Tech Index 2022: Data monetization

There are many ways to maximize the value of data, but organizations often define data monetization in a relatively narrow way.

Through this restricted lens, they see data monetization being all about taking a data set, licensing it to a third party and charging a fee for that license.

This definition is blunt and it’s one that, from a legal perspective, quickly runs up against a consistent barrier. Mention data transactions of this kind and many will immediately assume this means selling personal data, with the danger of falling foul of data protection regulations.

But increasingly, our clients are looking at a whole range of other ways to extract value from data, both within their own operations and externally in the marketplace, using a host of non-personal data that can be monetized with minimal regulatory risk. Alternatively, they are finding ways to comply with privacy by design requirements from the outset so they can tap the value of personal data in a compliant way.

Their definition of data monetization covers any effort to take data collected for one purpose and putting it to work in a separate context to increase efficiency and competitiveness.

It’s possible that this blurring of definitions may explain why less than a quarter of respondents to our survey say they are now making full use of data monetization opportunities. And it may be the reason why 65% continue to say they are making only limited use of data monetization, as they did in 2020.

More importantly, it may explain why just over one in ten say they are not currently monetizing data or have no plan to do so in future – a surprising finding in world that is increasingly data-driven.

We believe data monetization is going on much more widely than these numbers suggest, and that this is a trend that’s only likely to intensify as methods for collecting and analyzing data become more sophisticated.

Identifying opportunities

So where do the opportunities for data monetization lie?

We are seeing data used to increase operational efficiency and reduce cost across a wide range of sectors.

In manufacturing, companies transitioning to the Industry 4.0 model are increasingly using sensors, Internet of Things devices, enhanced connectivity, AI and data analytics to diagnose and quickly correct inefficiencies in their production processes.

In civil aviation, sensors are being used to capture real-time, in-flight data measuring the efficiency of aircraft systems and engines. If the aircraft is performing at 98% efficiency the data will be sent on to its destination so the right engineers are ready to reset and fine-tune systems for a speedy turnaround.

In the mining industry operators are using both live and historic data to analyze the performance of heavy machinery to schedule maintenance and repair programmes more efficiently.

In healthcare, we’re seeing data related to the treatment of individual patients being aggregated to find more efficient ways to deliver overall services and to improve software used to manage medical practices.

Data is also helping to improve customer responsiveness and drive brand loyalty in many fields.

Sometimes organizations are carrying out the data analysis themselves. But many clients opt to supply their data to an external broker who can then blend it with other data sets, including those of competitors, to get a much richer insight into customer trends in key markets. This is fed back to the original company to help it identify where it is meeting customer expectations and where it is falling short, and to identify where to invest money to maximize profits.

In some jurisdictions authorities are trying to unlock the huge stockpile of public sector data, some of which goes back many decades. The hope is that by making it publicly available to researchers, new ways can be found to make local and national economies more resilient. The EU’s Data Governance Act, for example, addresses access to public data and there have been similar attempts to improve access in the UK. If successfully coupled with the creation of common standards and interfaces, this could be an area of huge opportunity.

Specialist skills still a key concern

Our survey finds that only about 30% of companies who see opportunities in data monetization have specialist data scientists working in this area, roughly consistent with our findings in 2020.

Around half our respondents said they planned to hire specialists in future and the number saying they have no plans to recruit data scientists has declined slightly from 20% in 2020 to 17% today.

Access to talent remains an issue across the tech sector. However, we see a wider educational problem where data monetization is concerned.

Many IT and computer science graduates are leaving European and UK universities with first-rate data processing skills, but with little knowledge or experience of how to commercialize the data they are working with. Universities need to widen the curriculum to encompass commercial applications of data if they want to prepare students properly for work in the digital economy.

It's a lesson that has been learned in other jurisdictions. Israel, for instance, is producing many data science graduates who are absolutely focused on data monetization. They are really making their mark in the country’s rich array of tech startups.

The search for returns

Investing in data monetization must be supported by a business case clearly focused on delivering a real return on investment rather than just becoming an added cost.

Those of our respondents who see opportunities in monetizing data continue to believe it will have a positive impact on revenues, although the average expected revenue increase has fallen from 4% in 2020 to 3% in 2022. Despite that, one in ten think revenues could increase by up to 6%.

If those expectations look relatively modest, it’s worth remembering that, for the very biggest companies we surveyed, whose revenues run to billions of euros, a 3% increase is still a very big number.

More importantly the impact on the bottom line can be significant for companies of any size. If, as is very possible, they can find ways to monetize data at relatively low cost, they could potentially see a huge improvement in profit margin.