Instructions for a data thinking process
Data is one of the most important drivers of successful corporate development. But only if it is valued, treated accordingly and used strategically. There is no greater mistake than simply collecting data and trusting that some kind of insight will emerge from it. After all, in the field of tension between digitalization and data usage, nothing simply happens by itself. We show why your own data strategy is the final and most important building block of digitalization.
Digitalization is influencing and structuring all areas of our society. This presents companies with the challenge of adapting to the changing environment and offering customers new solutions in line with this digitalization. Internally, processes, products and services are increasingly digitized. However, the resulting data and the knowledge gained from it are only half-heartedly recognized. Only a few companies manage to actually use the digitalization that has taken place internally and derive solid, forward-looking decisions from it.
Data analysis and its enormous potential for success
This is hardly surprising when everything is managed and the keyword "digitalization" for most people in leading positions is about mitigating and reactively managing potential threat scenarios. Companies such as Apple, Amazon and Microsoft have become the richest corporations in the world, and not just because of their products. They have understood how to transform the extremely high information content of their data into new services and solutions through strategic use. They have a data strategy.
The good thing about this challenge is that the transformation can be planned step by step and contains different development plateaus that can be easily analyzed. Consistent data analysis allows companies to discover new information and correlations and ultimately generate new knowledge. When used correctly, data can provide new insights into business processes. This can significantly improve understanding of customers and your own company. The findings from data analysis can be incorporated into every decision-making process. They therefore form the core of a data-driven company. But how can data be used optimally? How does the data-analytical development of an organization progress from data-ready to data-driven?
Data strategy: data thinking as an approach
Companies must adapt to the rapidly changing environment of new technologies, disruptions through new business models and market conditions in order to be successful in the long term. In the course of this development, new strategies, ways of thinking and methodologies have also emerged on the entrepreneurial side. Data thinking is a very successful one. It therefore plays a key role in the formulation of a company's own data strategy.
The idea of data thinking is to establish the utilization of one's own data based on a new corporate culture. In a data-driven company, data and its profitable use are strategically at the heart of this way of thinking. For example, CEOs always make marketing measures or business decisions on the basis of detailed data analysis. Even when optimizing business processes or other internal company structures, decisions are made after careful consideration of all analytical findings. Data thinking minimizes the scope for intuitive gut decisions or spontaneous experiments. Instead, it maximizes the inclusion of statistical and data-based analyses.
The challenge is to coordinate the various specialist departments in a company. Analysts and marketers, for example, need to move closer together and make decisions based on a shared database. There are already some tools that can be used by employees without IT knowledge and with little technical understanding.
What a company's thinking says about its level of analytical maturity
The way companies think about data and how they handle it says a lot about their view of contemporary corporate development. Most people have already heard that decisions today are best made on the basis of data. However, very few have internalized which course an organization must set for this and which tools, methodologies and skills are required for which challenges.
The topic is certainly complex. However, with a clear structure and a dedicated roadmap, the gradual transformation from a "data-unaware" company to a "data-based" company can be tackled reliably.
Implementing a data strategy: Step by step
A holistic, cross-departmental data strategy forms the basis for a data-driven company because it contains clearly formulated goals within a specific time horizon. In the following, we describe a brief guide from the initial idea to an efficient strategy. The sequence of six steps can be divided into three phases:
Data thinking: first ask questions internally
1. vision
Companies must have specific goals that they want to achieve with data analysis.
- How is the business model defined, what is the market situation like?
- What goals are being pursued with the strategy and what specific business value is to be optimized
2. identification of relevant data sources
Companies must collect and cleanse available data that is generated on a daily basis and prepare it according to logical criteria. This is the only way to make it usable within the data strategy.
- Which internal and external data sources are already available and accessible? Which are available but not yet tapped?
- Is the stored data accessible to all departments?
It is important to pursue a cross-system or cross-departmental approach in order to avoid the creation of data silos.
Data thinking: Then ask questions to the outside world
3. concept for obtaining information
The analysis of key figures and values must be closely coordinated with the specialist departments. Their thirst for knowledge is essential for the right questions and formulation of use cases within the data strategy.
- How can companies link data to generate new information?
- Which tools can be used?
- How can companies use the new information?
4. concept for knowledge generation
The evaluation of all relevant information collected in the analysis is crucial. It must be examined in terms of its usability and benefits.
- Which value propositions for customers should be created from the data?
- How can the data from the marketing, sales and delivery channels be improved?
- How does the data help to make key activities, resource utilization and costs more efficient?
Data-driven company: Implement and establish
5. planning the implementation
In order to institutionalize the initial results, it is important to set up prototypes for data analysis. Only if the approach enables all employees to analyze data will it be successful from a business perspective.
- What integration solutions does the company need to connect internal and external data sources?
- Which analysis tools do employees use to develop the analysis?
- Which visualization tools do employees use to create dashboards and reports?
- Or are there training courses and workshops to empower employees?
6. data strategy
Finally, the preliminary considerations must be structured by assigning possible areas of responsibility to employees and teams.
- Who designs the analysis processes and who carries out the analyses?
- In which formats and meetings are the results interpreted?
- What does the data analysis workflow look like and how are responsibilities distributed?
Within the various stages of building a data strategy, it is important to be able to answer specific questions in order to establish a holistic corporate culture. However, the considerations listed above are just a few examples to explain the general approach. Using data thinking methods, companies are able to establish a well-founded strategic orientation that results in a change in awareness of data-driven work throughout the company.
