Big Data

Using Big Data for Improving Business Processes

The rapid advances in information technology (IT) have a number of effects on global business. One of these effects is that a large amount and variety of measurement data is produced at great speed. The availability of such “Big Data” creates a number of new opportunities for business but also entails numerous entirely new challenges.

Producers of consumer goods have recently recognized the potential benefits of using quantitative data for optimizing their business processes, e.g., for planning their product development and distribution. They noticed that simple data analyses and human expertise alone are not sufficient for making effective decisions in the complex environment of global markets, where a number of mutually interacting factors influence business success. The challenges that need to be faced include selecting and preparing appropriate data, choosing appropriate analysis techniques and tools, and transforming data analysis results into business-relevant knowledge. In order to address these challenges, Fraunhofer IESE applies a combination of a top-down and a bottom-up decision-making approach in collaboration with its customers:

Top-Down Approach: aligns measurement and decision making to a specific business problem

  • Specify the business problem
  • Derive a suitable data analysis problem
  • Derive an appropriate data analysis approach
  • Derive appropriate metrics and collect the required data
  • Apply the analysis approach and interpret its outcomes to solve the business problem

Bottom-Up Approach: explores existing data in order to gain new insights aimed at driving innovative business solutions

  • Use available data and, if necessary, explore new sources
  • Apply best-practice analysis approaches
  • Identify significant patterns in the data
  • Synthesize and interpret the discovered data patterns in order to gain business-relevant knowledge