Data strategy expert interview: from vision to practice

What is your definition of a data strategy?

The objective and content strongly depend on the client. If the need for a data strategy comes from IT, the focus is on architecture. A Marketing Manager will be more concerned with realising a 360-degree customer view, while a Logistics Manager wants to be able to deliver more efficiently. So, depending on who determines the data strategy, there will be a different focus point. This also makes the definition different for everyone. For some, it is about data analytics, while for others the focus is on data management.

Koen has worked for various organisations on the development and implementation of a data strategy. He talks about his approach and challenges.

Can you talk us through one of your data strategy projects?

At one organisation, the initial question was: 'Can we take advanced analytics more seriously in the organisation?' They did have internal labs that conducted experiments, but this did not amount to much. Yet there was the realisation that there was a lot of potential in it.

We started top down. We interviewed senior management to collect use cases for advanced analytics. These managers then pitched their own use case in a Dragon's Den format to the board. For each case, we put together a team, but it turned out that they were unable to properly get to work. The necessary data was not available, the data quality was not up to par, the definitions used were unclear and models did not come into production. The people were very much willing, but the organisation was not set up to quickly retrieve data and put models into production.

The next step was therefore to outline and implement an enterprise architecture for data distribution and advanced analytics. Thanks to the collected use cases, we managed to get funding for this.

After 1.5 years, the architecture worked, but the next problem presented itself. Turnover within the Data Science team was high. The employees did not feel understood and did not have the right resources. We therefore clustered them in groups in which they could spar at their own level, led by a manager who really understood them.

Only then were the key issues concerning advanced analytics taken care of. This whole process involved a lot of trial and error. Throughout the project, knowledge development and improvement of the working culture in cooperation with the business stood at the core.

How did data strategy become a theme throughout the organisation?

Once the advanced analytics project was successfully implemented, we broadened the scope. We integrated topics such as data quality and data sourcing, which were designed by colleagues, into one narrative data strategy. Data flows throughout the organisation. For the implementation of the data strategy, we developed a plan in which different parts of the organisation will work together. This plan consisted of one programme containing ten initiatives. Each initiative consisted of several projects, each with its own project owners, milestones and deliverables. Everyone within the organisation could contribute to this. Hundreds of people actually did this.

To activate all these people, we set up a large internal campaign. We organised a roadshow and several events. We also developed an information booklet. Furthermore, we developed dashboards that provided insight into the progress of projects. We looked at Key Performance Indicators (KPIs) in the field of data quality, data management and analytics. These dashboards were available centrally in the organisation.

What was the biggest obstacle in implementing the data strategy?

Generating support. A strategy is only successful if all stakeholders are willing to contribute to the goals of that strategy. Standardisation (of processes, tools, etc.) is often also part of this. Everyone wants to continue working in the way they are used to. There was a lot of resistance to change and fear of being forced into a mould. People were no longer allowed to use their favourite tool or were assigned a different manager. Most of the energy we put into the project went into change management.

What concrete results does the implementation of the data strategy lead to?

In the organisations I have been involved with, I see that the data organisation is increasingly focused on realising business value and working more and more on strategically relevant topics. Because of this higher relevance, (senior) management also has more time, money and attention for realising good data capabilities. This is a self-reinforcing effect.

Over time, I noticed that the organisations actually offer a better customer experience, work more efficiently, innovate faster, automate more and better and comply with all laws and regulations.

The field is developing rapidly. We see that data is becoming a serious theme at an increasingly higher level within organisations. However, there are currently still only a few organisations that have actually embedded their entire data strategy. It is a process of trial and error. There are also few good people with experience in change management and knowledge of data and architecture. Koen shares his vision on this.

What do you think is the status of data strategy in the Netherlands now?

Nowadays, departments like Marketing and Finance often already work in a data-driven way, but both with their own dynamics. Data never stays within one department and should be seen more as something to be managed at the organisational level.

More and more companies are getting a Chief Data Officer (CDO). This indicates that we are taking data management more and more seriously. It is not something you just do on the side; it requires serious attention. Most CDOs therefore quickly come up with a data strategy. The field is really evolving.

Why do you think organisations should work on their data strategy?

Data knows no boundaries in your organisation, it flows throughout the entire organisation. Besides, it has a lot of potential. Without a clear strategy, you keep working in a fragmented way and leave a lot of potential untapped.

What opportunities do organisations miss out on if they don't prioritise them?

Sand gets into your company's engine. Processes become less efficient. It is not clear which data you should use, you no longer know whether your security is in order and whether your data is reliable. If there is no central data storage and coordination, people will work at cross purposes or do double work. Chances are you will no longer comply with laws and regulations in the field of privacy and security. As a result, you lose control, negatively impacting your entire operation. Your customers' trust will then inevitably decline.

The trend of automation continues and there is less and less personal contact. Companies that now focus on personal customer relationships are increasingly in a niche.

Additionally, regulators are also demanding more and more. You need to know exactly where your data comes from and be able to explain what you are doing with it. This pressure will only increase. Companies that don't get it right will be in trouble. Having good data is also essential for your customer experience, operational excellence and innovation capacity.

What tips do you have for people who want to get started with their data strategy?

Tip 1. Make sure you have the right mandate

Whoever is tasked with developing a data strategy must also have the mandate to implement it. Ideally, you should start as high up in the organisation as possible. A middle manager can initiate it, but must quickly gain support from the top to eventually get the entire organisation on board.

Tip 2. Define a clear scope

Before you get started with your data strategy, you need to determine whether you want to include everything or focus on specific topics. Think of a business unit or a specific data discipline (such as only data analytics, or only data management). Make sure it always supports your business strategy.

Tip 3. First get the basics right

You need a department that deals with data and you need to have reached a certain data maturity. You must prepare the organisation and have a clear answer to the question 'What are we going to do with our data?'

There are often Data Managers, Architects, Analysts and Product Developers scattered throughout the organisation, each with their own vision on data management and data analytics. They are first realised very piecemeal. Then, the need arises to streamline this, in which the Data department plays a key role. Utilise the knowledge and lessons learned of colleagues who are already working on the subject, 'stand on the shoulders of giants', and convince them to collaborate on one single strategy. That way you get off to a flying start.

Tip 4. Take plenty of time

It can take years before you can put a coherent strategy on paper. In my practical example, we did have a direction (think of a general vision, knowledge and the will to do something with the data) and certain use cases, but we developed the data strategy component by component. We started with advanced analytics and moved on to other components from there. All those parts matured separately from each other. At one point, we wanted to offer that as an integrated set of solutions and services, resulting in the data strategy at the organisational level.

Tip 5. Inform your stakeholders at the right time

At an early stage, you do not know whether your plan will be feasible. You don't know the details yet and the timelines are still very uncertain. If you make a big announcement at that stage, you create uncertainty and ambiguity. It is therefore best to start with a small group, build something and deliver it in small pieces. In this process, you only include your most important stakeholders. Only during implementation do you start involving larger groups of people.

How do you then ensure that the implementation of your data strategy succeeds?

You need to formulate a clear mission and vision that everyone can understand, and make these concrete. Answer questions such as 'What are we going to do tomorrow', 'Why are we doing it' and 'Where will we end up?'. Create a clear roadmap with milestones and deliverables, with an owner.

Do your cost-benefit analysis at the project level, not at the strategic level. Share use cases and make a list of issues you will solve to demonstrate the potential added value. Make sure influential colleagues recognise these use cases and issues and express their support.

How do you think the data strategy market will evolve?

Data strategy is now a relatively new topic for the major players. In a while, every large company will have a data strategy in addition to, for example, an HR strategy and a marketing strategy. When the biggest change is then behind us, the role of the CDO may diminish. This will then take on a more coordinating role.

In SMEs, there is less need for a data strategy anyway because the complexity is lower. The goal here is mainly to be in control. Companies must establish a basic architecture, set clear definitions, allocate responsibilities, choose the right tools and gather the insights in dashboards. It is all a bit less complex than with large organisations.

The biggest challenge is finding good people with experience in change management and knowledge of data and architecture. Such people are currently very hard to come by.

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The data strategy model provides insight into your entire organisation based on enablement factors: organisational structure, capabilities, governance, processes and technology. The data strategy model provides insight into your entire organisation based on enablement factors: organisational structure, capabilities, governance, processes and technology.