Data Governance: Why Mindset is just as Important as Dataset

Data Governance: Why Mindset is just as Important as Dataset
The Manufacturer March 2021
By Jonny Williamson

It was in her previous role at a Dutch multinational dairy cooperative that Ieva Langenfelde saw the correlation between well governed master data and its impact on daily business transactions.

Now at The HEINEKEN Company, Ieva is part of the team helping to unify a federated organisation comprising 80 operating companies and 45 different ERP systems. The Manufacturer sat down with Ieva to learn how.

What does data governance mean to you?

Ieva Langenfelde: Data governance is about data ownership so that someone is able to make a decision regarding shared data in such a way that it’s fast, the process, roles and responsibilities are clear and the decision is in the best interests of the company.

The inability for large organisations to make fast decisions when there is data conflict or having to spend months deciding a course of action leads to frustration, additional rework, loss of potential revenue or unsatisfied customers. And it all often stems from master data, a fact that is sometimes overlooked.

Manufacturers will often have Business Process Owners responsible for a particular function or department, such as sales, manufacturing, logistics, supply chain. These BPOs are responsible for governing their process and whenever a change request is logged to alter that process or to escalate an issue, these are the people who assess, configure and implement it – or not.

Similarly, IT departments typically have Product Owners who govern their tooling and systems and any change requests from the business to adjust them. That’s typically not the case for data.

Data is collected once but used many times over, it’s created here but used everywhere, with different teams accessing and using it for a variety of reasons. That’s where conflict arises, with various functions or sites unable to agree on a way forward over something as small as a unit of measurement.

Just like process streams have BPOs and IT has Product Owners, data needs Data Owners. Therefore, you need data governance.

Who should that person be?

It needs to be someone who can understand what the decision is about, who isn’t too disconnected from the coal face; but at the same time is high enough up the chain to take control and is trusted to make the right decision.

It may be that this person is responsible for a number of different facilities in multiple locations, which is why they should be supported by a team of local or functional Data Advisors. This way, Data Owners are able to receive the local advice and impact assessment when an issue arises.

A large global organisation may have +/- 10 global Data Owners, supported by potentially hundreds of Data Advisors. To manage it all, and to channel and drive the surfaced data conflict resolution processes, I’d suggest having a dedicated Global Data Governance team who drive it until completion.

For the rest of the organisation, it’s crucial that you define and implement departmental escalation tracks and that people follow them. Yet, processes such as these are often missing in many organisations.

What’s your role at Heineken

I am part of one of the largest projects in HEINEKEN currently, implementing SAP S/4HANA and connecting our 26 European operating companies [OpCos]. We’re aiming to harmonise about 16 different systems, as currently each OpCo has its own way of working, of recording and using data and their own processes.

My role is to ensure that data governance and maintenance is implemented in a way that data can travel from the source to the target and fits into the target data model (aka one common language).

It’s also about ensuring that the data policies, processes and standards remain synced once the project goes live, in that sense it becomes more about data maintenance and quality capabilities as well.

It sounds like data governance is similar to continuous improvement.

Absolutely. Data governance is not a project. You don’t implement it and then you’re done. It’s never going to stop. Its creation can be seen as a project that is delivered over a specific timespan, but if it doesn’t become business as usual, if you don’t actively keep it going, then at some point it will run out of steam.

What stage has the project reached?

We are 18 months into a three-year project. We have focused on finance initially because the goal is to have standardised and simplified financial reporting across Europe. The benefit of starting here is that finance is already highly governed and largely digitised.

From my previous experience, I know that procurement is also a popular area to standardise and harmonise processes. For example, if you have multiple sites all purchasing supplies from the same vendor, knowing that and combining the orders delivers tremendous purchasing power and economies of scale.

What are you hoping to achieve?

We have a shared service centre in Poland and even though actions are taken on a central level, the different OpCos are still serviced separately. What this project will bring is more finance process standardisation, but in order to standardise the process you also need to standardise the data. It will bring much greater visibility, transparency, efficiency and ultimately, profitability.

It will also allow us to assess and support the performance of our OpCos quickly and accurately in a single report, where the one common language will ensure the columns and the rows will be universally understood, whether we’re looking at Greece, Italy, Germany or Russia. This is about providing insights to drive smarter decision making.

What elements are commonly overlooked in this sort of implementation?

If people in the organisation across all levels – operational, tactical and strategic – aren’t ready or don’t understand what’s taking place and why, then the change isn’t going to work.

Demystifying data governance and showing clear business benefits are key. For anyone who asks, ‘What’s in it for me?’, you need to have a prepared ‘stump speech’ that clearly and simply explains why this is happening, how it will work, the roles, the change impact and the roadmap.

Once implemented, what have you put in place to maintain momentum?

It’s easy for people to be so focused on their own role or department, that they don’t find the time to look up and see what’s happening around them. One of the roles of data governance is to bring the various different Data Owners together and present what they are working on to each other.

These interactions also reveal the data elements various parts of the business are using, and there are often hidden overlaps where departments make use of the same information but in different ways.

With this knowledge, I can then assess the quality of that data, whether it’s well-maintained and whether people understand how to maintain it, and if not, that would be something I would work on improving, for example, by issuing training materials.

Engagement and good training are key in order to ensure people have the knowledge and information they need moving forward. Quality training material can be challenging to produce because it needs input from different teams all working together to create something user-friendly, easily digestible and intuitive. However, it’s worth spending the time to get it right as it will save a lot of worries in the future.

What does the future of data governance look like?

Governance will only become more relevant and more necessary. Almost every organisation is undergoing some form of digital transformation and what that really means is digital solutions supported by data-driven processes with trustworthy information.

Master data is therefore a critical enabler for these transformations to take place. Having clean master data requires data quality, and data quality relies on rules and standards which need governance and ownership.

There is a lot of talk about data-driven decision making, but many don’t yet know the right data that drives their business. If they do, then is the data reliable? What is the quality standard? Do you trust it? If you do, then you’re probably Amazon. If not, you probably need some governance.