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Exploring revenue opportunities within enterprise data

Craig Macdonald | March 21, 2014
A business can develop a profitable data monetisation strategy – no matter where it is in the data value chain

Many companies are aware of the opportunities embedded in their enterprise data, but very few have developed active strategies to monetise it successfully. Now is the time! Market forces such as the decrease in cost of storing data, combined with an increase in the ability to analyse large volumes of data in real time, have created a field ripe for harvest.

To make the most of the new potential revenue opportunities, organisations should consider developing an informed, results-driven data monetisation strategy by keeping the following guidelines in mind.

Understand the potential value of enterprise data
When developing a data monetisation strategy, the first question a business should ask itself is, “how much is my data worth?” and then look to determine how much value their data would offer another company.

It goes without saying that exclusivity adds value. If a company has access to unique data that exists only within their own enterprise that could be of use to another businesses, it naturally could be attributed a higher monetary value. High data value is also attributed to captured consumer data regardless of exclusivity and organisations should consider, while factoring in data privacy concerns, whether they have access to any information on consumer behaviours such as financial transactions, retail purchases or geo-location data. Even greater value can be achieved by profiling the consumer with details such as name, job, or address as this can improve personalised services.

Data value also goes beyond unique and customer data. Additional elements companies should consider when determining data value include:

  • The transaction frequency of data — The higher the transaction frequency, the more valuable the data. For example, organisations that have access to data on high frequency transactions such as regular debit card use or habitual mobile internet searches can find this information of higher value than data retrieved from products or services that require less frequent transactions, such as car insurance or home mortgages.
  • Data accessibility — Making data as accessible as possible is another way to achieve higher value. Unstructured data such as texts, social media posts or call centre logs are often high in volume but low in value, as extracting insights is not so easy. Presenting this kind of data in an accessible format and in a way that’s readily scalable could automatically increase its value.

Find the market opportunity
Once businesses determine the value of their data, ascertaining their value proposition to potential customers based on where they are situated in the data value chain is the next important step when developing a data monetisation strategy.


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