Exploring revenue opportunities within enterprise data

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...


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.

Companies whose primary capability is in generating rich, raw data at high volume should look at ways in which they can sell that data at little cost or investment. For example, a financial services business offering a ‘data on demand’ service would benefit from this approach as they could offer large volumes of data to their ecosystem of partners, fuelling the financial industry with raw data that can be used within enterprise applications and for financial analysis.

Companies with a core competence in processed data can also find market opportunities. For instance, credit card companies that collect data from billions of transactions have seen the data become more valuable beyond its operational use. In fact, some of these companies have established separate businesses with the goal of providing insights and analysis to its customers from real time transactions, helping investors for example, who are seeking to understand consumer purchasing.

Additionally, organisations that use techniques such as data mining, predictive modelling or analytics, are in a good position to process large quantities of data and help other companies gain business insights from the data. Being able to present these insights in a meaningful way, for example through customer segmentation analysis, makes the data even more valuable, enabling it to tell a story by giving insight into which customer segment is more profitable or customer’s shopping habits.

Now: go-to market!
A business can develop a profitable data monetisation strategy - no matter where they fall in the data value chain -- as long as they focus on the right go-to-market approach suiting their business and potential customer needs. To meet data monetisation goals, businesses need to evaluate their core competency, the opportunities at hand, and understand the impact of playing at a specific stage in the data value chain.

Posted by Craig Macdonald, managing director, marketing data management and data monetization, Accenture Interactive

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