Written by Christopher Argent, Generation CFO Founder
There is a data skills gap in Finance teams today, as CFOs are scratching their heads how to fill this gap while delivering the ever-widening demands from the business.
In a time-poor world, we need to get smart and focused.  The above 2×2 matrix is taken from a much-debated article in the Harvard Business Review that gives us a method to move forward.
It presents an analysis of what we should be learning and planning to learn, and what should ditch (ignore or browse).
The below is the extended matrix for Data Skills learning, which I have added to with my take on what we should do, can do, and not do.
data skills for finance
As a busy finance professional we need to view this matrix through a new set of eyes, data champion eyes, strategic partnering eyes and make educated investment decisions based on what will offer the most value to the businesses we work in.
Ideal world… what we should do.
Assuming the investment will bring the returns… learn everything in the “learn” and “Plan” box… and back to the real world!
Real world… what we can do.
Focus on the “Learn” box, while partnering with internal and external experts on the “Plan” box.
I suggest this as an approach as Finance Professionals simply do not have the time to acquire these “skills” which in reality are more like different careers eg: machine learning is not a case of downloading Python and cracking on with a “Friday afternoon” project, it requires deep knowledge of statistical models, mathematics, data management, data cleansing, and a programming language.
So what do we focus on?

  1. Data Science, in this case, an understanding of the subject and the art of the possible.
  2. Data Visualisation, time to grow up and take this seriously, we may be happy in our graphs and charts, but the world has shifted, and business expectations are possibly ahead of your own output.
  3. Business Intelligence, arguably a combination of the above outputs, but in short a robust approach to reporting that delivers insight to the right people as required.
  4. Last but not least, and my add-on… Robotic Process Automation. I have added this because so much of what we do needs process improvement, and RPA can help to speed up an improved process and create data volumes to be used by us in 1, 2 and 3.  And by the way, RPA learning is not as hard as the consultants want us to think, it a bit like a macro Excel Macro working across all apps not just Excel, and a foundation level can be achieved in days.

So where does that leave the “Plan” box learning…
Well, in reality, it is the “recruit” box for most companies as you will struggle to upskill in this area, and certainly not from incumbent finance professional resources.

  1. An intro course on Data Science (in the “Learn” box) should give you enough understanding to work with data scientists, engineers, mathematicians, and your role in a data project, but you will not learn machine learning, which is a chapter of artificial intelligence and I suggest you steer clear of coding.
  2. “Information Security” is something that anyone working with data should learn about, but if you are in a large corporate, this will be a case of reading someone else’s InfoSec policy and compiling.  Operationally, your IT team should be telling how they are managing this risk.

Conclusion and offer…
So there you go future of finance boffins, a plan, a bit more focus, easy, right?
So why not start today, and if you are interested in learning more about how to run a finance data project, which is possibly the best way to upskill and learn the above topics while delivering something of value, do reach out to me here.