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.
Source: Computerworld UK
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