This is post #5 from a 12-part blog series that SAP commissioned from finance expert Steve Player, who runs his own practice, The Player Group and also heads up the Beyond Budgeting Roundtable in North America. This series explores how new technologies such as cloud computing, mobility, and in-memory processing of Big Data are transforming planning, budgeting and forecasting best practices.
A good friend of mine makes his living as a writer. He is very good at what he does. Two keys to his success are his discipline and the fact that he repeatedly uses the methods that he finds successful. This applies to his personal life as well as his professional one. I learned this when I once infringed on his weekly poker night.
My friend confessed that he had started a regular poker night in college to make sure he was never broke. He made sure that he had enough other players in the game that were perhaps less skilled (or less disciplined) than he. As a result he could typically count on leaving the night with more than he had started. I wonder how many similar games are played every week.
The best poker players seem to understand what the other players are holding. Their skill and experience combine to rapidly simulate the odds and likely outcomes. When watching these pros on televised poker, even the casual observer can see the published odds because the TV crew has computed all the possible outcomes and neatly summarized the odds of winning.
I see many advanced planning departments operating like skilled and disciplined poker players. The key difference is that they are using technology to find a disciplined way of repeating successes. Skilled planners use driver-based planning models that allow them to run rapid scenarios. They use these tools to see the odds more clearly. They have greater visibility in steering their organization.
While calculating the odds of a winning poker hand has a lot of data, it is a simple simulation. But it provides a great illustration of what in-memory processing is doing to Big Data on a much larger scale. With in-memory processing, financial planning departments realize that the power to do rapid, detailed risk assessments is at their fingertips.
Financial planning & analysis teams begin by building a Monte Carlo simulation to analyze risks and possible outcomes. Models are built to test all possible outcomes by substituting the range of possible values for key factors. The model is run over and over again summarizing results. Thousands of calculations are then summarized to help management understand the range and probabilities of possible outcomes. In-memory processing is moving Monte Carlo simulations to the mainstream of planning best practices as the calculations run thousands of times faster.
Powerful simulations are being conducted in the oil and gas industry in finding and completing oil field drilling and production. They are also being used to evaluate the potential impacts of price fluctuations on the entire transportation sector. Retailers are simulating the expected results of changing price levels. Insurance companies are examining potential costs of catastrophic storms to make sure they are setting premiums at the appropriate level to cover their expected risk.
Frankly, planning departments that are using in-memory computing to harness Big Data gain a tremendous advantage over those who do not. Their investment in the tools to improve their skill and discipline has them counting on leaving the table with more money.
Let me know what simulations you are running and how you plan to use simulations to see more. Next time out we will examine “Better and Faster Rolling Forecasts: Expand Predictive Abilities with Unstructured Data.”