention it deserves. In challenging times, a “putting-out-fires” mentality tends to take hold — this is not unreasonable but in focusing on satisfying the interest of the moment, business leaders too often forget that a consistent digital experience is essential to engaging consumers, the public and customers in a way that contributes to long-term success. Fulfilling this objective requires technology designed to deliver for marketing to meet this essential imperative. An organization’s agility and ability to invest adequate time and resources into marketing technology that enables a superior digital experience is essential for its sustainability and operational effectiveness.
Topics: Sales, Customer Experience, Marketing, Office of Finance, Voice of the Customer, Analytics, Data, Product Information Management, Digital Technology, Operations & Supply Chain, Conversational Computing
Contact centers play a substantial role in an organization’s success. The customer journey is engaged here, at each moment of interaction. Agents, whether human or machine-driven, are intrinsic to the customer experience and the value of the contact center. Customers are essential to an organizations’ overall business potential because they generate revenue. In a black-swan event, demand for customer service may spike or dip, so in these situations it’s of the essence that agents get the attention they deserve. In challenging times, a “customer-first” mentality tends to take hold — this is natural, but in focusing on satisfying customers, business leaders too often forget that the agent experience is essential to effective customer engagement. Fulfilling this objective requires contact center technology designed for this purpose. An organization’s agility and ability to invest adequate time and resources into agents is essential for its sustainability and contact center effectiveness.
Topics: Sales, Customer Experience, Marketing, Office of Finance, Voice of the Customer, Analytics, Data, Product Information Management, Digital Technology, Operations & Supply Chain, Conversational Computing
Subscriptions are the future of business. Subscribers are essential to an organization’s overall business potential because they generate recurring revenue. In a black-swan event, demand for a subscription may spike or dip, so in these situations it’s of the essence that subscriptions get the attention they deserve. In challenging times, a “subscriber-first” mentality tends to take hold — this is not unreasonable but in focusing on satisfying subscribers, business leaders too often forget that the subscription experience is essential to retaining them. Fulfilling this objective requires technology designed for subscriptions. An organization’s agility and ability to invest adequate time and resources into subscriptions is essential for its sustainability and operational effectiveness.
Topics: Sales, Customer Experience, Marketing, Office of Finance, Voice of the Customer, Analytics, Data, Product Information Management, Digital Technology, Operations & Supply Chain, Subscription Billing, Conversational Computing, Subscription Management, Subscriber Experience
Partners play a key role in the revenue and growth of every organization. Whether channel selling is in assistance to internal sales or independent, what happens in partnering has ramifications that are simply too important to underestimate. The imperative to maintain business continuity with channel partners becomes painfully clear in a global pandemic, and that imperative demands that organizations cultivate partner excellence and channel performance. This effort should start with partner leadership and operations, with the objective of building channel relationships that can survive the test of time. Effective sales channel partnerships are built on mutual trust and a shared belief in the market opportunity, and recruiting and managing partners must be supported by effective processes and technology. The health of these relationships and the resulting revenue from the channel hinges on an effective partner experience, and this requires technology investments that enable leaders to not just manage channel performance, but help inspire it every single day.
Topics: Sales, Customer Experience, Human Capital Management, Marketing, Office of Finance, Voice of the Customer, Analytics, Internet of Things, Data, Digital Technology, Operations & Supply Chain, Conversational Computing, partner management, Partner Experience
Products and services are the foundation of every organization, regardless of its industry or size. Products are essential to an organization’s overall business health because they generate revenue and engage buyers and customers. In a black-swan event, demand for a product may spike or dip, so in these situations it’s of the essence that products get the attention they deserve as they are marketed, sold, serviced and enhanced with innovations. In challenging times, a “customer-first” mentality tends to take hold — this is not unreasonable but in a rush to satisfy customers, business leaders too often forget that the product experience is essential to fulfilling on the customer experience and satisfying customers and buyers. An organization’s agility and ability to invest adequate time and resources into products is essential for its sustainability and operational effectiveness.
Topics: Sales, Customer Experience, Marketing, Office of Finance, Voice of the Customer, Analytics, Data, Product Information Management, Digital Technology, Operations & Supply Chain, Conversational Computing, product experience management
The workforce is the center of any organization, no matter if the workforce consists of employees, contractors or what we call gig workers. It stands to reason that a black-swan event has an immediate impact on a workforce and thus an organization’s overall business health. In challenging times, a “family-first” mentality tends to take hold — a reality that, far too often, business leaders and HR organizations underestimate. But organizational readiness is essential for sustainability and operational effectiveness.
Topics: Sales, Human Capital Management, Learning, Office of Finance, Voice of the Customer, Analytics, Digital Technology, Digital Marketing, Operations & Supply Chain, Workforce Optimization, AI and Machine Learning
Suppliers play a critical role in supporting the operations and processes of every organization. Whether direct or indirect, an organization’s relationship with its suppliers has ramifications that are perilous to underestimate. The imperative to maintain business continuity becomes painfully clear in a global pandemic, and that imperative demands that organizations cultivate excellent supplier relationships.
Topics: Sales, Customer Experience, Human Capital Management, Marketing, Office of Finance, Voice of the Customer, Analytics, Internet of Things, Data, Digital Technology, Operations & Supply Chain, Conversational Computing
In this analyst perspective, Dave Menninger takes a look at data lakes. He explains the term “data lake,” describes common use cases and shares his views on some of the latest market trends. He explores the relationship between data warehouses and data lakes and share some of Ventana Research’s findings on the subject. He also provides an assessment of the risks organizations face in working with data lakes and offers recommendations for maximizing the potential of data.
Topics: Big Data, Data Warehousing, Analytics, Business Analytics, Business Intelligence, Data Governance, Data Management, Data Preparation, data lakes
Supercharging the customer experience (CX) is more than just an opportunity. It’s essential for every organization that looks to optimize engagement at every moment of the customer journey. In times such as these, when business continuity is a top priority, organizations must address the customer experience, especially if it has not been a focal point of the executive team.
Topics: Sales, Customer Experience, Human Capital Management, Marketing, Office of Finance, Voice of the Customer, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Internet of Things, Contact Center, Data, Digital Technology, Digital Commerce, Operations & Supply Chain, Intelligent CX, Conversational Computing
The imperative to infuse digital technology into your organization is not new, but it’s more essential than ever that organizations embrace digital transformation and business continuity to improve processes. I have recently written about the need for digital innovation in business continuity, outlining the steps every organization needs to take. These steps involve a close examination of the digital technology they can apply effectively for business continuity during a pandemic, natural disaster, cyber event or geopolitical situation.
Topics: Customer Experience, Human Capital Management, Office of Finance, Analytics, Data, Digital Technology
Effectively managing data privacy and security is a high-stakes matter. When an organization doesn’t get it right, it often becomes front-page news and occasionally becomes a subject of litigation. Yet organizations face an equally challenging imperative to ensure that business users have easy access to the data they need. Depending on how they are implemented, data governance policies can inhibit access to data, making it harder to find and utilize the data assets of an organization.
Topics: Big Data, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Internet of Things
At Ventana Research we’re familiar with the need for digital transformation as we have been researching and providing education on this topic for almost two decades. And recent global challenges make even clearer the sea change at hand: digital innovation is essential for not only success, but survival. Business continuity during a pandemic, natural disaster, cyber event or geopolitical situation requires business and risk mitigation processes, but unfortunately very few organizations had been doing so. We are seeing how quickly organizations are going into survival mode, in how they operate and communicate to meet the expectations of the workforce, customers, stakeholders and potentially shareholders.
Topics: Sales, Customer Experience, HCM, Marketing, Office of Finance, Business Continuity, Analytics, Digital Technology
Artificial intelligence (AI) and machine learning (ML) are all the rage right now. Our Machine Learning Dynamic Insights research shows that organizations are using these techniques to achieve a competitive advantage and improve both customer experiences and their bottom line. One type of analysis an organization can perform using AI and ML is predictive analytics. Organizations also need to plan their operations to predict the amount of cash they will need, inventory levels and staffing requirements. Unfortunately, while planning begins with predictions, organizations can’t plan with AI and ML. Let me explain what I mean.
Topics: Office of Finance, Analytics, Business Intelligence, Financial Performance Management, Digital Technology, Predictive Planning, AI and Machine Learning
I was recently asked to identify key modern data architecture trends. Data architectures have changed significantly to accommodate larger volumes of data as well as new types of data such as streaming and unstructured data. Here are some of the trends I see continuing to impact data architectures.
Topics: Analytics, Business Intelligence, Data Governance, Data Preparation, Data, Information Management (IM), Digital Technology, data lakes, AI and Machine Learning
Topics: Analytics, Business Intelligence, Collaboration, Data Governance, Data, Digital Technology, Conversational Computing
Ventana Research recently announced its 2020 research agenda for analytics, continuing the guidance we’ve offered for nearly two decades to help organizations derive optimal value from their technology investments and improve business outcomes.
It’s been exciting to follow the emergence of innovative capabilities in the analytics market, but for businesses it can be challenging to stay on top of all these changes. To help, we craft our research agenda using our firm’s knowledge of technology vendors and products and our experience with and expertise on business requirements.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, natural language processing, AI and Machine Learning
Ventana Research recently announced its 2020 research agenda for digital technology, continuing the guidance we’ve offered for nearly two decades to help organizations derive optimal value and improve business outcomes. While we have seen more than four decades of digital transformation in the systems and tools businesses rely on, recent years have yielded transformative approaches to technology that can finally actually change the way people and processes work, rather than just make traditional processes more efficient.
Topics: Analytics, Internet of Things, Data, Digital Technology, blockchain, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing, extended reality
Kinaxis recently held its annual user conference, Kinexions, which focuses on helping the company’s customers improve their execution of supply chain and sales and operations planning (S&OP). Its RapidResponse software handles S&OP, demand, supply, inventory and capacity planning. S&OP is a function sorely in need of improvement: Our research finds that only 22 percent of companies perform it well or very well.
Topics: Continuous Planning, Analytics, Enterprise Resource Planning, AI and Machine Learning, continuous supply chain, business digital commerce
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