Robert Kugel's Analyst Perspectives

Building a More Predictive Finance Department

Posted by Robert Kugel on Oct 5, 2022 3:00:00 AM

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

There are four key technologies that finance organizations need to become predictive: Planning and budgeting, predictive analytics, AI and data management.

MicrosoftTeams-image (2)-4Planning, forecasting and budgeting are core competencies of the predictive finance organization. An application that supports high-participation, collaborative, action-oriented planning built on frequent, short planning sprints (what we call integrated business planning) makes these processes more accurate and effective. Our Office of Finance Benchmark Research confirms that organizations that use dedicated planning software technology have planning processes that work better: 66% of those that use an application have a budgeting and planning process that works well or very well, compared to 36% of those that use spreadsheets.

A planning approach built on integrated planning technology enables organizations to create more accurate plans because refinements are made at shorter intervals. Short planning cycles enable companies to achieve greater agility in responding to market or competitive changes. Planning across the entire organization in a coordinated fashion as an ongoing, collaborative dialogue that brings together finance, line-of-business managers and executives (rather than individual, functional silos) delivers better results. And because it’s high-participation planning and not silo-based, companies can plan with greater accountability and coordination in their operations. This ongoing dialogue tracks current conditions as well as changes in objectives and priorities that are driven by markets and the business climate. This sort of process promotes a forward-looking mindset in planning and reviewing, one that’s focused on performance improvement.

Dedicated software is an essential part of integrated business planning because it's not feasible for any midsize or larger organization to use desktop spreadsheets for this purpose. They have inherent technological limitations that make it impossible to execute short planning cycles because consolidating desktop spreadsheets is so time-consuming.

MicrosoftTeams-image (1)-3Predictive analytics is a necessary discipline for finance departments. Although technology has been available for decades, our research finds that just 24% of finance organizations use it extensively in their daily operations. Predictive analytics is a technique that companies can utilize to achieve better results. It can create more accurate and nuanced projections of future outcomes and is especially useful in quickly finding divergences from expectations to create more timely alerts. For instance, rather than having to wait until the end of the month to review results and initiate a course of action, a corporation could use predictive analytics early in the month to spot and address a probable revenue shortfall in a specific product line or to change production rates or shipments to avoid a likely regional stock-out caused by unexpected demand. Predictive analytics are increasingly embedded in planning applications. However, the capabilities alone are not enough; one barrier to the successful adoption of predictive analytics is the timely availability of accurate and relevant data. More on data availability below.

Artificial intelligence using machine learning (AI/ML) involves the application of data-science-derived algorithms that include the capability to “learn” and therefore change to achieve better results as more data is collected and more outcomes are observed. Additionally, AI applies ML algorithms, often in a way that mimics how a human might respond to, or manage, a process. Combined, the two make it possible to have computer systems that adapt to the needs of an organization without requiring a custom deployment process that results in a rigid implementation. Consequently, AI will have a profound impact on white-collar professions, especially in heavily rules-based functions such as accounting, as I’ve noted. Machine learning can also drive relevant prescriptive analytics. These present executives and managers with a range of possible responses to a specific business condition along with potential outcomes and an assessment of their probability.

MicrosoftTeams-image-4Because of their transformational potential, software vendors are giving AI/ML a significant amount of attention and I expect that their use will be pervasive in departments within five years. Ventana Research asserts that by 2025, almost all vendors of software designed for finance organizations will have incorporated some AI capabilities to reduce workloads and improve performance. It’s important, though, to recognize that while some applications are already in use, others are a decade or more away from replacing or significantly augmenting human tasks. It’s also important to understand that any practical application of these technologies requires the timely availability of large quantities of accurate and relevant data. Yet, as a measure of data accessibility, our Internet of Things and Operational Intelligence research finds that fewer than one-half of organizations (49%) rate their ability to track events and trends in their systems as either excellent or good. Which brings me to the fourth essential technology.

Data management is the final essential technology necessary for a predictive finance organization. The usefulness of predictive analytics and ML depend on being able to access and use large amounts of data. Increasingly, business software vendors have been incorporating what I call “data pantries” in their applications to streamline data access and management. These dedicated data stores make it easier for FP&A organizations to access a wider range of operational data, not just accounting numbers, to provide more insightful analyses for both finance and operations, as well as external data related to markets and competitors that can be useful for forecasting, planning and performance assessments.

MicrosoftTeams-image (3)-3Today, finance organizations struggle with handling just their basic data requirements. Data management has been a challenge in business computing for decades. Our Analytics and Data Benchmark Research reveals that more than two-thirds of participants spend the most amount of their time preparing data for analysis and reviewing data for quality issues. Increasingly, technologies like the data pantry are available to streamline the process of gathering, validating and preparing data, all of which supports a predictive finance department.

Over the past decade, technology has steadily advanced to enable the transformation of the finance department. However, technology by itself isn’t enough to foster an expanded mission. This sort of transformation can only happen when the CFO and CEO decide to alter the focus of the finance and accounting department. As resources are freed up and manual accounting workloads are reduced, the CFO, the CEO and the board of directors must reconsider the role of the department. At one extreme, they can emphasize departmental efficiency by reducing headcount and retaining a limited role as a transactions processor and reactive bean counter. Alternatively, the productivity gains and new capabilities that technology affords can be used to repurpose the freed-up resources to take on a more strategic role, one that provides more analytical and advisory services, facilitates planning and promotes agility. I believe that organizations that choose the latter course will have a competitive advantage over those that choose to be technology laggards. I recommend that organizations focus on developing their ability to best utilize the capabilities of the four types of software.


Robert Kugel

Topics: Office of Finance, business intelligence, Data Management, Business Planning, Financial Performance Management, ERP and Continuous Accounting, AI and Machine Learning, digital finance

Robert Kugel

Written by Robert Kugel

Rob heads up the CFO and business research focusing on the intersection of information technology with the finance organization and business. The financial performance management (FPM) research agenda includes the application of IT to financial process optimization and collaborative systems; control systems and analytics; and advanced budgeting and planning. Prior to joining Ventana Research he was an equity research analyst at several firms including First Albany Corporation, Morgan Stanley, and Drexel Burnham, and a consultant with McKinsey and Company. Rob was an Institutional Investor All-American Team member and on the Wall Street Journal All-Star list. Rob has experience in aerospace and defense, banking, manufacturing and retail and consumer services. Rob earned his BA in Economics/Finance at Hampshire College, an MBA in Finance/Accounting at Columbia University, and is a CFA charter holder.