Robert Kugel's Analyst Perspectives

I recently completed the latest edition of our Business Planning Buyers Guide, which reviews and assesses the offerings of 14 providers of this software. One of the points that I look at is whether and to what extent the software provider offers out-of-the-box external data useful for forecasting, planning, analysis and evaluation. What I discovered is that the availability of this type of vital information is exceedingly slim.

Enterprises do not operate in a vacuum, and things happening outside an organization’s walls directly impact performance. So, it is essential to incorporate external data in forecasting, planning and budgeting, especially for predictive analytics and machine learning to support artificial intelligence. I use the term external data to include any information about the world outside an organization (including economic and market statistics), competitors (such as pricing and locations) and customers. Until recently, it was adequate for organizations to regard external data as a “nice to have” item, but that is no longer the case. External data is necessary for many functions, including useful and accurate competitive intelligence used by sales and marketing groups. It is also essential for the effective application of AI using ML for business-focused planning and budgeting and predictive analytics.

Some enterprises already collect basic external data such as exchange rates, commodity prices, economic data and competitors’ prices. Few go deeper or gather external data in a way that makes it accessible across an enterprise. ISG Research asserts that by 2027, one-third of enterprises will incorporate comprehensive external measures to enable ML to support AI and predictive analytics and achieve more consistently performative planning models. 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.

External data enhances the predictive capabilities of models by dealing with the problem of endogeneity—specifically, the issue of omitted variables—in forecasting and analysis. To illustrate, consider a vendor selling ice cream on the beach. On most days, the price does not change, but on cooler-than-normal days when fewer people are on the beach, the vendor often drops the price to sell as much inventory as possible. Conversely, the price increases on very warm days when the beach is crowded. The vendor sells more than average, and people are willing to pay more. At the end of the season, the vendor brings in a consultant to advise on pricing for the coming year. If that numbers-driven consultant only considers the daily sales totals and the price received each day, the “obvious” conclusion is that higher prices lead to more unit sales, so raise prices. That illogical conclusion is only possible because a key variable about the external environment is missing: the daily temperature.

External data is also essential for creating robust ML systems that support AI. There are many potential uses of this technology for finance and accounting departments, as I have noted, including enhancing the accuracy and agility of forecasting and planning by automating time-series analysis to rapidly develop predictive models for more accurate project revenue and costs, balance sheets and cash flow. It can enhance the breadth of analytics available to improve situational awareness and decision-making. For these purposes, external data is almost always necessary to create performant models because such data can have high explanatory value in determining demand, supply, prices and costs and, in so doing, enables systems to avoid undermining their credibility.

Artificial intelligence and predictive analytics are similar. Predictive analytics use algorithms and advanced statistical methods applied to datasets to make forecasts more nuanced and potentially less biased and more accurate than those built around simple rules of thumb or intuition. Robust datasets that hold a large and diverse set of data from which to glean inferences create more useful and accurate forecasts. Predictive analytics can include ML to analyze data quickly. AI is making predictive analytics far more accessible than in the past, so access to external data that supports more robust model creation is essential to make the predictive models consistently performative.

A robust dataset is also valuable because predictions are almost always inaccurate. When a prediction turns out to be wrong enough, predictive models built around multiple drivers make it possible to identify which part of that model turned out to be wrong—either because an element of the model turned out differently or because the model itself was flawed. This provides useful information about what to do next time to achieve a better outcome and how to refine the model to improve its accuracy. Robust, in this case, almost always means that the dataset incorporates data about the external world. A robust dataset improves the quality of forecasting and enables better, deeper and more insightful analysis. And robust data is essential for making useful recommendations, particularly in qualifying the degree of certainty that might be attached to a specific recommendation.

While there are a lot of statistical techniques applied to predictive models, there will always be the need for experience and intuition in making decisions. External data used in forecasting, analysis and planning is useful in tactical, short-term (that is, up to one year) operational planning by a function or business unit—such as demand planning, marketing, manufacturing and supply chain. Especially in tactical planning that explores multiple contingencies, building predictive models using external data enriches the process by identifying a wider range of explanatory variables. External data also improves strategic and long-range planning—planning that typically extends the outlook past the fiscal-year budget—to strategize around market trends, identify opportunities and threats and develop project plans. This involves identifying the most important macro drivers of demand and supply. A typical application of these forecasts is identifying investment opportunities in the form of projects, capacity expansion and business acquisitions and applying rigorous methods of valuation.

Accessibility is a fundamental issue holding back the use of external data for forecasting, planning and analysis. I continue to recommend that providers of planning applications help by including data as part of the service. Some basic statistical data in the public domain could be incorporated in the subscription price, while the software provider could bill on an as-needed basis for more proprietary sets. Without external data, predictive analytics and forecasts built solely around an organization’s internal information are fundamentally flawed because of the absence of consequential information that drives results. Data services would be a win-win for providers and users, improving the statistical quality of results and an enterprise’s ability to enhance performance through more intelligent planning.

Regards,

Robert Kugel