Forecasting the Future of Forecasting: Where We've Been and What's It Given Us

The financial world is ever-changing, driven by technological innovation. The traditional budget models that have been the norm are rapidly being rendered obsolete as the era of instability becomes more widespread. Standing on the shoulders of what has come before, analysts using new forecasting models can produce analyses quicker, spend less time managing and massaging historical data and — most vitally — leave room to address rapid changes inherent in today's rocky environments. Evolution: From Crawling to Walking to Running

"Evolution" is truly the best word to describe the way that, over time, models used by companies to plan, achieve, and evaluate the results of budgets have changed. Starting with the simplest form of setting a budget, the incorporation of forecasting tools helped entrepreneurs predict how far off from that budget they might be.

As those forecasting tools became more sophisticated, so did the understanding of rapid change and market volatility. The failure to foresee changes in business activity, even with the most sophisticated of budgets, could still easily prove devastating. Rather than simply focus on understanding and predicting the future, rolling forecasts have developed to allow for more effective early warning systems—systems and processes that in today's climate, enable better outcomes.

Shifting From Data Production to Data Analysis

For years, much of the focus in budgeting and forecasting was on data production. Accounting led the charge by focusing on the aggregation of vast amounts of financial data and synthesizing this data into reports that would inform and guide forecasts.  Unfortunately, this data looked backwards, essentially advising management on what it should have done weeks or months earlier… If only they had known what the data was now telling them!

The modern use of data is more foward-looking. Organizations have focused on Data Governance featuring data ownership, consistency, centralized storage and improved access tools.  This focus is beginning to pay dividends for companies and allowing them to shift priorities to more granular information and, more importantly, analytics. New strategies involve less emphasis on simply collecting and storing data to proofing data to make sure it accurately reflects environments and behaviors.  This data enrichment enables better and faster decision-making — taking the formerly nearly mythic "single source of the truth" (SSoT) of structuring information models from the realm of myth to reality.

Trending Changes: Happening in Seconds, Not Hours

Accounting teams have been focused for many years now on automation, so that effort can shift from production to analysis.  But FP&A has lagged behind this curve in many organizations and can no longer be satisfied with manual effort. The manually updated spreadsheets of the past simply cannot keep pace with the rapidity of "trending" changes and the occurrence of unexpected events, ranging from wild swings in commodity prices to the emergence of new competitors to risk events. There is an increasing recognition that the need to improve information production practices extends beyond just financial accounting to more comprehensive FP&A data that includes operational metrics across multiple dimensions.

Automation is the key to this evolution, starting with automating production of core data. This allows for the faster and more accurate ongoing generation of rich operational data, keeping pace with the rapid changes in the underlying business. Reporting can also be automated, with software able to pinpoint the most valuable insights in a way that would take countless hours.

"The (Single) Source of the Truth"

The financial accounting world has largely completed the shift to SSoT, if only to support regulatory requirements and avoid punitive action. This experience can provide the platform that FP&A functions can leverage as they too move towards that goal.  The bedrock of effective forecasting is the combination of a solid base of data – agreed to by all users – and the automated analytics against that base that in turn help inform what the future may look like. And that bedrock is well within reach, enabling better early warning systems and more nimble organizational response.