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Trends and Outliers

TIBCO Spotfire's Business Intelligence Blog

12/11
2013

Unlocking the Value of Big Data

Many companies today are rallying around the potential for creating lucrative new revenue streams by unlocking the value of their data.

shutterstock 6223402 292x300 Unlocking the Value of Big DataWhile data analysis has the potential to be monetized, companies need to take time to truly understand the value of her data to ensure success, according to Sunand Menon, founder of consultancy  New Media Insight LLC.

He advises firms take four steps to successfully unlock the value of their big data:

1. Establish ownership of data: One of the most common mistakes companies make is assuming that if they collect data and store it on their systems, they own it.

“Or [they assume] if they collect data and then add a proprietary methodology to it, that automatically qualifies it as proprietary data,” he notes. “Or if they create analytics from raw, underlying data, and subsequently barter the analytics, they must be able to charge hard dollars at a later stage. All of these assumptions may be true . . . but are more likely false.”

Unless companies have written data contracts in place that allocate ownership, they might not be able to do anything with that data. To establish data ownership, he advises companies to enlist content specialists and legal counsel who understand how data is created, stored and could be commercialized. Data should be labeled as “data we own,” “data customers own” and “data third parties own.”

“Quantify (as much as possible) the value-add of any derived data versus the original data, in order to be in a better position to create mutually agreeable data usage and revenue share agreements with suppliers and co-creators of the data,” he advises.

2. Pin down who might value firm data and why: Firms should identify target customers by analyzing a broad set of potential users and interviewing customers to determine where data gaps exist for these groups and if firm data could fill a need.

Ask prospective customers to rank datasets and metrics according to how valuable the data might be to them and their willingness to pay for the data.

3. Set goals for monetization: Menon advises companies to then set a target that’s large enough to pursue and also worth the organization’s investment of time and resources. Consider the various ways to commercialize data and identify the ones that would be viable to reach the target.

“Understand whether the data provides more value than just the new standalone revenue. You may come to the conclusion that your original $100 million target was unrealistic, but $10 million is achievable,” according to Menon. “Sometimes the revenue may be small in absolute terms, but the data capability may be complementary to your current, core business. The value may be in the combination of the two, which can drive significantly higher core business revenues.”

4. Test and execute: Companies should identify areas where the risk for failure is high and create scenarios to test execution. Additionally, they will need to create tangible criteria to measure success and implement test programs with defined roles and responsibilities.

“The results of the test programs can help get you to a more informed view on whether you go ahead with implementation, whether you stop or whether you need to make some modifications to your business model and/or execution,” Menon concludes.

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