Advanced analytics (Data Science)
All around us is variety. A driver has variation when parking his car; train arrival times have variation; the human race exhibits variation; and products that come out of a process are never the same. Every process exhibits variation.
What exactly is Big Data? There is no universally accepted definition. Obviously it has to do with a large amount of data, but the size of the data itself is not what matters. One difference between a normal large data set and big data is that in the latter case, there are multiple sources present that are not directly linked to each other in the same database.
On the one hand, consumers privately generate more and more data in the form of documents, photos and movies (Facebook, YouTube, etc.) On the other hand, more and more data is available in companies as devices generate and exchange operational data and sensor data. In the factories of the future, all devices, machines and ERP systems are interconnected. This is called the “Internet of Things” (IoT). So much data is being generated in the areas of operational performance, logistics, supply chain, product quality and marketing that it is beyond human comprehension.
Thanks to broadband, wireless Internet and data servers in the cloud, data is accessible anytime, anywhere. The focus then shifts to data analytics, including algorithms that allow machines and systems to make autonomous decisions, such as starting production and ordering raw materials.
“Big data analytics is the distillation of meaningful meaning
from a large, complex and unstructured data set”.
Gordon Moore stated in 1965 that the number of transistors in an integrated circuit doubles every two years. This is called Moore’s law. The amount of data generated follows a similar trend. Data storage capacity doubles approximately every 40 months, and since 2012, 2.5 exabytes (2.5 ×1018) of data have been generated daily.
Lean Six Sigma Green Belts and Black Belts know how to analyze and visualize data. But what the difference between normal data and big data? A key feature is that big data is unstructured data and often has multiple internal and external sources. Conventional methods of analysis then no longer work. The challenge is to link these various sources and make real-time connections and obtain meaningful information from them.
This enormous growth in the amount of data requires challenges, but at the same time it also offers opportunities. Data is the “new gold. Parties capable of collecting data and distilling meaningful meaning from it can use it to gain an edge over others.