A Quick Guide to Data Monetization Strategies

By Data Science Salon

Netflix’s success with its series “House of Cards” and AI-powered ways to reduce energy consumption at Google server farms by 40% have a shared origin. Both cases are perfect examples of companies monetizing their data.

According to the IDC data, there were 64.2 Zettabytes of data created and replicated in the world in 2020. The amount of data created between 2020 and 2025 will be greater than the twice amount of data created since the advent of digital storage. 

Data bears a great promise of harnessing insights, powering up new business models, and delivering better performance. Yet in the end, every company stands before the question of how to monetize data.

Some answers to this question were delivered by Mario de Felipe, Chief Data Officer at Grupo ASV, during his speech delivered at a recent Data Science Salon event.

Being a summary of the speech, this post covers:

  • Data as an asset
  • Direct strategies of monetizing the data
  • Indirect strategies of monetizing the data

Data as an asset

“There are claims that data is the new oil. But I have bad news. Data is not a new oil at all,” comments Mario de Felipe. According to his experience, data can be considered a raw material comparable with oil, yet without certain qualities. 

“You cannot copy and paste a barrel of oil, but you can copy data. Data will not finish and they can be used again and again,” says Mario de Felipe. “Data is an eternal flame!” he adds. 

The fact that the data is replicable, reusable, and needs not to be transported makes it harder to precisely count the exact worth of the owned data. The situation is even harder due to both direct and indirect benefits granted by the proper usage of the owned data in the company. 

Data monetization strategies 

As mentioned above, there are both direct and indirect ways to benefit from the owned data. Both ways enable the company to earn more money and are not mutually exclusive. 

Direct strategies - get money for your data

Direct strategies are delivered by the straightforward earning on the owned data by getting money from them or getting access to some products or services granted by sharing the data the company owns.

  • Exchange and selling the data - in this model the company earns money by sharing the data it owns or exchanging it for some kind of service. An interesting example comes from Oikotie.fi, the Finnish job board. While the main income is generated by job postings, the company also enables the recruiter to buy additional information about the performance of his or her ad compared with similar postings. 
  • Increase sales using data - this way of data monetization includes techniques like upselling and cross-selling, aimed either to sell more to existing clients or to offer them products they normally don’t buy. According to the McKinsey research cross-selling is one of the most powerful income boosters for companies, accounting for 21 percent of the value companies derive from revenue synergies. 
  • Develop new products using owned data - the company can use the data it owns to bring new, more desired products to the market. An interesting example comes from Netflix. During the design process for the new online series, the company analyzed the most replayed parts of shows and films featuring Kevin Spacey to spot the moments the target group is watching the series for. The insights were later used to bring “House of Cards” to the market. 

Indirect strategies - let data augment your business

While the list above shows the ways to directly get the money for data, the ones listed below provide the company with savings and risk mitigation, not the hard cash.

  • Optimizations and process improvement - the data can be used to boost effectiveness and reduce costs of operations. An extreme example comes from Deepmind. The company delivered the AI that reduced Google’s cooling bill by 40% without any investment in equipment - only by applying AI-controlled power-saving policies. 
  • Fraud detection and risk mitigation - AI can support all institutions in mitigating risks and detecting frauds. Danske Bank for example was able to reduce the false-positive alerts regarding fraud detection by 60% and increase true positives by 50% by leveraging the power of the AI-based solution. By that, the company was able to redirect the efforts to tackle the actual fraudulent activities. 
  • Making more informed decisions - having better access to the data and information flowing through the company enables the management to make the decision faster, based on more reliable information. This is the core of business intelligence (BI) systems. According to Forbes, 54% of companies agree that BI is vital to their current and future initiatives.

Summary - Have a plan

“To start monetizing your data, you need a plan,” shares Mario de Felipe “Choose one of the strategies listed above and define the business use case for data.” he adds. 

At this point, the expert emphasizes the role of data science in monetizing the data: “The core of data science is to solve business problems - through the data” he concludes. 

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