Why companies need a data strategy

Guidance for a data thinking process

Data is one of the most important drivers of successful business development. But only if they are valued, treated accordingly and used strategically. There is no bigger mistake than simply collecting data and trusting that something will result from it. Because when it comes to the tension between digitization and data use, nothing simply comes naturally. We show why your own data strategy is the last and most important component of digitization.

Digitalization influences and structures all areas of our society. This presents companies with the challenge of adapting to the changing environment and offering customers new solutions along with this digitalization. Internally, processes, products and services are increasingly digitalized. However, the resulting data and the resulting insights are perceived half-heartedly. Only a few companies are able to actually use internal digitization and derive solid, future-oriented decisions from it.

Data analysis and its enormous potential for success

This is not surprising when there is only administration everywhere and the keyword “digitization” for most people in leading positions is about mitigating and reactively managing potential threat scenarios. Companies such as Apple, Amazon or Microsoft have become the richest companies in the world 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. You have a data strategy.

The good thing about this challenge is that the transformation can be planned step by step and includes various development plateaus that are easy to analyze. Consistent data analysis allows companies to discover new information and connections and ultimately generate new knowledge. Used correctly, data can therefore provide new insights into business processes. As a result, understanding of customers and one's own company can be significantly improved. The findings from data analysis can be incorporated into any decision-making process. They thus form the core of a data-driven company. But how can data be used optimally? How does the data analytical development of an organization go 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 caused by 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 of them. It therefore plays a key role in formulating your own data strategy.

The idea of data thinking is to establish the use of one's own data based on a new corporate culture. In a data-driven company, this way of thinking strategically focuses on data and its profitable use. CEOs then always make marketing measures or company decisions based on detailed data analysis, for example. Even when optimising business processes or other internal corporate structures, decisions are made after taking a close look at all analytical findings. Data thinking minimizes the scope for intuitive gut decisions or spontaneous experiments. To do so, 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, must move closer together and make decisions based on a common database. There are already some tools that can also be used by employees without IT knowledge and with less technical understanding.

What a company's thinking says about analytical maturity

The way companies think about data and how they handle it says a lot about their understanding of contemporary corporate development. Most people have already heard that decisions are best made based on data today. However, very few have internalized which course an organization must set and for which challenges which tools, methodologies and competencies are necessary.

The topic is certainly complex. However, with a clear structure and your own roadmap, the gradual transformation from a “data-unconscious” company to a “data-based” company can be tackled reliably.

Implement data strategy: step by step

A holistic, cross-departmental data strategy forms the basis for a data-driven company because it includes 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

1st 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?
• What goals are being pursued with the strategy and which specific business value should be optimized

2. Identify relevant data sources

Companies must collect, clean and process available data, which is generated daily, according to logical criteria. This is the only way they can be used within the data strategy.

• Which internal and external data sources are already available and accessed? Which are available but not yet developed?
• Is the stored data accessible to all departments?

It is important to take a cross-system or cross-departmental approach 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 carried out in close coordination with the specialist departments. Their desire to learn is essential for asking the right questions and formulating 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 is important to examine its usability and benefits.

• Which value propositions for customers should be created from the data?
• How can marketing, sales and delivery channel data be improved?
• How do data help to make key activities, resource use and costs more efficient?

Data-driven company: Implement and establish

5. Planning the implementation

In order to be able to institutionalize the initial results, it is important to build prototypes of a data analysis. Only if the approach also enables all employees to analyse data, is it entrepreneurial successful.

• 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 trainings and workshops to empower employees?

6. Data strategy

Finally, preliminary considerations must be structured by transferring 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 a data strategy, it is important to be able to answer specific questions in order to build a holistic corporate culture. However, the considerations above are just a few examples to explain the general procedure. Using data thinking methods, companies are able to establish a well-founded strategic orientation, which results in a change in awareness of data-driven work throughout the company.

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