Best Place to Start Your Big Data/Data Science Journey is…Finance?
If you work in Finance you love MS Excel. Don’t lie. You know you do. Many businesses, very big businesses, have Excel models they’ve grown up with. We never had a better way to work and Excel was the best way to put together our assumptions and model to our heart’s content.
But these models can be very manually intensive to maintain and aren’t truly auditable. Or the person who built it doesn’t work there anymore and no one knows how to modify it.
Don’t worry, you can still use Excel. But what if I told you that you could process millions of rows of data in seconds without Vlookup merging your many datasets together or, dare I say, without using MS Access? At EMC, we use Pivotal’s Big Data Suite. It’s light years ahead of what we were doing with Excel.
So if you’re starting a Big Data project at your company, Finance should be one of your first stops. They have tons of models leveraging disparate data sets and they are used to assisting in many major business decisions. What better way to create a Return On Investment (ROI for you Finance people) for your project than to do it with the Finance team? You know, the ones that write the checks?
If you are in Finance or support Finance the dust is just settling from rev 106 of the 2016 Plan. Here are a few ideas. Some may not apply to your specific business but I tried to call out where you can get some quick, easy wins, secure funding, and grow your Big Data program.
- Install base – Whatever’s happening to your product(s), this is a major driver in your Financial Planning for the current and next 3-5 years
- Revenue and/or Bookings – Sales predictions can be leveraged with business forecasts to see if there are gaps or blind spots.
- Staffing models – Every Finance team has a staffing model if they support a people business. Staffing can be ~70 percent of their spend. Anything you can do to improve hiring will help the bottom line. Similar to Sales, you can use the prediction in combination with business forecasts to see if there are gaps or just to challenge business assumptions in their forecast.
- Skill Gaps – Similar or combined with Staffing, make sure you are hiring the right people in the right locations.
- Service Costs – Which products are more costly to support and why?
- Accrual Balances – Accrual example: some companies have to keep a balance due to product warranty. I would put a big star on this one; reducing an accrual increases bottom line profitability. **Great way to fund your next project**. My only caution is the Accrual could increase with your new model. But most Finance people are very conservative with their assumptions so this shouldn’t be an issue.
It’s a safe bet your Finance team already has a model that does this in Excel. The Big BUT is their models likely don’t scale because they are built at a high level and are full of assumptions. Don’t get hung up on the term Data Science. Sometimes you don’t need a Data Scientist, just the ability to pull together large disparate data sets.
There will always be an opportunity to improve a model with a Data Scientist but bringing together disparate data sets at the lowest level can dramatically improve your models. This saves time, reduces human error, and increases business visibility masked by assumptions or product mix shifts within a hierarchy and auditable to the lowest level.
Whichever project you choose, make sure you size up the Dollar Impact, Risk Reduction, Efficiency Gained and get buy in from the current Excel model owners to work with you.
I will put another gold star on “get buy in from the current Excel model owners” because nothing kills a project more than a group who doesn’t want the help.
Once you get buy in, don’t just replicate the Excel model. Think bigger and expand it to the lowest level then build it bottom-up. If you don’t you’re just automating a flawed process. This still has value but you are leaving a ton on the table and it may not be enough to fund your next project.
I hope this helps. I really believe Finance is a great place to start your Big Data journey as it has many models. Finance folks are data-driven by nature and their models assist in major business/ financial decisions.