I spent years in the talent acquisition space, and I think that at least several months of that time – cumulatively – was spent just trying to get people to calm down. Talent acquisition is a critically important business process, but if I had a dollar for every time I had to remind someone that there really are no recruiting emergencies, I’d be a wealthy woman.
Streaming data has been part of the industry landscape for decades but has largely been focused on niche applications in segments with the highest real-time data processing and analytics performance requirements, such as financial services and telecommunications. As demand for real-time interactive applications becomes more pervasive, streaming data is becoming a more mainstream pursuit, aided by the proliferation of open-source streaming data and event technologies, which have lowered the cost and technical barriers to developing new applications that take advantage of data in motion. Ventana Research’s Streaming Data Dynamic Insights enables an organization to assess its relative maturity in achieving value from streaming data. I assert that by 2024, more than one-half of all organizations’ standard information architectures will include streaming data and event processing, allowing organizations to be more responsive and provide better customer experiences.
Field service is a segment of customer experience that is dominated by two elements: the complexity of the issues handled, and the high cost of providing on-site services. It is recognized as a critical component of the service experience, especially when managing the condition of high-precision equipment in the medical, manufacturing and utility industries. It is also a high-risk moment in the customer life cycle. Consumers often experience the process as a series of disconnected visits and handoffs that fail to resolve issues the first time.
Organizations are collecting vast amounts of data every day, utilizing business intelligence software and data visualization to gain insights and identify patterns and errors in the data. Making sense of these patterns can enable an organization to gain an edge in the marketplace and plan more strategically.
Although the digital transformation of the finance department was a topic of discussion before 2020, it became a front-and-center issue as organizations locked down and in-office interactions became impossible. Finance and accounting departments were immediately confronted with a challenge because of their limited adoption of technology that would support a virtual working environment. As our 2019 Office of Finance Benchmark Research found, they are technological laggards: 45% are at the tactical or lowest level of competence in using technology across multiple processes and functions, while only 12% are at the highest. In my experience, many finance and accounting professionals and those running the department do not necessarily think that such competence is necessary, but this thinking is outdated because, increasingly, technology is the only practical way to address the department’s responsibilities (for example, the new revenue recognition for contracts accounting standards). To gain full advantage of technology, finance and accounting organizations must become “fast followers,” avoiding the bleeding edge but breaking the habit of waiting until the last possible moment before adopting proven advances.
When joining Ventana Research, I noted that the need to be more data-driven has become a mantra among large and small organizations alike. Data-driven organizations stand to gain competitive advantage, responding faster to worker and customer demands for more innovative, data-rich applications and personalized experiences. Being data-driven is clearly something to aspire to. However, it is also a somewhat vague concept without clear definition. We know data-driven organizations when we see them — the likes of Airbnb, DoorDash, ING Bank, Netflix, Spotify, and Uber are often cited as examples — but it is not necessarily clear what separates the data-driven from the rest. Data has been used in decision-making processes for thousands of years, and no business operates without some form of data processing and analytics. As such, although many organizations may aspire to be more data-driven, identifying and defining the steps required to achieve that goal are not necessarily easy. In this Analyst Perspective, I will outline the four key traits that I believe are required for a company to be considered data-driven.
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
Since its inception 20 years ago, Ventana Research has advocated for a shorter accounting close because it can improve the performance of the entire organization, not just finance and accounting. An important benefit of a shorter close is increased staff time for analysis and the preparation of reports and narratives that improve communications with the board and outside investors. Similarly, the department can provide those in operating roles the financial and managerial accounting results to highlight opportunities and issues they must address.
There are more digital channels in the commerce space than ever before: the web, mobile apps, text, voice-activated “agents,” video and social channels. Conversational computing and hyper-personalization are transforming customer engagement, and organizations may need to undergo a digital platform renovation to optimize customer and product experiences or risk lagging behind competitors. B2B selling and buying are increasingly using methods similar to B2C digital approaches to mirror the digital commerce experience that has grown substantially within the last few years. Salesforce Commerce Cloud is one of the platforms utilizing this approach.
We’ve recently published our latest Benchmark Research on Data Governance and it’s fair to say, “you’ve come a long way, baby.” Many of you reading this weren’t around when that phrase was introduced in 1968 to promote Virginia Slims cigarettes, but you may have heard the phrase because it went on to become a part of popular culture. We’ve learned a lot about cigarettes since then, and we’ve learned a lot about data governance, too.
In my more than two decades in the world of human resources and human capital management technology, I have never seen a topic become so completely ubiquitous so quickly as has employee experience. This is great news from my perspective. As I addressed in this recent analyst perspective, market factors have forced organizations to acknowledge the tremendous bottom-line value of an engaged workforce, and that engagement is wholly dependent upon an employer's commitment to providing a personalized, well-rounded experience.
We conducted our recent Smart Close Dynamic Insights Research in part to assess to what extent the substantial disruptions of the pandemic have impacted the accounting close. When office lockdowns began in the first quarter of 2020, many finance departments were challenged by having to do their quarterly close remotely without their normal face-to-face interactions. In the United States, the Securities and Exchange Commission was so concerned that corporations would be unable to meet their filing deadlines that they gave registrants carte blanche to extend their filing if necessary. As it turned out, only a relative handful did, and all but one of those was based in China; but for many, that first calendar close required a heroic effort. Since then, organizations have made concerted efforts to adopt and use technology to enable them to operate resiliently under any conditions. Our research finds that while organizations have to some extent adapted to operating a more remote working environment, progress toward a faster close has been elusive. The research also confirms that organizations that use technology effectively to automate processes are better able to complete their close sooner.
OneStream offers a platform designed to serve the needs of accounting and financial planning and analysis organizations. The software handles financial close and consolidation, planning and budgeting, analysis and reporting. For me, the most significant announcement at the company’s recent user conference was the unveiling of its Sensible ML (Machine Learning) offering, which is in limited general release. I’ve commented on the importance of artificial intelligence in business applications, and Sensible ML is a promising and important step in that direction.
I recently wrote about the growing range of use cases for which NoSQL databases can be considered, given increased breadth and depth of functionality available from providers of the various non-relational data platforms. As I noted, one category of NoSQL databases — graph databases — are inherently suitable for use cases that rely on relationships, such as social media, fraud detection and recommendation engines, since the graph data model represents the entities and values and also the relationships between them. The native representation of relationships can also be significant in surfacing “features” for use in machine learning modeling. There has been a concerted effort in recent years by graph database providers, including TigerGraph, to encourage and facilitate the use of graph databases by data scientists to support the development, testing and deployment of machine learning models.
“Lead to cash” is an often-used term and is a companion to “quote to cash” and “order to cash”. What they all represent is an approach which recognizes that there is a process designed to convert a lead from a qualified interest to an active sale, through quote and contract negotiation, to order or contract, invoice and payment. “Quote to cash” and “order to cash” are subsets of this process, with different starting places, but ultimately end in the same place: with a payment for a delivered product or service.
A few years ago – somewhat tongue in cheek – I began using the term “data pantry” to describe a type of data store that’s part of a business application platform, created for a specific set of users and use cases. It’s a data pantry because, unlike a general-purpose data store such as a data warehouse, everything the user needs is readily available and easily accessible, with labels that are immediately recognized and understood.
Organizations are continuously increasing the use of analytics and business intelligence to turn data into meaningful and actionable insights. Our Analytics and Data Benchmark Research shows some of the benefits of using analytics: Improved efficiency in business processes, improved communication and gaining a competitive edge in the market top the list. With a unified BI system, organizations can have a comprehensive view of all organizational data to better manage processes and identify opportunities.
Topics: business intelligence, embedded analytics, Data Governance, Data Management, natural language processing, AI and Machine Learning, data operations, Streaming Analytics, operational data platforms