Ventana Research Analyst Perspectives

Ventana Research is happy to share insights gleaned from the latest Value Index research, an assessment of how well vendors’ offerings meet buyers’ requirements. The 2022 Revenue Performance Management (RPM) Value Index is the distillation of a year of market and product research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to vendors supporting the spectrum of revenue performance management. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the Product Experience ﹘ Adaptability, Capability, Manageability, Reliability and Usability ﹘ and two related to the Customer Experience ﹘ Total Cost of Ownership or Return on Investment and Vendor Validation.

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.

As organizations shift focus to a broader definition of sales that includes all sources of revenue, vendors are also pivoting to include “revenue” as part of promotional messaging. But it’s my view that just changing your message or description does not necessarily deliver the capabilities and product experiences customers need to successfully plan, execute and achieve revenue targets and objectives. The just-completed 2022 Ventana Research Value Index for Revenue Performance Management addressed this shift, focusing on available product capabilities that support customer needs as well as their overall experience.

The term "corporate spend" usually refers to the incidental but still significant outlays organizations make to support operations. Especially in nonmanufacturing industries, purchases of indirect goods and business services – such as computers, office supplies, furniture and services – as well as travel and entertainment can represent a significant percentage of total costs. Technology has evolved to the point where executives – especially the chief financial officer – need to take an overarching approach to corporate spend that utilizes technology to tighten controls, deepen visibility into expenditures, increase productivity and reduce process frictions. Spend management software and corporate spend cards – either physical or virtual – offer a means of achieving spend management objectives. This is part of a broader trend to digitizing outlays: I assert that by 2025, more than two-thirds of organizations will be using spend management software and corporate cards to achieve greater control and increased efficiency.

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.

Ventana Research defines subscription management as the processes and technology needed to manage the subscriber experience from the first digital touch to the continuous modifications of orders for services and billing. Effective subscription management requires a new generation of applications designed to manage the life cycle of subscriptions and provide subscribers with the experiences they expect. The subscription business model has grown in popularity across many industries, and for many organizations it is now part of how they conduct business. Organizations, whether through line extensions, completely new businesses or through mergers and acquisitions, now have a mixed business model combining subscription and usage with one-time sales, often as a bundle of related products and services. The model establishes a regular, predictable income stream and monetizes existing and new assets. In addition, usage-based pricing is preferred by many consumers, both B2B and B2C, because it is more closely aligned to actual consumption patterns. For product companies, selling by subscription enables them to maintain ongoing contact with customers to facilitate future sales. Subscription is also popular with customers as it allows a degree of control from the buyer’s point of view and can be cancelled or modified, typically online, in a frictionless manner.

Today’s contact center agents find themselves handling increasingly more complex interactions due to changes in consumer demand, advances in self-service and the proliferation of digital contact channels. This added complexity requires continuous agent support for successful customer experience outcomes. Intelligent software can reduce agent workload and improve customer interactions by picking up customer cues.

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.

Ventana Research recently published the results of our Business Planning Value Index Research and I commented on its connection to our emphasis on using software to unify planning processes across an enterprise to improve performance. Since 2007, we have advocated what we call Integrated Business Planning (IBP): a high-participation, collaborative, action-oriented approach to planning and budgeting built on frequent, short planning sprints. Short planning cycles enable companies to achieve greater agility in responding to market or competitive changes.

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.

Digital Transformation. The Subscription Economy. Omni-Channel Selling. Customer Centric. These are all terms used to label trends and events that are changing the way business is being conducted, a change that has accelerated due to recent events. Regardless of the terminology, there is no doubt that the way vendors and buyers are interacting, whether B2C or B2B, is different today for many organizations than it was even five years ago. But to be fair, no technology on its own can transform your business without changes to the other two key elements: people and processes. In addition, change is unlikely to happen if you are also relying on your existing ERP or CRM systems.

The technology underpinning customer experience (CX) is a hodgepodge of tools that have been developed for niche use cases and then expanded to fill broader roles. Examples include the old (CRM, help desk software and speech analytics) and the new (customer data platforms and conversational AI). This is because CX is a set of very specialized processes that happen in different parts of the enterprise, managed by people who often do not connect with peers handling related processes. Service-related activities are focused in the contact center, personalization and loyalty in marketing departments, and so forth.

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.

Value-added tax is a type of levy that is applied at each step of a transaction chain, from basic inputs to the final good or service. The amount assessed is based on the value added by an organization (hence the name) when a transaction occurs. VAT is used throughout the world because, historically, it has been harder to evade compared to income taxes. VAT is a common method of national taxation: Approximately 85% of countries impose it worldwide. A notable exception is the United States, where sales and use taxes are imposed at the state and local level and applied only to the price of the final good or service. As commerce has become global and cross-border sales have increased, traditional methods for calculating, applying and complying with VAT regimes has grown more complex. To achieve higher tax revenue while ensuring better compliance, governments are turning to technology to make collections more effective while making processes more efficient.

I recently examined how evolving functionality had fueled the adoption of NoSQL databases, recommending that organizations evaluate NoSQL databases when assessing options for data transformation and modernization efforts. This recommendation was based on the breadth and depth of functionality offered by NoSQL database providers today, which has expanded the range of use cases for which NoSQL databases are potentially viable. There remain a significant number of organizations that have not explored NoSQL databases as well as several workloads for which it is assumed NoSQL databases are inherently unsuitable. Given the advances in functionality, organizations would be well-advised to maintain up-to-date knowledge of available products and services and an understanding of the range of use cases for which NoSQL databases are a valid option.

Today, organizations understand the importance of good external data that can be integrated with internal data to train machine learning models. Our Machine Learning Dynamic Insights research showed that external data adds a significant value in gaining competitive advantage, improving customer experience and increasing sales. But getting the right external data for a particular requirement is not always easy. Internal data is usually not enough to train different models because of its narrow scope of usage and lack of relevance. Manual data acquisition methods are resource-intensive and can take weeks or months to get the data ready to feed into models.

I recently attended an analyst conference held by Unit4, an enterprise resource planning vendor focused on midsize organizations in people-centric industries. The conference was intended to communicate the company’s strategy, product updates and roadmap. The meeting took place shortly after announcement of the availability of Unit4 Industry Mesh and the acquisition of Compright, which does compensation planning as well as in the context of the broad technology shifts affecting ERP applications.

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.

Software that automates the full scope of the accounting close, including reconciliations, consolidation and reporting, has grown more capable and affordable over the past five years. By enabling consistent process management that captures best practices, and by automating rote, repetitive activities to boost staff productivity, these applications enable organizations to shorten the close, make the process more efficient and reduce the risk of material errors by strengthening accounting controls. As accounting departments have learned over the past two years, close automation software helps ensure business continuity under any circumstance, especially as remote workforces that are able to perform the close virtually become more commonplace.

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.

As businesses become more data-driven, they are increasingly dependent on the quality of their data and the reliability of their data pipelines. Making decisions based on data does not guarantee success, especially if the business cannot ensure that the data is accurate and trustworthy. While there is potential value in capturing all data — good or bad — making decisions based on low-quality data may do more harm than good.

In a previous Analyst Perspective, we discussed some of the big-picture trends that are bringing cost control back as a core driver of contact center operations. In this report we will tackle some of the practical ramifications: how those trends affect decision-making and operations.