CFOs Need Mavericks to Boost Data Analytics
- September 29, 2018
We are moving from an era of data scarcity to an era of data abundance. This is being driven by the Digital age we live in. But data is not made equal, some of it is far less useful or trustworthy than other data. This is a huge challenge for society and our education systems. Zooming in on what I know best – the finance function in corporations – big data can become a curse, if you allow yourself to drown in it. To turn it into a blessing, CFOs need to change a few things, starting with the kind of talents they hire …
We live a data paradox. We feel submerged by data which is all around us and omnipresent, whereas those data could make our lives so much easier are much harder to find and identify. The real problem is most data is not useful. You need to figure out where data can provide you with real insights instead of bringing more complexity or setting people going off in tangents. This is not an easy task. As a CFO, I try to stay focused on the basics (revenues, costs and profits) and build up from there starting with simple data analytics that gives me more clarity on the market opportunities, the go-to-market costs and the different scenarios to allocate business profits. It is only when I feel I have a strong understanding of these scenario’s that I then move to look at big data outputs to try and validate the scenario’s that have been identified.
I know this is easier said than done. Another good ploy is to surround yourself with the right people.
I recently stumbled across an article in Healthcare Finance, referring to a survey by WorkDay, which perfectly summarizes the promise of big data for the finance functions: data-driven decision-making (rather than intuitions) that makes the CFOs and their teams more resilient and intelligent, delivering quality insights and strategic advantages for the whole organization. But the report also indicates that many corporate finance functions are still unable to deliver these insights, due largely to:
My previous blogs were about point 3 and, indeed, the need for partnerships, specifically between the CFO and the CIO, which is key to aligning on the right data sets.
Regarding point 1, I do not think that access to non-financial data is a problem, as such. The difficulty is to get data in a trustworthy format that you can easily embed into your own CFO story. There are so many different templates and subsets across departments and business units. This is also where a strong CFO-CIO can help getting everyone in tune.
Where I think CFOs have still a long way to go is, indeed, point 2 and the access to skills.
To refresh the knowledge inside your finance department, I strongly encourage you to search for less traditional profiles. Besides accounting and finance champions, attract some nerdy data scientists and make them feel comfortable in your team. They are so scarce on today’s market that you had better offer these mavericks a motivating challenge, as well as a suitable infrastructure to start getting value out of artificial intelligence. Otherwise, like in this testimonial, they will not stay long in your team.
The infographics here illustrate that data scientists and business analysts are like chalk and cheese. They do not naturally work together. But it is your task, as CFO, to show leadership and make the best of both worlds.
A tactical tip? Introduce some job rotation so that each group of skilled people understands the benefits brought by the other one. There is a kind of trade-off to be found here.
If you keep data scientists and business analysts separated, the risk is that your own exploitation of Big Data may go too behind the wall, disconnected from the business realities. Data science just for the sake of it.
Conversely, if you keep them together all the time, you restrict the power of data scientists to think out of the box and to provide real insights once their job is combined with the skills of the business analysts.
In conclusion, there is not one silver bullet to make your finance department smarter and more resilient, but investing in a good mix of new talents while increasing the data analytical skills of the existing teams… is definitely a sound decision. Though, I must confess, it is based on my own intuition and experience more then on machine learning.
Always happy to read your comments!