“Big Data” has become the latest buzzword on the business intelligence landscape. According the article Selling into Micromarkets (July-August 2012 Harvard Business Review), Big data is defined as:
“…vast data sets typically collected in multiple forms from many sources, often in real time… they’re so extensive and complex that specialized software tools and analytics expertise are required to collect, manage, and mine them.” (p80)
Much has been written about big data but not many authors have been able to cite actual, practical examples of its use.
Selling into Micromarkets describes a successful project in which traditional sales territories were analyzed as individual, county-sized “micromarkets”. The five steps in the project were:
- Define micromarket size
- Determine growth potential
- Gauge market share
- Identify the causes of market share differences
- Prioritize growth pockets
The project was deemed a success since sales volume increased 30%.
One of the important issues in discussing big data is getting the data in the first place. In Step 3, the authors specify gathering margin and revenue data across lines of business. They admit, however, “Often, this step is a sticking point for companies because they don’t have ready access to data at this level.”
Organizations wishing to develop their analytical firepower first need to ask the following questions:
- What data is available?
- What is its level of granularity?
- Can it be integrated with other existing data sources?
Answers to these questions will determine what analyses can be undertaken.