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data thinking

Data thinking: an innovative framework for data-driven solutions

As we all know, digitalization is the industrial revolution of our time. Buzzwords such as artificial intelligence, big data and machine learning are shaping not only the digital transformation, but our everyday lives.  Four out of five organizations are convinced that data-driven AI solutions will permanently change their industry. By 2025, we can save around 350 million euros in costs and increase company revenue by 150 million euros in Germany alone. But did you know that 60% of data projects don't make it past the testing and experimentation phase? This is because there is usually no common data tool or data strategy. User needs are often not defined clearly enough or desired goals are not explicitly stated. This is where data thinking comes in: the data-driven innovation method answers economic questions with a data-based mindset. We’ll show you how you can use data thinking to save your company from this lack of a plan and use data as an innovation driver.

Data as the basis for a strong strategy

Data generates knowledge and knowledge is power. Data thinking is the combination between data science and design thinking. But what exactly does it mean? In data thinking, you ask yourself how you can create business value from data.

Figure 1: Data thinking combines data science and design thinking

Design thinking provides the answer to this question. The creative process for idea generation identifies the user’s needs and specifically addresses the desires and possible use cases. You develop solutions for data-driven challenges in a creative way. As a business expert, you put the needs of the user at the center of all considerations. Through an iterative approach, you also ensure that your idea meets the user requirements and evaluate it with the help of defined KPIs. So, you should clarify the following questions:

  • What problems does your user have?
  • How can you solve them in a data-driven way?

You then use data science methods to develop these creative solutions into complete data solutions. For example, data mining and data analytics are powerful tools to identify trends and correlations. They enable you to exploit the full potential of data by generating knowledge from it. By working together with IT experts and data scientists, you can check whether the project is feasible at an early stage. For example, it's important to assess whether the necessary forms of data processing are possible and if the required system architecture is available. So, clarify the following questions here:

  • What data is available for the solution?
  • Is the solution technically feasible?

The basic idea of data thinking is to develop user-centric and data-driven solutions in a creative way by working together in an interdisciplinary team.

Step by step to data happiness

Unfortunately, data alone does not contain a solution manual: what problems does your user have and how can your ideas be implemented? In data thinking, it’s important to put quality before quantity and concept before a lack of a plan. The data thinking process helps you to develop structured data-based solutions. It’s divided into three essential steps:

  1. Exploration
  2. Ideation
  3. Evaluation
Figure 2: the iterative data thinking process

The first step is exploration. True to the motto "Fall in love with the problem, not a particular solution", the first step is to focus on the problem. You need to formulate it clearly and as precisely as possible. To do this, you analyze the specific use case and evaluate existing company data. Which data can you tackle quickly with little effort and still achieve a large impact, and for which data sets do you need to plan more time? Towards the end of the exploration, you and your project team determine which key figures or metrics should be used to evaluate the solution ideas. The exploration lays the foundation for the further iterative procedure.

The second step is ideation. Whether it’s new products, innovative services or promising business models - this is where you can unleash your full creative potential. To do this, you work with the IT team to define the requirements for data quality and structure. Which data sources can be helpful apart from existing data sources? Share your ideas with your project team and develop a valuable database. However, the challenge is not to collect a lot of data. Limit yourself to the necessary and most promising data. Based on this, you develop prototypes and MPVs, so called minimum viable products. With them, you bring the most important characteristics of your idea to life.

The third and final step of data thinking is the evaluation. Your solution ideas are put through their paces here. To do this, you present the prototype to selected test users inside or outside your company. They test your solution and give you feedback on the idea and implementation. Based on this, you evaluate the idea again objectively using the key figures and metrics defined in the exploration. After that, the process of data thinking starts again. This happens until your solution meets all the required target values in the KPIs in the evaluation. Then you have achieved your goal, and the product can be launched on the market.

Becoming a data-driven company with data thinking

The advantages of data thinking are obvious: the framework helps you and your company to develop user-oriented and data-driven solutions. The business focus ensures that data is not just senselessly collected and stored but used profitably. At the same time, early IT input ensures technical feasibility. The positive side effect: teamwork involving all areas increases acceptance of the solution in the organization.

The opportunities and potential of digitalization are mostly still uncharted territory for businesses. Digital transformation is not a single step, but rather a continuous process that requires corrections and adjustments. Data thinking offers you a tool you can use to integrate data into your corporate culture step by step. This way, your company can invest in data-driven and user-centric solutions that have a measurable business impact right from the start. The data process is no longer a black box. An iterative approach and constant exchange with users guarantee you a transparent problem solution with a chance of success. 

Do you want to put your company on the path to data-driven business? With the concept of data thinking, you will succeed in building a company-wide data strategy. Feel free to contact us and we will help you lead the digital development of your company to success.

Rebecca Marzahn

Rebecca Marzahn

Rebecca gehört bei StackFuel fast schon zum alten Eisen. Mehr als 2 Jahre ist sie jetzt an Bord und Assistentin für Marketing und Vertrieb. Wenn Sie nicht gerade Beiträge für Social Media oder den Blog schreibt, jongliert sie geschickt zwischen den beiden Abteilungen. Als echte Powerfrau ist sie nebenbei noch in den letzten Zügen ihres Masterstudiums. In ihrer Freizeit widmet sich Rebecca leidenschaftlich dem Hundesport und nimmt mit ihren beiden Hunden an Wettkämpfen teil.

Data thinking: an innovative framework for data-driven solutions

Did you know that 60% of data projects do not make it past the testing and experimentation phase? This is because there is usually no common data tool or data strategy. This is where data thinking comes in: We’ll show you how you save your company from this lack of a plan and use data as an innovation driver.

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