It is not the accumulation of mountains of data that generates added value; like the true precious metal, data can only unfold its special value after professional processing. Companies such as Google, Amazon and Facebook have shown the way and earned many billions with their data treasures. Companies must develop an individual digital business model in order to use the recovered treasures profitably. This requires true experts.
In order to keep track of the flood of data and ultimately use the data profitably in the company, a pronounced affinity for numbers, coupled with a healthy, well-dosed portion of creativity is required. A key role in data evaluation is played by highly competent data scientists, whose environment is located between big data, analytics and business intelligence, the so-called data scientists.
The data scientist cannot be compared to the typical IT nerd, nor do they sit in a dark room to collect and analyze data. The main task of a data scientist is the presentation of future-oriented analyses – which can then be presented to top management in strategic plans. The data specialist digs through vast amounts of data in order to derive qualified conclusions. Ideally, these results flow directly into the business plan. Such a role in the company should not be regarded as a mere staff function, but rather as a creative impulse generator to define new business potentials and optimize existing processes as required.
In order to be successful, the data scientist must master a number of challenges and possess the appropriate skills. Below is an excerpt of the key competencies required:
– Statistical understanding and affinity for numbers;
– Healthy curiosity;
– IT expertise;
– Entrepreneurial thinking;
– Consulting skills.
Data scientists are able to target complex issues both to top management and to the employees affected at work level. These all-rounders are therefore also good storytellers, and they needed this ability in order to present the entrepreneurial processes to all stakeholders in a targeted manner and to convey them well visually. The professional everyday life of the data scientist is highly heterogeneous, because data management is a sensitive topic on the one hand. On the other hand, data that flows together from completely different areas must be linked in a technically meaningful way.
Data science is also of great importance for top management, because data that has been competently prepared, evaluated and focused on potential business options is now a decisive factor in strategic corporate decisions. Examples are the big players in the industry such as Apple, Google, Uber and Airbnb. These companies have based their procedures and technical developments primarily on data-supported analyses. With the help of data scientists, companies can analyze the needs and inclinations of their customers and position themselves accordingly.
However, more than in other areas directly related to big data, analytics and business intelligence, there is a massive shortage of professionals and executives. This is all the more true as the majority of data scientist courses in Germany were only founded in recent years. So, today’s senior specialists and executives are lateral entrants. In addition, the special combination of skills – which the data scientist combines –  is often described as being like a needle in a haystack. Here, outside advice can be worth its weight in gold.
Author
Dr Monika Becker, head of business unit software, Hager Unternehmensberatung
Becker has been active as a consultant for Hager Unternehmensberatung since 2001. In the business unit software, she and her team fill demanding technical and leadership positions for clients whose business consists of solutions based on standard software or custom software developments.
