“Organisations don't deal with data in a uniform way”

The importance of good data management

Organisations that manage their data effectively don’t only have higher turnover, they also outperform their competitors. Nevertheless, data management and its governance is underestimated. Two PwC experts, Alexander Staal and Mark Brouwer, discuss what makes good data management so important, and how you can create a good foundation for quality data.

Meaningful information from data

Alexander Staal, finance transformation leader at PwC: “The world is growing more volatile. The dynamics in which companies operate are becoming more complex and things are moving faster. Where directing the organisation is concerned, that means being quickly able to anticipate on developments in the world. And stakeholders also expect more and more information, about ESG for example. The only way to achieve all that is to create meaningful information from data.”

According to Mark Brouwer, data management expert at PwC, high-quality data management offers many benefits for an organisation: "It leads to efficiency, it allows the Finance function to report faster, and providing the right data ensures the organisation's compliance. And with the right data you have the flexibility to make faster adjustments and choices.”

In practice, however, PwC finds that companies aren’t really “data management-minded”. Staal: “In general, organisations are in fact thinking about this issue. They’re becoming increasingly aware of the importance of good data. But what we see in more and more organisations is that the data landscape isn’t uniform. That's partly because systems and processes aren’t very sexy. And obtaining quality data requires an incredible amount of work; the processes involved take a lot of time.”

Responsibility and ownership

According to Staal, the root cause of the lack of a uniform approach to dealing with data is that in many organisations there’s no clear owner of the data. “It's everybody's problem, but at the same time it’s nobody's problem. That makes it hard to find a solution.”

Brouwer: “The key is to assign responsibility for data management. We've noted that organisations are in fact working on data management, but in a variety of different places within those organisations. You therefore need to assign the responsibility to the executive board or the management. If the board doesn't care about data, then the data owner – the CIO or the CFO – won't care about it either.”

Clear structure for good data management

Laying the basis for good data management starts with having a clear structure:

Assign ownership at the highest level, i.e.: “executive sponsorship”.
Know what the focus is: is the data needed so as to tap into new markets or is data quality needed to meet reporting obligations?
At the start of the process, select the business domain in which efforts will lead directly to measurable results

Different definitions = inconsistent data

The bigger an organisation, the more data it has to process into useful information. According to Staal, the question is whether an organisation can handle that enormous amount of data effectively. “That vast amount of data presents a huge challenge for the CFO or CIO. And the data is often incomplete and inconsistent. It’s assembled from a variety of different sources that aren’t based on the same definitions. Just try getting meaningful information from all that!”

According to Brouwer, the Finance function has its hands full straightening out information from different sources. “The Finance function has traditionally focused on processing financial data, but now it’s increasingly also processing information from other fields, for example operational data. When you start including that data in reports, you need to report under exactly the same definitions as for financial data. And that's a challenge.”

As an example, Brouwer refers to a company that PwC helped with its international production chain. “We made sure that all components of the products have the same definitions in all countries. That avoids buying the same product half a dozen times and being unable to achieve economies of scale.”

Staal says that another area in which definitions can vary widely is business turnover. “You regularly see that the definition of turnover differs from one department to another. So is turnover gross or net? Is it turnover minus volume discounts or is it turnover in which the direct marketing costs have already been deducted? It depends on what the organisation considers to be the definition of turnover. And even if you already use the same definitions, you also need to see if the foundation has the same structure.”

According to Brouwer, the solution for aligning definitions is to be found in the interplay between the data owner and the data user. “It should actually be the data user who sets the definitions that the data owner must comply with. If you get that right, then you’ll have a process in which the definitions are properly aligned and that delivers quality. The user will then know what they’re getting and the owner of the data will know what to provide."

“Data governance” to keep all that data in check

Staal agrees that “data governance” is important. “Data may be consistent initially but then get weaker again after a few years. By following a set process,” he says, “with the right approval steps, with decision-making and a clear division of roles, you can at least keep control of the consistency and completeness of data.”

Brouwer adds that “because more and more data is being stored, there’s also a kind of race that you have to win. The more data you store, the harder it is to manage it. That involves processes, procedures, and automation. You need to consciously consider how to structure the IT landscape so that the data can be organised properly. Just storing more data without thinking about how that volume of data affects the IT organisation and processes means having to start all over again every three years.”

Data management quality requires a long-term effort

Ensuring the quality of your data management takes time. Staal says that for big organisations with a turnover of a billion or more, it can easily take a year to harmonise all the systems and processes. “And for organisations that operate internationally, it’s even more complex. Legislation and regulations are different, IT systems work differently. In the case of an international takeover, for example, you may need to deal with all kinds of separate IT systems. On the other hand, you also have companies in the Netherlands that have grown through acquisitions and acquired a lot of local parties. Problems then arise because those parties haven't been harmonised, and the definitions for the processes haven't been aligned either.”

A good business case removes reluctance to address data management

According to Staal, companies are hesitant to tackle data management because of a lack of understanding. “They don't know in advance what they can gain from it. But you can build a good business case around data. Ultimately, an organisation often finds itself in situations where there is inconsistent data, with a whole lot of people working to find an explanation in that volume of data as to why there are so many differences. You can quantify it by looking at what it will cost to solve the problem, and then at what you can save in terms of the time you are now spending on improving the quality of the data. By doing an activity analysis, you can see how many hours employees spend on activities that don’t add value.”

Listen to the podcast

This interview is part of the “Future of Finance” podcast series, in which PwC experts give their views on current topics such as the digital transformation and data management. You can listen to the full version of this interview here.