Data Thinking: With innovative framework to data-based solutions

Did you know that 60% of data projects don't make it past the testing and experimentation phase? The reason for this is that there is usually no common data tool or data strategy. This is where Data Thinking comes in: We show you how to save your company from the concept of planlessness and use data as an innovation driver.

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As we all know, digitization is the industrial revolution of the present. Buzzwords, such as artificial intelligence, Big Data or Machine Learning are not only shaping the digital transformation, but also our everyday working lives. Four out of five companies are convinced that data-driven AI solutions will permanently change their industry.

By 2025, we can save around 350 million euros in costs in Germany alone and increase company revenue by 150 million euros. But did you know that 60 % of data projects don't make it past the testing and experimentation phase? The reason for this is that there is usually no common data tool or uniform data strategy.

Often, user needs are 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 show you how you can use data thinking to save your company from the concept of planlessness and use data as an innovation driver.

Data as the basis for a strong strategy

Data generates knowledge, and knowledge is power. Data Thinking (in German: Daten denken) is the combination of Data Science (data science) and Design Thinking (design thinking). But what exactly does that mean? In data thinking, you ask yourself how you can create business value from data.

Design thinking provides you with the answer to this question. The Creative process for finding ideas identifies the needs of users and specifically addresses the wishes 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 solution idea meets the user requirements and evaluate it with defined KPIs. So clarify the following questions here:

  • What problems does your user have?
  • How can you solve these in a data-driven way?
  • What data do you have available for the solution?
  • Is the solution technically feasible?

The basic idea of Data Thinking is that you will creatively develop user-centric and data-driven solutions through collaboration in an interdisciplinary team.

The step to data happiness

Unfortunately, data alone does not contain any solution instructions: What problems does your user have and can your plans be implemented in a practical way? In data thinking, it is important at this point to put quality before quantity and concept before lack of plan. The Data Thinking process helps you to develop structured data-based solutions. It is divided into three essential steps:

  1. Exploration

  2. Idea generation

  3. Evaluation

Figure 2: The iterative data thinking process

 
 

The first step is the Exploration. True to the motto "Fall in love with the problem, not a particular solution." is about focusing on the problem. You also have 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 which data sets do you need to plan more time for? 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 the Idea generation. Whether it's new products, innovative services or promising business models - here you can unleash your full creative potential. To do this, you define the requirements for data quality and structure in collaboration with the IT team. 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 MVPs, so-called minimum functional products. With them you bring to life the most important characteristics of your idea.

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 your 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. Then 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 your goal is achieved and the product can be launched on the market.

With data thinking to the data-driven company

The advantages of Data Thinking are obvious: The framework helps you and your company, user-oriented and data-driven solutions to develop. 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 from all areas increases the acceptance of the solution in the company.

 

The opportunities and potential of digitization are mostly still uncharted entrepreneurial territory. Digital transformation is not a single step, but rather a continuous process that requires corrections and adjustments. Data Thinking offers you a tool with which you can integrate the use of data step-by-step into your business. Integrate corporate culture can. This way, your company can invest in data-driven and user-centric solutions that have a measurable business impact from the start. The data process is No black box more. The iterative approach and the constant exchange with the 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 can build a company-wide data strategy. Feel free to contact us and we help you to lead the digital development of your company to success.

 
 

As we all know, digitization is the industrial revolution of the present. Buzzwords, such as artificial intelligence, Big Data or Machine Learning are not only shaping the digital transformation, but also our everyday working lives. Four out of five companies 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? The reason is that there is usually no common data tool or data strategy. Often, user needs are 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 show you how you can use data thinking to save your company from the concept of planlessness and use data as an innovation driver.

 

Data as the basis for a strong strategy

Figure 1: Data thinking as a combination of data science and design thinking

Data generates knowledge, and knowledge is power. Data Thinking (in German: Daten denken) is the combination of Data Science (data science) and Design Thinking (design thinking). But what exactly does that mean? In data thinking, you ask yourself how you can create business value from data.

Design thinking provides you with the answer to this question. The Creative process for finding ideas identifies the needs of users and specifically addresses the wishes 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 solution idea meets the user requirements and evaluate it with defined KPIs. So clarify the following questions here:

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

Subsequently, you develop these creative solutions with the help of data science methods into complete data solutions further. For example, data mining and data analytics are powerful tools for identifying trends and correlations. They enable you to exploit the entire data potential by generating knowledge from the data. By working together with IT experts and data scientists, you can check at an early stage whether the project is feasible. It is important to assess whether, for example, the necessary forms of data processing are possible and the required system architecture is in place. So clarify the following questions here:

  • What data do you have available for the solution?
  • Is the solution technically feasible?

The basic idea of Data Thinking is that you will creatively develop user-centric and data-driven solutions through collaboration in an interdisciplinary team.

 

Step by step to data happiness

Unfortunately, data alone does not contain any solution instructions: What problems does your user have and can your plans be implemented in a practical way? In data thinking, it is important at this point to put quality before quantity and concept before lack of plan. The Data Thinking process helps you to develop structured data-based solutions. It is divided into three essential steps:

  1. Exploration
  2. Idea generation
  3. Evaluation


The first step is the Exploration. True to the motto "Fall in love with the problem, not a particular solution." is about focusing on the problem. You also have 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 which data sets do you need to plan more time for? 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 the Idea generation. Whether it's new products, innovative services or promising business models - here you can unleash your full creative potential. To do this, you define the requirements for data quality and structure in collaboration with the IT team. 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 MVPs, so-called minimum functional products. With them you bring to life the most important characteristics of your idea.

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 your 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. Then 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 your goal is achieved and the product can be launched on the market.

 

With data thinking to the data-driven company

The advantages of Data Thinking are obvious: The framework helps you and your company, user-oriented and data-driven solutions to develop. 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 from all areas increases the acceptance of the solution in the company.

The opportunities and potential of digitization are mostly still uncharted entrepreneurial territory. Digital transformation is not a single step, but rather a continuous process that requires corrections and adjustments. Data Thinking offers you a tool with which you can integrate the use of data step-by-step into your business. Integrate corporate culture can. This way, your company can invest in data-driven and user-centric solutions that have a measurable business impact from the start. The data process is No black box more. The iterative approach and the constant exchange with the 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 can build a company-wide data strategy. Feel free to contact us and we help you to lead the digital development of your company to success.

Rebecca is almost old news at StackFuel. She has been on board for more than 2 years now and is the assistant for marketing and sales. When she's not writing posts for social media or the blog, she skillfully juggles between the two departments. A real power woman, she is in the final stages of her Master's degree on the side. In her free time, Rebecca is passionate about dog sports and competes with her two dogs.

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