Written by Jim Webb, Oracle
Originally posted September 26, 2019
Advanced predictive analytics can be a catalyst for change for small and medium businesses (SMBs) even if they already are using predictive analysis.
The technology for predictive capabilities has evolved. It’s much simpler to use, and outcomes are easier to share, apply, and explain. Analysis is no longer limited to big-picture outlooks, such as what’s going to happen with the national economy. Financial professionals can now use advanced predictive capabilities daily to identify new insights to improve forecast accuracy.
In fact, I consider the ability to integrate these tools into the everyday life of the financial professional a game-changer.
Rx for the Lack-of-Insight Epidemic
Most SMBs using predictive analysis are a lot like the cash-strapped Oakland A’s when managers were preparing for the 2002 season without three star players who were becoming free agents. To formulate a winning plan against seemingly impossible odds, they needed to start looking at more data.
It wasn’t until the managers stopped focusing on finding replacements that could match performance stats of the departed players and started focusing on a formula for scoring more runs that they moved from 10-games-behind to breaking the American League record of 20 consecutive wins and clinching the American League West title.
The key to the turnaround was the advanced analytics work of Peter Brand, an economist who explained the current state to General Manager Billy Beane this way in the movie depiction Money Ball: “There’s an epidemic failure within the game to understand what is really happening, and this leads people who run Major League Baseball teams to misjudge their players and mismanage their team.”
Beane and Brand built a winning team with undervalued players by focusing on historical performance stats that no one else was looking at, and now their model (sabermetrics) is common in baseball.
The same player data was available to all of the A’s competitors in 2002, but no one was analyzing it like Beane and Brand. This latent data was enough to model a forecast that proved to be a true winning formula. It set the Oakland A’s and that season apart and became a management standard.
This is exactly what is happening now in business. A far superior model for forecasting using advanced predictive analytics has emerged, and it’s only a matter of time before it becomes commonplace. Between now and then, winners will advance, while those that stick to outdated models will fall behind.
Growing small and medium businesses benefit from advanced predictive capabilities in three overarching ways.
The first is the integration of the existing planning data for planning and forecasting. Many companies already use predictive analytics in marketing, product development, and other non-finance areas, but this data is not fully optimized for forecasting.
Functional and line-of-business leaders are seeking answers to questions, such as:
- What product-mix strategy should we use?
- How much should we charge for our products?
- What are customers saying about our products?
- What customers should we target?
- Who are the profitable customers, and who are the customers that we should not target?
Ultimately, all of these questions are about profitability and market position and should be considered before preparing a financial forecast. But in many cases this connection is never made, and like the stats that Beane and Brand used, the information is not used.
So, if predictive analytics is taking place anywhere in the organization, the first step toward a better forecast is connecting finance teams with business analysts. Generally, this takes an unacceptable amount of effort or isn’t otherwise possible with legacy applications or spreadsheets, and this is where technology can spark beneficial change. Once these groups can share and consult on data together, improvement starts.
Second, modern cloud solutions have automated much of the work of predictive analytics as a way of augmenting valuable human knowledge and skills. For example, Planning in Oracle’s EPM Cloud provides a no-coding experience for finance professionals as they review historic data, make a prediction query based on that data, and then apply judgement to the prediction for the ultimate decision. The ease and speed of cloud-delivered services like this have not been available before, so this is a new way for medium businesses to advance their forecast accuracy.
A third area where predictive capabilities helps is after the financial forecast through ongoing learning and process improvement. Many organizations choose to save prior forecasts. If they’re doing forecasts on a quarterly basis, they may save three or four forecasts with the idea that one day somebody will go back to those previous forecast snapshots and measure against actual performance to learn and improve for the future.
The challenge, though, is the above scenario is a manual activity, and it might or might not get done. With a business system with modern predictive capabilities, learning is always happening because collaboration among finance and non-finance business groups is occurring frequently. They are regularly sharing data, per the second area I addressed.
When these previously separated groups team up, they become aware of misalignment and gaps that they need to overcome to work together. Usually, the first thing they realize they need is a common language around the business and its assets and operations. Most of the businesses I work with use different words for critical planning inputs, such as full-time equivalent and profitability.
Consolidation of planning activities into a unifying process that flows into a comprehensive planning system uncovers disparities and then provides tools for collaborative teams to quickly make adjustments and move on.
Making Your Money Ball Move
Expanding predictive capabilities is a great way for SMBs to bring more precision to future planning. Adopting modern cloud financial platforms like Oracle EPM Cloud can be a catalyst for data optimization, innovative thinking, and the ongoing tuning of forecasts toward a future state of best-possible accuracy.