This article was written by Roger Camrass, of CIONET UK and a visiting professor of the University of Surrey, and is based on the conversations during an event on’ unleashing your corporate assets – co-creation is the best way forward’, at a dinner sponsored by Fujitsu and held in November 2020.
As we enter the digital era, monetising data assets has become one of the most important priorities amongst today’s IT and business executives. But the task is complicated by the explosion of data sources and volumes, the fragmentation of data sets held within legacy applications, and the shortage of data science skills. The event brought together twenty delegates from multiple sectors to discuss this important topic and to develop some shared insights.
Data monetisation – an accidental process?
David Jack, CTO and CPO of a data sciences company, dunnhumby (a subsidiary of TESCO and world leader in retail data analytics), shared a fascinating career that included some remarkable successes in monetising corporate data, much of which was accidental.
At Betfair, a leader in online sports betting, David discovered that establishing market prices for betting derivatives became a valuable bonus for the business. Due to the prominence of Betfair, these prices became industry standards and encouraged derivative traders to move their servers close to Betfair’s data centres so that they could seek commercial advantage measured in nanoseconds.
For Trainline, the knowledge of where and how passengers were booking their train tickets online led to valuable information about travel intentions. This enabled the company to target travel discounts to individual consumers and thus improve conversion rates for different travel services.
At Metapack, a company that is a lead integrator between retailers and carriers, David discovered that he could predict with accuracy where logistics blockages were occurring across the UK and help to alleviate them for the benefit of all parties. This added to the strength and value of the company in its selected niche.
In all these cases, corporate data assets helped to generate new sources of revenue and customer value. Dunnhumby handles trillions of retail transactions across the world and is constantly searching for new value sources. Some of the issues that David deals with day-to-day include shifts in regulations, modernisation of legacy systems and data security.
Describing the data-maturity curve
Recognising that data is an asset, Glenn Fitzgerald described how Fujitsu has developed a maturity model to help organisations assess where they are in its potential exploitation. The model includes four stages: data starved, data-sustained, data-empowered and data driven. Most companies are at the early stages of the maturity curve and need support to become data driven.
Fujitsu works with its clients to analyse which data assets might contribute directly to business outcomes, and how they might create solutions that can monetise these assets. Further information about the maturity model can be obtained from XXXX
Where does value reside?
The delegates emphasised the dual role of data in helping to improve internal operations as well as increasing customer engagement:
- Improving internal operations – examples were drawn from manufacturing, where data from sensors can help to identify potential failures in machinery well ahead of visible signs of deterioration. This is particularly important in reducing the cost of automotive warrantees by identifying potential areas of failure well in advance
- Increasing customer engagement – preventative medicine and healthcare are two important areas where patient data can help detect potential illnesses in their early stages of development. Cost of prevention is usually much less than that of the cure. Such data exists but continues to be fragmented and subject to privacy issues
What are the potential barriers?
Most of the delegates mentioned legacy as a critical barrier to data monetisation. For example, bank and insurance company CIOs stated that numerous acquisitions had left them with a multiplicity of incompatible systems that were hard to integrate around a specific customer account.
Data capture is also a challenge in many sectors. One publisher described the antiquity of his ‘titles’, some extending over hundreds of years. The firm recognised the value of the content but had little ability to digitise and sell this. In the case of a global law firm the corporate knowledge could have far greater value within and outside the various practices if it could be captured, codified and shared using digital technologies.
A manufacturer of consumer goods mentioned that his company had difficulty gaining visibility of the end consumer over existing customer channels. Data about individual customers remained in the hands of intermediaries and was not passed on to the manufacturer. This has prompted investment in online platforms to reach the end consumer directly.
Can eco-systems help?
Developments such as open banking hold out the prospect of new ecosystems that could bring small but highly innovative digital natives together with large and established incumbents to share and monetise customer data. Incumbents hold the majority of consumer data but the digital natives have the relevant business models and platforms to make full use of this. Few large financial services companies have the necessary funds to tackle legacy by themselves. Many will rely on partnerships with start-ups who bring new tools and capabilities to help resolve legacy issues.
Data sharing was mentioned by several delegates as a way of breaking down current barriers. For example, the need to tackle COVID-19 attacks in care homes requires numerous sources of data to track down how infections are reaching these homes. This is an issue across government, especially in public services such as the police and criminal courts where individual data can lead to rapid resolutions of issues such as re-offending. However, misidentification can be a problem when sharing such data.
Product traceability across complex supply chains was cited as another benefit of data sharing. For example, the efficacy of a specific drug can be monitored at each stage of the supply chain by testing. This vital data can be passed to recipients such as physicians and patients.
Findings answers to leading questions
The event concluded by discussing practical ways to realise value from corporate data both in the public and private sectors. Glenn Fitzgerald stressed the need to define a realistic goal or outcome for data monetisation, and to select the relevant ecosystem partners to achieve a practical result. Fujitsu organises workshops to help its clients analyse data resources and propose practical solutions to exploit these.
Most delegates agreed that a new wave of technologies could help achieve broader monetisation possibilities such as IoT and 5G which extends the range of data capture (e.g. from humans and machines); blockchain that aids privacy and helps overcome regulatory requirements; and artificial intelligence that expands the ability to extract useful insights.