Chapter 5Business Analytics at the Data Warehouse Level

During the last couple of years, a lot of changes have happened at the data warehouse level, and we can expect many more changes in the future. One of the major changes was called by the phrase Big Data. The reports that created this term came from McKinsey Global Institute in June 2011. The report also addressed the concern about the future lag of skilled analysts, but this we will discuss in the next chapter. In this chapter we will only focus on the data warehousing aspects of the Big Data term.

The Big Data phrase was coined to put focus on the fact that there is more data available for organizations to store and commercially benefit from than ever before. Just think of the huge amount of data provided by Facebook, Twitter, and Google. Often, this oversupply of data is summed up in 3 Vs, standing for high volumes of data, high variability of data types, and high velocity in the data generation. More cynical minds may add that this has always been the case. It is just more clear for us, now that we know what we can use the data for, due to the digitalization of the process landscape.

The huge amount of data may lead to problems. One concrete example of data problems most companies are facing is multiple data systems, which leads to data‐driven optimization made per process and never across the full value chain. This means that large companies, which are the ones that relatively invest the most in data, cannot realize ...

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