Data-driven finance: the burden of the head start?

Many companies think their finance department is ahead when it comes to working with data. But is that actually true? In many cases, the finance department turns out to hardly work in a data-driven way at all. In this blog, I tell you more about data-driven working within finance.

A finance department without data is simply unthinkable. In many companies, it is precisely the finance department that has been working with data for years, while the rest of the organization lags miles behind in its use of data. That doesn't mean, however, that the finance department works in a data-driven way. In fact, in many cases the law of the handicap of a head start kicks in: because finance started working with data long ago, it is costly and complex to bring this situation up to the latest state of the art. And why would you? With Excel combined with your accounting system, you have everything you need to meet the reporting requirements, don't you? Undoubtedly. And the gain for a finance department in working data-driven doesn't usually lie in the quality of reporting either.

Defining working with data

First, a matter of definition: working with data (however reliable and accurate) is something different from working data-driven. When working with data, or 'data-informed' working, you use available data to support your decisions, and you try to steer your processes by analyzing the available data. If you truly work data-driven, you go one step further: all regular decisions are no longer made on the basis of data, but executed (automatically) based on logical choices that follow from the supplied data. With data-informed working, you make every decision yourself, with the help of data. With data-driven working, the decision becomes part of the process, and you focus your attention only on the exceptions to the standard process.

An example: a company aligns production with sales. In a data-informed organization, management will, on the basis of higher-than-expected revenue, decide to increase production and hire an extra production employee. In a data-driven organization, the software analyzes whether the increased revenue still fits within the calculated productivity, and whether it is a cyclical increase. This automatically checks whether increasing production really requires an extra employee, or whether production capacity can perhaps be shifted from another product.

In the first scenario, the basic decision lies with management, and besides a number of facts, a good dose of intuition will play a role in the decision. In the second scenario, in the ideal situation management may not even be bothered with the production question, because it can simply be handled within the existing parameters.

The step towards data-driven working

At the time most finance departments started working with data, this was still a thing of the future. Having data available for reporting was the first step, and although that succeeded well in many cases, the step to data-driven working is still big for many companies.

It is therefore important to be honest about the business case: if your emphasis within finance is on record-keeping and reporting, there is not much to be gained by making the switch to data-driven working. Of course you can save some time and effort by automating those reports, but that doesn't deliver truly decisive benefits.

The role of finance within your organization

To really benefit from data-driven working within the finance department, you need to shape the role of finance within your organization differently. Reporting and accountability are of course an integral part of finance's tasks, but it is at least as important to use finance as a navigation aid for the decisions ahead of you. A data-driven finance department can help an organization identify opportunities and bottlenecks in processes, help determine risks for new initiatives, and warn about trend breaks long before they become visible in historical data. Investing in a data-driven finance department gives your organization a sharper view of the future and the decisions required, without needing an army of analysts to crunch everything. That transition is, precisely because of that head start, not always easy. The organization's reporting needs can't simply be put on pause, and the fact that the books must remain accurate is also undisputed.

So what, then?

It is important to describe in advance which applications you see for data-driven working within your finance department, and what is needed for them. By starting with such a 'data strategy for finance', you get a picture of the wishes and possibilities within your data landscape. This prevents a scenario in which you only start to notice benefits once you have already replaced all the tools within your finance landscape. You therefore avoid investing in tools you may no longer need.

As always, it is important to make the transition to data-driven working feasible and scalable, so that data-driven working within finance doesn't have to be a cost item. A good data strategy helps you clearly outline the benefits of your new approach, so that you take the right smart steps in the right order. This is how you work towards a truly data-driven finance department.

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