The German weekly magazine, "Der Spiegel", took on the subject of mass, unstructured data this week in its cover story. What are the opportunities in analyzing that kind of data ? What are the risks of analyzing Big Data ? In the article, Blue Yonder CEO Uwe Weiss highlights the uses of predictive analytics while providing some specific examples. Here, he adds to his thoughts on that.
Industrial Big Data: Hamburg port is becoming smart port © jyleken - Fotolia.com
We never viewed ourselves as "tracker dog trainers" at Blue Yonder. But maybe there is something to the idea. "Algorithms are the 'trackers dogs' of the virtual realm", as the cover story of this week's Spiegel, "Die gesteuerte Zukunft" ("The Guided Future"), puts it. Algorithms really do track connections in "payment streams at supermarket cash registers, from weather services, vacations, and from traffic reports." The Spiegel report shows how Blue Yonder makes use of that analysis to predict sales, for example for retailers OTTO and SportScheck, and to predict what the right number of personnel should be in individual stores at the dm pharmacy chain. And just like intelligent animals, the algorithms learn from experience. The bigger the data quantities and the more "experience" the algorithms have, the more precise their analyses will be.
Algorithms can provide answers to anything – companies just have to ask the right questions
A lot of things can be predicted. For example, the article shows how Google was able to exactly predict a virus outbreak in 2010 using search behavior analysis. That was important information for the health sector. As the host of the Second BIG DATA & ANALYTICS Congress , we know - like no other company - how commercial enterprises can make the right decisions using predictive analytics. But that only touches on the range of possible uses. The conversion of Hamburg's port to a "smart port" illustrates industrial Big Data's potential.
Industrial Big Data means automation
Sensors in machines, in facilities, in automobiles, at places where goods are on-loaded and off-loaded, and in warehouses generate immense volumes of data. And that can be combined with all kinds of imaginable external factors: weather, holidays, and the economic situation. Predictive analytics goes beyond prediction, just as automated materials planning in commerce does. Predictive analytics enables automation: steps in the production process are automatically interlinked, traffic is managed, traffic jams are prevented, and rail tracks are only moved when a load arrives. All this will change our world. It will make our economy more efficient, it will increase customer satisfaction, and - without a doubt - many of us will be confronted with the question of what personal data we want to reveal and what we expect for doing so. It is hard to believe that three-fourths of Germans have never even heard of Big Data. For that reason, I consider it to be part of my job as the CEO of a Big Data company to be very careful with people's data.