Big Data specifies itself according to two main criteria.
There’s structured data that’s saved in a defined and fixed form (e.g. in data bases and tables), unstructured data that’s available in all manner of different formats and platforms (e.g. emails, audio and video files etc.) and semi-structured data such as HTML and XML.
Furthermore, Big Data differentiates according to information source. On the one hand, whether the data is available internally or derives from external sources. And on the other hand, whether the data is created/generated by people or machines.
What does a data scientist need to be able to do?
Technical know-how is of course a prerequisite in order to be successful as a Big Data master. The ideal candidate combines this know-how with knowledge of content and document management, databases, data warehousing, semantics, project management, social media and business intelligence.
At the Best in Big Data" congress in Frankfurt, Wolfgang Hackenberg from Steinbeis-Transferzentrum pvm described the position as follows:
“He/she needs mathematical understanding, business skills, must be able to visualise data and handle diverse technologies. He/she needs curiosity, impatience and laziness and must be a certain hubris – equipped with greed, arrogance and pride in their own achievements.”
This seems to require a degree in IT, information systems or mathematics.
But is this job description not a little bit like travelling to the Scottish highlands in the search for a mysterious, water-based creature with a long neck? You’re bound to end up on the banks of Loch Ness gazing eagerly/desperately into the mist with the promise that, if you’re lucky, you’ll find what you’re looking for.
Experts that offer all these characteristics are an extremely rare species. Consequently a range of professions linked to Big Data have established themselves.
In any case, the DATA SCIENTIST represents the job of the future in the field of Big Data. They should offer excellent ability in statistics and mathematics as well as detailed knowledge of company goals, employees and of the sector in which the company is active.
Additionally, the data scientist needs to be competent in the fields of databases, network technologies and programming. They represent the company or department externally and must therefore possess an acute sensitivity and strong negotiating skills for communication. Many fundamental and managerial decisions fall under this field of duty.
According to a study by ECM Austria it’s twice as likely that a data scientist makes a business decision based on a complex algorithm used to analyse (News von EMC:Nur ein Drittel der Unternehmen kann Daten effizient nutzen).
The need for these experts is growing rapidly. But where are they? According to a study by MGI and McKinsey (Mc Kinsey: Big Data) , in the USA alone there is a deficiency of between 140,000 and 190,000 people who have the ability at an analytical level to understand results and to make important company decisions. And the need is growing.
Incidentally, this is what your data scientist could look like:
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