Businesses now possess a treasure trove of data that they can convert into revenue – but only if they understand how to deploy analytics to enhance business value – turning insights into measurable action that provides value both to consumers and companies. This can be done in three main ways: by enhancing services with data-focused improvements; through specific data products, where data itself is sold; and by providing new, data-powered services.
Machine sensors, location information, purchasing histories, digital media, browsing histories and online behaviour – all are sources of data that have the potential to be turned into gold.
The opportunities are expanding even further as the traditional boundaries between businesses dissolve, opening the way to mutually profitable co-operation. However, before any business seeks to mine its data and convert it into a sellable product, it is important that all involved understand how to actually monetise it.
Monetisation as a data product
It certainly is not about using data that is already in circulation in a slightly better way. The difference is more fundamental. It is about using under-exploited data to break into new markets. Monetisation can be achieved by using advanced analytics so that data that was captured for other purposes can be reworked to provide new value, enabling an enterprise to offer new services and secure new opportunities. The data scientist behind it all may not have been seeking the answer to a specific problem but may have been exploring the questions that nobody within an organisation had previously asked.
A good example of monetisation as a data product can be found within the telecoms industry. Here, business-focused analytics are being used to reach beyond the traditional telecoms arena, and provide retailers with services that are not about phone use or data downloads. Instead, the telecoms companies have analysed footfall information triangulated from their networks to give retailers an anonymised and highly valuable picture of where people go within a location, such as a shopping centre, the numbers of customers walking into a store, the type of customer passing by, the time of day and what their interests might be.
Similarly, public transport operators can use the data to more closely meet demand at certain times of day and at specific locations, while local authorities can examine it when planning car parking facilities or managing public events. The data also gives advertisers directly relevant information about the movements of particular demographics and where and when it is best to target them.
In another field – manufacturing – the monetising of sensor data as a service is changing the nature of businesses and markets. Train manufacturers, for instance, can now provide predictive maintenance to rail operating companies by analysing the data transmitted from engine sensors. Originally, these data sets from component sensors were used to provide information to engineers for resolving problems after they had occurred. However, imagination and investment in advanced analytics has allowed use of the sensor data to build a bigger picture and predict when parts are set to fail and should be replaced before the train has to be cancelled.
This has reduced the huge expense and disruption from break-downs when trains are out on the rail network. Servicing is conducted in timely fashion in the depot, rather than having to be accommodated as an emergency. Of course, to take this step forward, the manufacturer could not just discard the expertise of the engineers, instead, it was brought into the analytical process.
As more manufacturers follow suit and get savvy about their data, they are changing the nature of their proposition, offering much more than just machinery. In transport, this means that the customer will increasingly buy a transport capability – supported by telematics – rather than just a train or car that they must monitor and maintain themselves.
In other areas of business, monetisation has taken the shape of developing risk profiles through the use of graphical analytics to chart the links between organisations and thereby provide a data product. Open source data such as company profiles at Companies House in the UK are matched against internally-derived information and supplemented with analysis of data from news websites.
These analytics techniques have permitted the relationships to be established between tier one and tier two organisations in manufacturing, for example, so the full ramifications of any activity or supply problem are understood. Analytics have also facilitated important advances in risk assessment in the financial services field, through companies matching trading data with payment details.
A creative approach
Although these examples of monetisation seem straightforward enough, they are founded on a more creative approach to analytics than organisations traditionally use. Indeed, the path to monetisation requires a company-wide change in outlook so that business people feel able to ask the questions they really want to, but previously avoided as impractical.
There is of course, a balance to be struck between asking more off-the-wall questions that yield results and the cost and time involved in providing the answers. The problem with bold ideas is that they often provide no return. But the five percent that succeed more than subsidise all the rest.
The response to this conundrum by the more innovative organisations has been to place their analytical challenges in a managed risk portfolio. This comprises standard business analytics that deliver incremental value and a number of “crazy ideas” that might be hugely profitable.
Many large organisations find it very challenging to give rein to innovation. However, it is a cultural shift that is required if a business wants to extract full value from data monetisation.
We are now well into the era of big data, so enterprises must look further ahead and start equipping themselves for monetisation or risk being left on the sidelines by their more innovative competitors.
Duncan Ross is director of data science at Teradata UK