In order to create added value from data, both key skills (Data Skills) must be available in the company and responsibilities must be distributed accordingly within the organisation (Data Roles). Collecting and storing data is only the first step on the way to becoming a data-driven organisation. Many companies want to build a modern data unit to get closer to their strategic goals. For this to succeed, it is important to know which roles and skills you need for this and how you can integrate them into your company.
Link tip: There is also a growing awareness of the importance of data in the field of management - We have summarised here which roles are crucial here.
How to create added value from data
Building a customised data unit
Internal development of data know-how and expertise
Efficient division of labour for the implementation of data use cases
A comprehensive data department is usually to be set up to implement own use cases. Creating value through data requires a number of specialists and an efficient division of labour. To become a data-driven company, a holistic approach is often necessary, structural transformation of an organisation is necessary.
On the way to becoming a "data-driven company", a goal-oriented development of suitable roles for the implementation of data-driven projects and staffing with suitable personnel is crucial. We therefore advise companies on how they can build a customised data unit that is adapted to their specific organisational structure.
Data Roles and Data Skills
Once the specific need for data roles and data skills has been determined in an individual strategic consultation tailored to the respective organisational structure, the concrete development can begin. The classic data roles include, for example:
- Data Scientist
- Data Engineer
- Data Analyst
- Data Architect
- Data Stewart
The exact number and composition of a team can change over time and depends on several factors such as budget, strategy and company size. Data roles are intended to ensure that the crucial data skills are available in the organisation and that responsibilities are distributed accordingly. Especially in the beginning, it may be that certain skills, which are later distributed to several roles, are initially taken over by one person. Such data skills are, for example:
- Dealing with technologies
- Programming languages
- Project management methods
Many of the terms and concepts surrounding data roles and data skills are still comparatively new. That is why the job titles are often still very broad and their task catalogues very extensive. Thus Data science projects can start quickly and deliver initial results, it is important to define in advance as precisely as possible what the needs are in order to avoid mistakes in staffing and building up expertise.
This is how we support the definition of data roles and data skills
Identifying appropriate data roles is key to achieving strategic data goals (AI Development and Data Engineering). In addition, the necessary skills for setting up Effective Data Units is determined. Our advice therefore covers the following four areas:
- Evaluation of required data roles and data skills as well as definition of
- Evaluation of the Maturity level existing data roles and data skills in the company.
- Develop a strategy on how the required data roles and data skills can be built up and integrated into the existing data system. Corporate structure integrated can become.
- Accompaniment in strategy implementation through support in the areas of:
- Further education
Our counselling takes place within the framework of a Workshops takes place. Here, the development of suitable roles takes place against the background of the strategic corporate goals. The workshop usually lasts one day, followed by several weeks of support. During the workshop, an initial concept for integrating the data roles and skills into the company structure is developed, and active support is provided during the support phase in filling the roles with suitable personnel through training and recruiting.