Big Data
How data analysis can be optimised

Efficiently utilising large quantities of data requires more than just buying corresponding analysis tools. Fundamental changes must also be made in terms of the approach. Otherwise, by 2017, around 60% of the current projects to analyse big data will be halted within the pilot or experimentation phase.

This is at least what market research company Gartner predicts, while also offering tips as to how companies can prevent failure:

Choose a task that promises quick results.
Find a current problem that either offers high business benefits or rapid success. This could be the operational decisions of the day, tactical considerations in areas like planning, or individual strategic decisions, such as expanding into another country.
Commission third-party companies and buy finished programme packages.
Many companies believe it is better to establish a central analysis department with its own tools. However, for rapid success, it is often of benefit to commission an external service provider or buy modern analysis software to demonstrate the value of big data analyses for a certain problem.
Identify the key players in your company who need to be won over.
It is the naysayers, pessimists and decision-makers who you have to get on your side. The most difficult task is always to change people’s beliefs. A business case that shows the value of big data analyses helps with this. What’s more, it is generally necessary to establish a business culture based on data.
Consider whether you actually want to set up the expertise and tools in-house.
An in-house solution is not the best method in every case. It is only sensible if a) the analyses in one’s own sector are an important USP or if the sector is of strategic importance, b) highly flexible and granular control is required and c) there is the possibility in the company to apply the analyses in numerous fields of application or business sectors. (Source: Gartner/rf)