"Perhaps CIOs feel more comfortable starting with internal data sources, because the thinking is that much of it is already being managed by ITV," said LeHong. "However, in many cases, these internal data sources are not controlled by IT at all. For example, call center recordings, security camera footage and operational data from manufacturing equipment all represent potential internal sources of data to investigate, but they are usually not under the control of IT."

Therefore, CIOs and their teams will need to work with the business to fully understand the pockets of data that are available. With some creative thinking, even data that is already captured data can be made richer. Enterprises that use big data technologies can afford to keep the full, raw data, building up rich sources of data that can provide new insight. However, CIOs will need to ensure that there is always a clear business purpose and outcome for storing this new data.

Internal data has an additional advantage. It is a good starting point for big data projects because the enterprise already owns the data, and it may be easier and/or less costly than accessing external data sources. Also, compared with external sources, the enterprise will be more likely to trust the internal data because it comes from enterprise systems, logs and other enterprise assets.

Some enterprises have used big data technologies to make existing analytics faster. Although technology may enable faster speed, getting business value from that speed often requires process changes.

Gartner research shows that early adopter enterprises that implemented faster analytical capabilities changed their processes to get the maximum benefit from the speed. For some enterprises, the speed in analysis provides the ability to include a full week of sales data when running analytics, such as price/promotion/markdown optimization. In the past, because these optimizations would take a day to run, Sunday's sales data often did not make it into the calculations. Now, with the ability to run the optimizations in minutes, enterprises can include the full week's data — making their optimizations immediately up to date with market activity.

"CIO's must ensure that big data projects that improve analytical speed always include a process redesign effort that aims at getting maximum benefit from that speed," said LeHong. "Before pursuing big data investments, ensure that the evaluating team has a clear understanding of how faster analytics will lead to an improved business outcome — and build this into the business case."

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