Analyst Perspectives

ISG Research recently announced the results of its Business Planning Buyers Guide for 2025. We rated Board Exemplary and a leader in customer experience and in reliability. Board recently launched two external data offerings that significantly strengthen the capabilities of its planning software. Its Board Foresight is designed to support more effective and potentially more accurate forecasting using predictive models harnessing artificial intelligence (AI). Board Signals makes third-party industry-specific economic analysis available on its platform to better enable enterprises to identify and quantify potential risks and opportunities.

I have commented on the need for enterprises to use external information in the forecasting and planning processes. 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. Enterprises do not operate in a vacuum, and things happening outside an organization’s walls directly impact performance. So, it is essential for those doing business planning to incorporate external data in forecasting, planning and budgeting. This is especially the case for any predictive analytics and machine learning used to support AI.

Until recently, it was adequate for Financial Planning and Analysis (FP&A) and other enterprise planning organizations to regard external data as a “nice to have” item, but that is no longer the case. External data is necessary for many processes to function well, for instance the gathering and validating of competitive intelligence used by sales and marketing groups. It is also essential for the effective application of AI using machine learning (ML) for business-focused planning and budgeting and predictive analytics. Board’s offerings are designed to meet this need, making this data readily available on its platform.

Foresight addresses the need that forecasters have for timely and relevant economic indicators and the ability to identify useful correlations that can be used in the creation and maintenance of predictive analytics and driver-based models. Its correlation engine helps identify performance drivers useful for designing planning models as well as identifying key performance indicators (KPIs) that can be used in assessing how well any part of the organization or individuals are performing. Building models and forecasts around useful data sets also enables executives and managers to quickly identify the specific factor or factors behind differences between the forecast and the actual outcome. These sorts of multivariate models also make it possible to identify emerging disparities within the forecast period, which enables organizations to address the differences to accentuate positive outcomes or mitigate potentially negative ones.

Incorporating different points of view makes scenario planning more useful, especially from third parties that can be more objective about external conditions. Spreading the cost of acquiring these sources of data across a larger user base makes these viewpoints more accessible and affordable. Board is able to tailor economic, business and market indicators to inform enterprise forecasters and give them timely alerts on developments that can have an impact on demand, prices, costs and other factors.

Using external data makes business planning a more useful tool for executives and managers for achieving improved performance and increased competitiveness. Business competition is a matter of us-versus-them, so why limit analysis, planning and performance reviews to internal comparisons such as actual-to-plan and percentage changes from a prior period? Why celebrate a 7% increase in sales against an expected 5% rise when the market grew by 12% and, in reality, the company experienced a market share loss? To remain competitive, a comprehensive set of external data is no longer nice to have, it’s a necessity.

External data is required for useful and accurate business-focused planning and budgeting, and for performance benchmarking. Consider the example of an ice cream vendor who sells out on very hot days even when charging a premium but lowers prices on cooler days trying to move the inventory, only having to throw away unsold items at the end of the day. Using only internal data—volume sold and price-per-unit—a regression analysis would erroneously conclude that the more you charge the more you sell. Including external data such as temperature provides a more complete picture and one that produces more accurate forecasts and recommendations. This is becoming increasingly important as organizations adopt AI using ML, because not using external data in modeling will result in flawed models with potentially expensive negative consequences.

ISG Research asserts that by 2029, one-third of enterprises 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 data more effectively to support more accurate and valuable predictive and prescriptive business planning. I recommend that enterprises that are thinking about acquiring a dedicated planning application consider Board and using these offerings. I also recommend that those using Board for business planning evaluate Foresight and Signals.

Regards,

Robert Kugel