Data is becoming a key asset of today’s world. Most valuable companies claim to be data-driven, decisions are data-based, and our business strategy needs a corresponding data strategy to enable success.

What is meant by data in this context is, however, not the raw material but rather the information or insights that can be derived from them. Unlike oil, where the transformation from the raw material to the final product is straightforward and relatively standardised, data as a raw material can be transformed into an extremely useful or completely useless product.

Don’t worry. The following text will not discuss sky castles full of Artificial Intelligence, Machine Learning, or data science.

FP&A Reporting in a global company

Let’s solve once and for all the question of how to design management finance reporting for a global company that operates in many local markets and wants to automate the revenue, P&L, expenses and other key management reports.

We want to deliver one version of the truth reports that are shared across the world and free up time spent in local business organisations for business partnering and more valuable activities. At the same time, we need this automated reporting to be relevant for all the countries, regions and functions. Only then will local teams not be required to produce reports themselves, and full benefit will be achieved.

The vision is simple, and it is surprising how many organisations still struggle to achieve it. Why is that? Let’s illustrate the challenge with a few classic examples:

• One country has 80% of its business in areas outside of the global growth focus, making them reported as “other” from a corporate perspective;

• Another country decided to structure business units in a different way which better fits them but is again not compatible with the corporate view;

• Although corporations typically operate in around 100 markets, it often is just a few that executives are interested to understand in detail;

• Yet even a tiny market has its specifics and needs accountability and budgets assigned to be efficient, as well as to ensure proper governance and controlling.

Both Global and Local FP&A teams have a role to play

So, on one side, we have a desire in a global board room to see every country in the same way, digital tools that ask for harmonisation and on the other side, local business units that need their local operating model reflected for the reporting to make it useful for them.

As in life, there is no one size fits all. Everybody is different, and it is only when we embrace diversity that we as a society (and a company) thrive. Pushing a country to comment on their revenue performance based on a report that consistently presents 80% of their portfolio as “other” does not make sense. It also makes little sense to ask a global executive to dwell on specifics of a country that accounts for less than a rounding error.

The key to solving this puzzle is to understand which capabilities are best managed centrally and which should stay in the hands of local teams.

Master data management, core data model management, and data quality management are capabilities that all lead to one version of the truth data sets. Using the analogy of raw materials from the beginning, this is the oil refinery that is always needed in the process. Interestingly also the data visualisation can be efficiently delivered centrally, similarly to consumer product branding.

It does not make sense to have every finance team developing management reporting end-to-end, where local finance teams add value is mastering local business specifics and delivering insights to support business objectives.

Capability-based definition of roles and responsibilities

This suggested the split of responsibilities ensures focus on everyone’s strengths:

Figure 1

From an architecture perspective, we must clearly draw the line between core transformation (e.g. accountability unit = business unit/function, management accounting and other standards, exchange rates, planning scenarios) and customisable transformation (product structures, department structures below accountability unit level, etc.).

This will allow meaningful customisation and enable powerful reporting visualisation, whether delivered centrally or locally via self-service dashboarding tools.

Summary

From an investment perspective, I suggest focusing on a rock-solid data foundation with an efficient data model. This is not easy, neither cheap nor fast. But if done well, we can finally turn the page on F&A management reporting and fully focus on the new chapter of data science and Artificial Intelligence, which by the way, both need this data foundation as a necessary enabler.