Maybe these situations sound familiar to you…
You want to enhance the activities of your company with digital, data-based approaches. The opportunities and challenges of digital transformation are obvious, so it makes sense to rethink certain business strategies based on data. You’ve built a strategy through creative methods like design thinking, and you know exactly what data-centric solutions you need to develop – but the technical departments are pushing back. Design thinking is all well and good, but your use cases haven’t taken enough account of the data and IT system challenges involved.
Or did it happen the other way around? You and your team have used your expertise in data analysis to develop a promising data solution for your company. The technical implementation of the measures is clear – what’s missing is a good understanding of whether this solution brings real business value to the entire organization. How should the solution be implemented sustainably in the company? What benefits does it bring not only to the team, but also potentially to customers and internal users? Is the cost-benefit relation for this solution appropriate?
Business strategy and data science, both sides are essential for success these days. It is therefore all the more important to bring these parties together at the conceptual level. Design thinking and data mining approaches such as CRISP-DM (Cross-industry standard process for data mining) offer great potential in their respective areas of application, but there is a lack of communication, of bringing these ideas together for a sustainable data strategy.
The rapid pace at which innovative technologies are developed today demands a high degree of adaptability from companies. Creative methods such as design thinking help to think outside the box and find flexible solutions for the challenges of digital transformation. At the same time, Design Thinking enables an organization to consider not only internal but also external factors in its strategy development.
Data Thinking uses this approach to address customer needs and link them to data-based solutions. It allows strategy and creativity to be incorporated into the development of new solutions and thus sustainably anchors the process in business development.
Data is the key – Data Mining and CRISP-DM
Creative, future-oriented thinking is an important key to success. Often, however, these methods are used without or with minimal involvement of technical experts. Developing a digital roadmap for an organization without getting data scientists and IT professionals on board is not sustainable in the long run. Data mining frameworks such as CRISP-DM are designed to develop data-driven solutions. However, they are strongly located in the technical fields and are rarely considered by decision-makers and strategists.
This is the greatest advantage of Data Thinking. First, it combines the concrete technical approaches of CRISP-DM with the often more abstract concepts of the design thinking method. Secondly, it also offers an opportunity to bring data and IT experts to the table right from the start and to actively shape the development of new measures.
Data Thinking is a framework for the research, design, development and validation of data-driven solutions with a user-, data- and future-oriented focus. It is therefore easy to see that Data Thinking is the clear way forward. But how can companies profitably apply this framework in practice?
The answer is Process Mining. Process Mining has strong roots in data science as a method for data analysis of business processes. However, some process mining solutions go one step further. With the help of machine learning and versatile analysis and controlling features, process mining can be a powerful instrument for sustainable business development.