Analyst Perspectives

It’s likely that finance analytics trace back to when people first began to record transactions on clay tablets. Financial analytics were given a boost with the codification of double-entry bookkeeping, an elegant system for recording transactions that facilitate the assessment of the performance and health of an organization. Further advances were achieved with the first mechanical – and then digital system – for automating computations, while personal computing devices made the numbers accessible to all.

Aside from these advances, the scope of the analysis performed by finance departments hasn’t changed much. Our Office of Finance Benchmark Research found that only 34% of organizations regularly perform customer profitability analysis, 30% measure product profitability and only 24% utilize predictive analytics. That will change in the 2020s, mainly as a result of easier access to data and the use of artificial intelligence for analytics.

By itself, data has limited usefulness to a business; the application of analytics is necessary to transform data into actionable information. Data analysis of one sort or another has long been a core competency of finance departments, applied to balance sheets, income statements or cash flow statements. Today, however, finance must go beyond these basics by expanding the scope of the data being examined to include all financial and operational information that can yield actionable insights.

Finance departments need to use more than accounting data to support management decisions because, by itself, accounting data is insufficient to assess company performance and guide business decisions. Analysis should include, for example, data from the systems that manage sales operations, human resources and field service, and that data must be available to all departments and applications that need it.

Beyond the traditional financial analysis, techniques applied to the fusion of operational and financial data require an understanding of different parts of the business to define relevant metrics that should be tracked and communicated. Combining financial and operational data gives managers and executives a deeper and more complete understanding of performance and the factors driving results. It provides them with more realistic analyses to guide business decisions.

For example, combining information from customer relationship management systems with financial data can lead to better understanding of customer profitability and costs. Likewise, understanding all the factors driving costs related to a project can improve project management and support better-informed pricing of resulting products. Being able to analyze inputs and outputs in terms of units of things — such as labor hours, board feet of lumber or the number of full truckloads — separately from prices and costs makes performance measurement and management more accurate and actionable.

In addition, finance departments must be able to easily organize financial and operational data to drill down into details based on the relevant characteristics of the business, not just the chart of accounts. Executives and managers need to examine results from multiple perspectives, such as by some combination of business unit, department, product type, sales territory, customer and sales channel. They also need to be able to “drill around” data to explore other views of related information, perhaps in other applications, to improve understanding of business conditions or factors that may be influencing results.

Finance organizations also need to be able to quickly create and revise reports and dashboards to communicate analyses to those who need the information. Companies can realize fully the benefits of finance analytics only if the analyses are communicated in a timely fashion. Business is fluid, so it’s important to have tools that make it easy for analysts to adapt to changing requirements.

Data issues often prevent finance organizations from using analytics as effectively as they could. The inability to access needed data and excessive time spent in preparing data constrain the capabilities and limit the productivity of analysts. Our Analytics and Data Benchmark Research reveals that 60% of accounting and finance teams spend a majority of their time preparing data for analysis.

Technology now can facilitate data management. Ventana Research has been using the term “data pantry” for a type of data store created for a specific set of users and use cases that’s part of a business application. A data pantry makes it possible for analysts and business users to immediately access all necessary data gathered from multiple sources for analysis and reporting, without the need to repeatedly perform data extraction, enrichment or transformation motions. This delivers all of the information needed in a consistent and useful form and format. 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. Increasingly, vendors are offering a data pantry capability (even if they don’t call it that) as part of an offering, especially vendors that position products as a platform.

Artificial Intelligence will be the most consequential technology for business computing this decade, but financial analysts won’t have to be data scientists to make use of the technology. 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. Increasingly, all vendors of software aimed at the office of finance will differentiate offerings by the capabilities and accuracy of AI functionality. And organizations will adopt this technology to attract and retain the best talent, because AI enables substantial reduction of low-value work, so there’s more time for workers to focus on tasks that require their expertise, experience and judgment. The main objective of AI-enhanced software is to have the applications do more so that people can contribute more.

There are many potential uses of AI, especially automating and enhancing the breadth of analytics used by financial planning and analysis organizations, applying machine-driven task supervision to speed process execution and reduce errors, generating recommendations and automating report commentary. AI-enabled systems increasingly will:

• Improve the effectiveness of predictive analytics and forecasting by marrying financial and operational planning.
• Package analytical techniques to make it easier for business analysts with limited time or training to provide more sophisticated analyses.
• Reduce bias in forecasting and increase the accuracy of forecasts and assessments, enabling executives and managers to make better business decisions. This will be accomplished by employing a wider range of financial and operational data and building on proven analytical techniques.
• Speed the delivery of intelligence, enhancing organizational agility by being able to react faster to changing conditions by automating what are now manual data movement and reporting motions.

Today, finance organizations do a good job of delivering basic, inwardly focused financial analyses to executives and managers. But they can do more. Their analytics must provide deeper insight into the operating performance of the organization, incorporate more external data and provide information as immediately as possible. And information technology is essential to delivering better finance analytics. Organizations must use the right software and have the right data available to be able to improve the scope, quality, business impact and timeliness of analytics. Expectations for the analytics finance organizations provide will rise steadily throughout the 2020s. I recommend that leaders of FP&A groups evaluate how they can use technology to expand and enhance the value of the analytics they perform and deliver to their entire organization.

Regards,

Robert Kugel