The usefulness of any data type or data source depends on the type of analytics being performed. For some businesses, data analysis functions as a tool of real-time intelligence gathering and performance measurement. Another business might use purely descriptive analytics that focus on profiling, segmentation and consumer identification. A more ambitious version of data analytics is concerned with transforming data into predictions -- asking not only what is but what will be. The fastest rising application of data in business analytics is known as optimization, where different types of data are compared to maximize efficiency in targeted outcomes.

Data is important when it's been refined into a useful tool. To put this in perspective, think of unrefined data as if it were unrefined oil: it's possible to collect huge amounts of data, but it has to be transformed into a useful product to be valuable in an economic sense. Application has to be extracted out of the data. The role of business analytics is to refine the data.

Consider the following example: Company ABC sells toy cars. Management decides that it wants to understand its potential market, but it can't decide about which type of data to collect. Should it look at buying patterns in real automobiles? Should it take surveys of the favorite toy colors for children? Should it look at ethnicity, religion, gender or income in the target market?

Company ABC probably wouldn't start collecting data on its consumer's dining habits. There doesn't seem to be much correlation between dining and toy car purchases. Even if its employees had remarkable statistical modeling tools and could perform complex econometric studies, this data is unlikely to be important.

The most important data is the data that provides the greatest competitive advantage. Mining and refining data is not a cost-free process. Businesses should look for data that provides the highest return on their business analytics investment.