Ventana Research Analyst Perspectives

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

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

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

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

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

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

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

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

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

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

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

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

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