Investing in data quality is a tedious job, but worth the effort

CEOs are much better able to manage their organisation when that data is up to standard, particularly when they are confronted with major unexpected events such as the COVID-19 pandemic. Moreover, it saves a lot of money. It's therefore well worth investing in that 'tedious job'. 

Different definitions cause confusion

You could say: one push of the button and an organisation knows how many people it employs. However, it’s just possible that such a simple question will yield several answers, for example because different definitions of what constitutes an employee circulate within the organisation: are you talking about people or FTEs? Is an FTE 36, 38 or 40 hours per week? Do you take the average during a period or the headcount at the end of that period? 

Inaccurate or missing data

Organisations are therefore not always able to answer relatively simple but important questions on the basis of their own non-financial data. This is because clear definitions are lacking, or because data are recorded inaccurately, in the wrong format or not at all. Or because systems that have been connected over time don't communicate well with each other. 

Why are non-financial data so important? 

• You can't coach based on the result

Non-financial data are very important for managing organisations, with the financial data being the outcome of that management process. Compare managing based solely on financial indicators with a coach shouting '2-0, 2-0' along the sideline. That might well be the desired result, but it doesn't much help the players on the field. Better data lead to better decisions. 

• Responding quickly when engulfed by a crisis

The response of businesses to unexpected events – their resilience and agility – is almost always about information. How quickly are you as a business able to obtain information from which you distil insights and on which you ultimately base decisions and actions. 

During this coronacrisis, we have seen that many businesses did not start looking for information and a solution until the crisis had engulfed them. They didn't have the answer ready or didn't know where to retrieve certain information. Businesses, for example, needed a very quick answer to the question of how long they would survive. In most cases, the answer did not come at the push of a button. 

• The promise of data

The great, almost magical promise of new technologies and brute computing power potentially enables finance departments to accomplish things they thought impossible just a few years ago. 

Enormously powerful analytics tools distil ready-to-use and valuable information from oceans of data and can, in principle, provide information from which it may not yet even be apparent whether it makes sense. However, anyone wanting to head for such a promising future must first ensure that the available data and data structures are consistent and reliable.

• It saves money

Flexible and resilient data structures with corresponding processes and systems are not yet widespread. The finance departments within organisations are therefore involved in a lot of time-consuming and manual work. Reliable and consistent data simply provide efficiency benefits, and therefore money. This often involves dozens of percentages of all work within finance departments. 

Improving the quality of non-financial data is time-consuming, intensive and often results in laborious debates between many people within an organisation, because it affects almost every department. Moreover, it is typically one of those investments whose usefulness and return are not immediately apparent. In many cases, it is not clear where the responsibility lies for solving this complex problem.

It's a job almost no one feels like doing, so nothing gets done. Or the focus is on improving processes and systems, which is also important, but that will not solve the problem of data quality: a new system will not deliver on its promise if it runs on incomplete, ambiguous data.