In the context of planning, budgeting and benchmarking, external data includes information about the world outside an organization such as economic and market statistics, competitors and customers. Today, a comprehensive set of external data is a “nice to have” item in most organizations, but that’s likely to change. External data is necessary for useful and accurate business-focused planning and budgeting, and for performance benchmarking. It is also essential for the effective applications of artificial intelligence (AI) to these functions.
Many organizations already collect basic external data such as exchange rates, commodity prices, economic data and competitors’ prices, but few go deeper or gather external data in a way that makes it accessible across an enterprise. Ventana Research asserts that by 2023, fewer than 20% of organizations will incorporate comprehensive external measures in performance reviews and benchmarking to improve performance. Those that do will have a clearer picture of how and where to address operational shortcomings, and will be able to utilize AI more effectively to support more accurate and valuable predictive and prescriptive business planning.
A comprehensive set of external information is necessary for accurately analyzing and reviewing performance and for developing actionable next steps. Competition in business is an us-versus-them situation, so why is it that almost all corporate planning, budgeting, analysis and review activities are performed in an us-versus-us mode? Typically, comparisons in review meetings are made in terms of year-to-date compared to last year, or actual versus plan. It is not that these us-versus-us measures aren’t relevant and important for assessing performance, but they fail to tell the whole story and can be misleading. For example, if the plan called for a 4% increase in revenue and the sales were up 8%, is it all high-fives? It shouldn’t be if the market grew by 15%, because the company lost market share and, worse, didn’t understand and acknowledge it. Conversely, if sales were flat but the market was down 5%, heads shouldn’t roll because the organization actually outperformed the competition. To provide a complete picture, financial planning and analysis (FP&A) organizations must include external data in planning and benchmarking analysis and reporting. This is especially important as they adopt AI-assisted forecasting and planning.
As vendors increasingly add AI to their forecasting and planning tools, it’s important to note that an organization can’t just switch on artificial intelligence and presto—its forecasts and plans will be more accurate and relevant. Users of the technology must have a well-developed set of data for AI-enabled systems to draw on to “learn” how the organization operates down to a reasonable level of detail. Even then, if that data set is confined to, say, internal financial information with a limited amount of operational data and little or no external data, the result will be more like a financial statement extrapolation rather than a well-considered business outlook.
While financial data is an essential element in business forecasting, planning and budgeting, it’s insufficient to predict outcomes in a way that’s useful to business executives. Information based solely on internal financial information will serve the needs of the finance department in a relatively steady state environment, but lacking operational history and external data, it will have limited usefulness for managing the business and very limited value in responding to abrupt changes in the business environment. Money is an abstraction of the underlying realities behind accounting entries, such as units consumed to produce outputs, supply chain events and things that didn’t happen, such as when a competitor didn’t have a special promotion. Consequently, the results are from a model that predicts financial outcomes without explaining the underlying operational or external events producing that outcome.
Data about the environment outside of the organization is an especially important to building AI-enabled business models because this information almost always serves as a significant explanatory factor. Broad economic measures including GDP, industrial production, a purchasing managers’ index, price indexes and weather data are just some of the common variables that affect demand, prices and costs. Industry-specific data is also useful, especially in identifying leading indicators for specific products or business units and explaining buying behavior. Accurate and timely external data will become increasingly important to the FP&A organizations because the most effective application of AI to forecasting, planning and budgeting will require its use to isolate relevant measures that drive results and affect the company’s performance.
There are several reasons why companies don’t include information from the outside world in planning, reviewing and benchmarking. At the top of the list are habit and inertia, an attitude expressed as “we’ve always done it this way,” which is common in all organizations and especially in risk-averse finance departments. A second, related reason is that with workloads already allocated toward a standard set of tasks, an FP&A organization would be hard pressed to find the time to strike up a new initiative without a specific directive to incorporate external data. A third is that these numbers aren’t always readily available. Our Finance Analytics benchmark research found that just 12% of organizations have all the external data they need to analyze their performance. Most have some information, including easily captured commodity prices or exchange rates, but not having enough time or lacking the tools to efficiently collect other measures is a barrier. A fourth reason is the credibility of the data, which arises from two issues: the perceived lack of relevance of the data to the company, and the assessed competence of the source of the data. The former often is expressed as “our company is different” while the latter calls into question if the numbers are even accurate, consistent or timely.
External information is essential to building AI-driven forecasting, planning and budgeting models, for assessing performance to internal objectives and for benchmarking performance relative to markets and competitors. I recommend that organizations that are considering using AI to enhance the accuracy and value of their planning and budgeting efforts explore and identify the external factors that are most consequential for their performance. I also suggest that planning and budgeting software vendors that are serious about offering AI-enabled features offer a curated set of external data to their customers as part of the service to facilitate their use by FP&A organizations. Providing a reliable and readily available set of external data to customers can overcome most of the barriers to incorporating external data and will enhance the value of their software as a business tool.