What is the data & analytics maturity of your Finance organization and why it is important to know this?

In earlier articles I have described the importance of a foundation with an information-centric data hub to store data from operational and financial processes and leverage this to provide forward-looking and actionable insights by the Office of Finance. Often, I got into discussions about the availability of the ‘right’ data and the quality of the data. Recently, I was made aware of two other important aspects to be investigated, when implementing a new unified Enterprise Performance Management & Advanced Analytics software platform:

1. How mature is your Finance organization in collecting and providing data and information in a structured way?
2. How do you transfer the data from creation to value-adding actions with good governance?

Below are a number of characteristics described of how you can determine the data and analytics maturity of your Finance organization (where your Finance organization can be in different columns per item):

When you have an impression of where your Finance organization is right now, you can then determine what your ambition is for improvement. A roadmap can be created with the steps to do in order to achieve the desired level.

As a next step it is important to determine how data from the original creation of the transactions needs to be processed to the users for analysis and value-adding actions. A so-called Data Value Map can be used as shown below. This is a way for people to brainstorm with each other to ensure that a joint vision emerges about the use of data and how this can be unambiguously converted from creation to use.

It is important here to ask at every step of acquiring, integrating, etc. what, why, how, when and by whom activities are being done and also to ask what needs to be started or stopped and what can be improved in this chain.

The above steps are sometimes forgotten or, at the very least, not carefully dealt with when starting a project to introduce a new system. What happens is that in the new system often is copied what was done in the previous system. For the use of e.g. machine learning, it is obvious that this second step needs to be carefully considered, otherwise you will soon find out that outcomes are unreliable. It needs to be ensured that sufficient granularity and quality of data for statistically reliable outcomes is available.

My advice is to determine the data literacy & analytics maturity of your Finance organization first and to ensure that you have a plan to bring this to a sufficient level, before you can take on the role of a strategic business partner.

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Drs. Marco van der Kooij is an independent consultant at ForSight Consulting and has been working in the field of enterprise performance management and business intelligence for more than 25 years.