Many truly innovative businesses are now well advanced in turning their own data into new revenue streams, despite fears about the cost in time and resources.
But as other enterprises consider their first steps into this potentially very valuable field, it is worth being clear what is meant by monetising data.
Firstly, it is not just about using data you are already familiar with in a slightly better way. Instead, it is really about using under-exploited data resources to open up new areas of business. It involves using data – often captured for other purposes – to drive value rather than process, and to provide new services, and new opportunities.
Take this example: a train manufacturer was using sensors on its locomotives to provide retrospective information for engineers – when something went wrong they could then search for root causes. That might seem as far as it could go. However, imagination, determination and investment has allowed them to supply predictive maintenance support to the operating company.
This data analysis allows them to predict when parts are set to fail before the failure occurs, and helps reduce hugely expensive break-downs when trains are out on the rail network. Servicing is conducted in timely fashion in the depot, where it is straightforward, rather than trackside.
We are now seeing more manufacturers engaged in similar ventures, extending their reach and proposition to end-users. This could revolutionise our relationship with transport – rather than buying a train or car, the customer is buying a mode of transport and the manufacturer is selling the journey or capability, rather than the device itself.
The train company moved from viewing sensors as a source of readings for the after-the-fact use by the engineers on the ground, to one where it was part of a bigger picture that could, by means of analytics, be turned into a competitive advantage. Crucially, they did not discard the expertise of the engineers, they brought it into the analytical process.
Another example is a major telecoms company that has used business-focused analytics to create data products from its ability to monitor footfall information triangulated from its networks. It is able to sell anonymised information about where people go within crowded commercial areas – such as a shopping centre – so that retailers can find out the numbers of customers walking into the store, the type of customer walking by, the time of day and what their interests might be.
Another technique – graph analytics – is now being used to develop interlinked business risk profiles and to plot the hitherto obscure relationships between organisations. Some of the raw data for these products comes from internal and privately held information, and some from open source data from organisation like Companies House in the UK.
Information about the directors and key individuals in different companies is analysed and supplemented with data from news websites. This enables the relationships between organisations to be discovered, for example between tier one and tier two organisations in manufacturing. The practical outcome is we can understand the impact that happens if a major company does not obtain an order, not just for that company’s risk profile, but also for all the companies in its network.
If you are able to add in trading data and payment details the information gives an even more powerful picture of the flows between companies and the linked risks.
These examples show how data is being exploited by those who own it. Yet ownership alone is not sufficient; to be successful they have adopted a more creative approach to analytics. This is not about seeking the data that would help solve a particular problem, but about asking: "What are the problems that could be tackled?"
In order to achieve this a cultural shift has to take place within organisations. It is often the case that business people do not ask the questions they really want to, because they do not think the answers can be had.
Of course, there is a balance to be struck between asking the crazy questions that may yield results at some point in the future, and the cost and time involved in providing the answers. The obvious problem with crazy ideas is that nine times out of ten they provide no return. But what we find is that when space is given for exploring this type of question, the ten per cent that succeed pay for all the rest, and more besides.
In response to these challenges, the most innovative organisations are taking the sensible route of placing their analytical challenges in a portfolio. This allows them to balance risk and reward across the portfolio, so that they have standard business analytics that deliver incremental value, as well as the long-shots that can be hugely profitable.
It goes without saying that to succeed in these approaches an organisation must have the right technology and staff – but what is less understood is that the use of analytics for data monetisation must also have board level support, otherwise it will never happen. Historically, this has been difficult to achieve, as few senior executives understood the importance or value of data.
Clearly the key success factor for data monetisation is to deliver an environment that fosters data innovation, which is, admittedly, an aim that large organisations frequently find very challenging. But fortunately there are outside forces that help move us in this direction – the impetus for taking this step in large organisations can often be disruptive changes in their market. Suddenly the realisation dawns that a more radical response is needed and that new sources of revenue must be created, and using their data as an engine for change becomes an urgent consideration.
As we see these disruptive factors break down some of the traditional boundaries between industries, so we see the opportunities to monetise data increasing – provided that organisations are realistic, bold and ready to admit that they may need specialist help from data scientists.
