Leistungen // Laterales Management
a new career – data scientist
Today, humanity produces as much data in two days as it produced throughout history up to 2003. The total quantity of these data doubles every two years, meaning that it will reach 40,000 exabytes (40 million gigabytes) in 2020. At this point, we shall probably have reached the optimum in data processing and the end of Moore’s Law.
One exabyte has storage capacity for 250 million DVDs. In roughly one hour, humanity produces as much data as are currently stored in the USA’s Library of Congress. For companies, this means that digitalisation is creating an incredible flood of information. The buzz words BIG DATA, SMART DATA, DATA MINING, DATA ENGENEERING, PREDICTIVE ANALYST or, as mentioned above, DATA SCIENTIST are associated with the people who have to cope with this. Degree courses in these disciplines are proliferating in much the same way that mushrooms do after rain.
What makes a data scientist?
Big data is a general term for volumes of data that are too large, too complex, short-lived or too unstructured to be successfully evaluated for companies using traditional data-processing methods. If everything is networked with everything else, all parts are separate sources of information. Today, customers are accessing banks by online banking via their fixed PC, smartphone, tablet and credit cards. All these devices produce data that are of relevance to the bank. These data must be analysed, organised and evaluated.
According to an analysis, the USA will need 150,000 people, who can carry out this work, in the near future. The University of Munich offered an appropriate course for the first time in 2015, Mannheim and Darmstadt will follow from the 2017 summer semester.
Many companies are still working in the dark. There is much talk of big data but many managers have no idea how much data their organisation actually possesses, which of these data are particularly relevant and how they can be successfully exploited. Organisations will certainly not be able to wait until graduates are equipped to get to grips with big data in two to three years. More rapid solutions will be found – both internally and externally.