Data monetization has been frequently cited as one of the leading trends in data and analytics. According to Hortonworks, ‘data will become a product with value to buy, sell or lose,’ and ‘there will be new ways, new business models and new companies looking at how to monetize that asset.’ Data monetization even tops IDC’s industry prediction list, with the market intelligence giant noting that ‘by 2019, 40% of IT projects will create new digital services and revenue streams that monetize data.’
Reading all of this, it would be easy to think that this is really going to be data monetization’s year, and companies of all hues and sizes are sitting with their finger on the trigger to start flogging off their information assets to the highest bidder. But are they?
Data monetization is not a new phenomena. Companies have been buying and selling data since Moses was in short pants. What has changed recently is the increase in the volume of data being collected by companies, as well as the velocity and variety. And this is only going to increase as adoption of IoT goes mainstream, with billions of connected devices generating massive volumes of data and opening up new opportunities to find value in it.
The challenge facing businesses is how to get this data, and/or products based on the data, to market. The real question is, though, is this challenge too great? Are they going to be able to develop and exploit new opportunities for monetization, and should all companies even really be looking to monetize their data in the first place?
Data can be monetized in one of two ways - directly and indirectly. The majority of organizations’ data monetization efforts have, to date, focused primarily on direct methods where it is packaged up and sold on to other organizations. For example, selling on a sales lead database would see a customer buy the data ‘product’ once, and continue to use it as is. This works well when the data rarely changes. In industries where the data changes more frequently, you could sell access to the data stream itself, such as a stock ticker.
However, directly selling it isn't the only path to data monetization, nor is it necessarily the best for companies who are not naturally information service providers and need to think more broadly about how they can utilize data to generate profit. These require more indirect methods to make sure information translates into economic gain. A common example is embedding data along with tools for analyzing it in the products and services they sell, as Walmart does with its Retail Link trading partner portal, which provides its suppliers with access to its entire sell-through data.
Whether directly or indirectly, though, your data isn’t going to monetize itself. Firstly, you need a cohesive organization-wide strategy for data productization, with everybody aware of the monetization goals. The technology also needs to be in place. Data monetization initiatives usually involve a significant amount of data that requires heavy-duty processing power from the likes of Hadoop. You also need people with the right skill sets to use this technology. This includes data scientists and technicians capable of ensuring the data is clean and consistent, as well as building, testing, and running the analytical algorithms and predictive models that will produce insights. They can also review current and potential systems/applications that show the potential to be monetized. The sales team will also need to be trained up in how to sell your data products.
Before a business considers any data monetization strategies, though, there are several fundamental questions that they need to ask themselves. Firstly, what are the privacy implications and do they actually have rights to the data they are trying to monetize? In the European Union, privacy laws cover all data, whereas laws in the United States are more industry specific - focusing on financial information, health information and children. Failure to abide by these laws is likely to result in severe penalties, including large fines and, in some cases, criminal prosecution. There is also the question of who owns any data. Data gathered through IoT sensors in particular can pass through many hands before it reaches the end user, and all may believe they have some claim to its ownership.
Another potential risk is reputational. Customers need to trust that you are using their data discreetly, and some may see the sale of their information as a violation. While people tend to accept a certain level of data tracking as the cost of using services like Facebook and Google - and this level of acceptance is only going to grow as the practice becomes more widespread - there has been wide scale debate about how this data is used, and occasional outrage.
Ultimately, data is a valuable commodity, and companies would be remiss if they did not try to exploit it in every way they can. Will it really take off this year? Given the complications around privacy and the ongoing search for new ways to monetize it, it is likely to take longer than many expect. There is no need to rush, though. There will need to be agreements between persons and entities involved in the creation and processing of data to determine who has claim over it and each party’s obligations. Companies must also be careful to ensure that they have the infrastructure in place before they begin. The initial outlay on technology and skills is also large, and companies will need to think hard about whether it is really right for them. It’s better that everything is properly considered, because mistakes could be hugely costly.