What Is a Data Strategy and What Is Data Strategy Design?

This is the first article in our introductory series on data strategy design showing you how design thinking can help you become a data-driven company.

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Data is the key asset for data-driven businesses and thus a key factor for survival and success in the digital age. Therefore, companies need a strategy how to obtain the right data and how to use the data in the right way and especially for the right purpose. To find the right goal and way you need an interdisciplinary team of creative, analytical and business-oriented people working together. Design thinking is the method you need to form your team and which guides them in the right direction. Data strategy design is the application of design thinking in the field of data analytics and science to create data strategies for data-driven business.

The long read will take about 10 minutes.

Why Do I Need a Data Strategy?

As more companies invest in big data technologies and are trying to apply artificial intelligence (AI) methods in practice, the more they realize the value of their data as the main competitive advantage in the age of digital transformation. The well-known phrase “Data is the new oil” means that data is a corporate asset that boosts your productivity. It enables you to optimize your business processes, e.g. in the case of data-driven marketing to plan your campaigns to be more effective and more efficient. It also makes new business models possible that allow for data monetization, e.g. by applying analytics to your business data and selling the results as information product to your clients.

If you take data as an asset seriously, you need a strategy how to exploit, to refine and to utilize your data sources, i.e. you need a data strategy. Yes, data is the new oil, but there are two aspects where the analogy breaks: First, you can utilize oil only once, but you can utilize data endless times. Second, the value of oil is defined by its energetic value and so one barrel of oil has a defined value, but the value of data cannot be measured in bits and bytes. The true value of data depends on the concrete use case and the value only proves when the data is actually used.

“Data is the new oil – but not like oil”: Like oil, data drives your business. But unlike oil, data has no fixed and unique value. Data can be used as often as you like and its value varies depending on the application.

Corporate assets need to be managed and evaluated and so does your corporate data. A data strategy helps you defining the answers to the most critical questions of a data-driven business:

  1. What data should we capture and store? Don’t treat data as a by-product and only store what you measure by incident. Measuring everything is neither the right approach: data is dirty and thus data quality management is expensive. There are also data privacy laws to consider: the European General Data Protection Rule requires you to minimize the data you store about your customers!
  2. How can we extract relevant information from the data by applying the right tools and methods? Think of the analogy “data is (the new) oil” again: You can’t put oil in your car’s tank. Your gasoline or diesel engine would break down. The same applies for your business processes and decisions: they run on information – not on data!
  3. Why should we analyse this data? Just buying analytics software and digging in the data won’t give you any valuable insights. You might find correlations, trends and anomalies but you need to know the business-critical questions first. You might remember it from the reading of the book “The Hitchhiker’s Guide to the Galaxy”: 42 is the answer, but if you don’t know the question, this information is useless.

How Do I Design a Data Strategy?

When thinking about a data strategy, start with why:

  1. Why do we need data and analytics? What are relevant use cases? Think of problems you might solve with analytical solutions and that create benefits for your company.
  2. How do we apply analytics to the data? What are the possible analytical methods? What analytics tools and skills do we need therefore?
  3. What data do we require? What are the potential data sources we do own, or could earn? What paid or public data might be added to our data landscape?

Starting with the why and taking the perspective of the user is a key concept of design thinking. Indeed, design thinking is also becoming more and more popular in data science, because the role of a data scientist is to create data products and design thinking is all about creating successful products from the perspective of its users.

The combination of design thinking and data science is sometimes called “data thinking” but be careful using this term: it doesn’t mean you should think up your product with data first. You don’t go into your warehouse, look what you’ve got on stock and then decide what to built with that: a house, a car or a ship. Same goes for data: you don’t access your data warehouse, look what you’ve got in your data tables and then decide what to build with that data: a solution for lead scoring, for churn prevention or for lookalike targeting. Instead you start with the (internal) user or (external) customers, analyse their needs and pains and then sketch (data-driven) solutions.

For this reason, the author prefers the term “data strategy design (thinking)” instead of “data thinking”. It’s about designing user-oriented data products and about long-term strategic planning. For most analytical solutions you need the right data to make it work. If you don’t have the data yet, you should start gathering this data as soon as possible. This holds true especially for AI solutions which rely on machine or deep learning which are depending on (massive amount of) training data.

Btw: this is the key difference between data science and data alchemy (a term coined by Lukas Vermeer in his presentation at the Predictive Analytics World Business conference). A data alchemist is trying to make out information from any data he has by chance. A data scientist is looking for the data he needs to answer his questions. So, stop doing data alchemy and start doing data science!


So start today to capture the data you need tomorrow! #DataStrategy #DataThinking

Companies which utilize their data to extract business-critical information will make better decisions and thus outperform their competitors. However, the value of data analyses is determined by the data you analyse. Data collection is an expensive and tedious process. So, you should start thinking about data as soon as possible. However, do not start your thoughts with the data, but with the user of the data (e.g. clients or employees). In the next article you will learn how to apply design thinking on data-driven business models to create successful data strategies.

Create Your Own Data Strategy

If you also want to learn the Data Strategy Design Method, then come to our online training. We have 3 trainings in English for the different levels of experience: 

At the Starter Training „Data Strategy“ you will learn everything you need to know to get an overview of the possibilities of data driven business models and processes.

The Advanced Training „Data Thinking“  is suitable as an advanced seminar and shows you how your company can use the data strategy design method to identify, design and implement successful data science & analytics projects.

The Expert Training „Data Business“  is the right choice for you if you already have a solid knowledge of analytics and data science. We show you the right tool to develop data-driven business models and processes together with your team or your customers.

Would you like to have more information on a specific matter? Then please do not hesitate to contact us.

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