Analytics are not a new topic for Treasury Café because they are critically important for a finance and treasury organization to think about. Why is that?
Finance professionals know how to work with numbers and data. It is a large part of the educational curriculum for one very good reason – it is also a large part of the job! The finance professional is working with mathematical and statistical concepts from the first day of their career to their last.
Depending on their level, this extends into statistical methodologies, models, concepts, and processes. Financial options may be priced based on the statistical properties of a normal or lognormal distribution. In other circumstances, they are priced using Monte-Carlo simulation models, representing any number and variety of statistical distributions.
Often times, statistical analysis of financial market data is required in order to develop the models and the parameters under which the simulation is performed. Alternatively, the finance person is directing others (such as banking or consulting partners) who are performing this analysis.
In either case, they know enough in order to accomplish the mission at hand. In some companies, and in some industries, the finance group may be the strongest when it comes to these analytical capabilities.
The finance group is generally the one that is producing forecasts and simulations for the organization’s future. If we want to know the answer to a question such as “what is the range of cash flows we are going to generate 5 years from now?” it is likely someone in the finance group knows what this is. They will also be able to tell you the balance sheet and income statement impacts as well.
Newton’s law says that something at rest remains at rest, unless something with enough force changes the inertia. In financial forecasting, we call those forces value drivers. Modeling value drivers is often the critical piece of the puzzle, and this usually entails statistical analyses, testing, and development.
A Pumpkin Tale
For example, last weekend I took my kids to the pumpkin farm since it is Halloween time here in the US. At these places we buy pumpkins, jump on “magic pillows” or “bouncy houses”, watch pig races, find our way out of corn-field mazes, ride ponies and camels, go on hayrides, and other similar activities.
From the pumpkin patch operator’s perspective, the majority of their revenue occurs during a 6-week period of the year, and the bulk of that on weekend days. For those who don’t know, this 6-week Fall period in the Midwest US can be 30 degrees Fahrenheit or 90 degrees. It can be sunny, or it can snow. It can be summer-breezy or bone-chilling Artic-galey. For the pumpkin patch business, weather during these 12 days is a value-driver!
In order to analyze our possible future as pumpkin-patch operators, we would look at weather patterns over a long enough period of time in order to be confident (from a statistical viewpoint) that we have captured the full range of potential weather environments. We would then develop a model out of this data in order to predict a range and probability of different future outcomes of the weather, and its subsequent impact on pumpkin patch attendance and buying behavior. The ability to do this lies in the finance staff’s statistical abilities.
A Seat at the Table
Finance has native talents in the analytics arena, and it certainly has a strong interest in the outcome of analytic efforts. Therefore, it is critically important for finance to have a major voice in the process.
The location of analytics talent will vary by company and by industry. As an example, in some companies or industries marketing rules the roost, and in these situations it likely that the marketing support group has analytics prowess, and they are busy linking web traffic, twitter word counts, in-store visits to income level, area of residence, interest and other data-base elements to arrive at a revenue forecast.
That is fine - the more analytics the better - but if the capability resides in that area then it must be shared with finance and treasury. If not, we run into the silo problem, which we touched on in “The Matrix”.
This is the problem we want to avoid. We want to be a part of the process so that we may leverage the talents and knowledge for the good of the overall organization, and to provide the finance and treasury team enough opportunity to grow and develop.
As the era of Big Data continues, this will become more and more necessary.
· Can finance and treasury safely ignore the Big Data phenomenon? Why?
· Will Big Data significantly impact the way your organization runs its business?
· What are the finance and treasury staff’s analytic, statistical, and technology capabilities in your organization?
· Where is the “natural home” for your organization’s Big Data capability?
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