Data analytics: the new win-win-win

The power used to lie with those who possessed information. But these days the winners are those that recognise and exploit the value of this information. Businesses in the sharing economy have internalised this principle and geared their business models to innovative forms of data analysis and processing. Not only have they achieved obvious success this way, but they’re posing a growing threat to the business establishment, forcing it to question its business models and rethink the way it approaches data and data protection.

Which way is the data wind blowing?

The power used to lie with those who possessed information. But these days the winners are those that recognise and exploit the value of this information. Businesses in the sharing economy have internalised this principle and geared their business models to innovative forms of data analysis and processing. Not only have they achieved obvious success this way, but they’re posing a growing threat to the business establishment, forcing it to question its business models and rethink the way it approaches data and data protection.

This paradigm shift requires two things: bringing together data from many different sources, and state-of-the-art data analytics. Only by combining the two can this data be transformed into knowledge and economic advantage in the form of new data models. There’s even statistical proof that companies that systematically make decisions on the basis of data and modern analytics are able to gain a clear strategic competitive edge.

The truth of game theory

At this point I’d like to make a brief digression into game theory. Game theory models decisionmaking situations where an individual’s success depends not just on their own behaviour, but on other people’s as well. It’s in the sharing economy that game theory has found its link to reality. Peer-to-peer providers have realised that the utility to each individual increases if everyone acts together and positions themselves as part of an ecosystem. To do this you need sufficient volumes of data, knowledge of the relevant market mechanisms, and the right methods and processes. You also need to be able to think in terms of network structures way out of the box.

Winning in the latest dimension

Data analytics enables individuals to exploit the know-how of the mass. The following example illustrates what this means in real life. On behalf of a German doctors’ organisation we at PwC have developed a predictive model for the medication of multiple sclerosis patients. The actual tool runs via an easy-to-use app that physicians can use to improve the way they prescribe medication for their patients and manage their wellbeing more efficiently and systematically. The app draws on more than 190,000 data sets for other MS patients, the experience of hundreds of doctors, and the medical knowledge of other parts of the pharmaceutical industry. The expert tool interprets this universe of data using complex algorithms to make reliable predictions about the patient’s drug tolerance and the development of their health.

This kind of predictive, personalised data analysis benefits everyone involved: the patient gets quicker, more successful treatment and improved quality of life; doctors are able to make better recommendations and boost their reputation; the pharma industry has an opportunity to refine its knowledge of the efficacy of its drugs; and finally, health insurers benefit from lower healthcare costs.

More transparency, more trust

In various respects transparency plays a key role in the sharing economy. The co-consumption model is rooted in the principle of trust in the peer group. If you’re able to offer transparency on the way data are used and commercialised and at the same time add value, you can gain the trust of co-users and win new business.

Reliable data and transparent data handling are also key in dealing with regulatory authorities and other third parties. Doing business in industries such as financial services and healthcare means complying with a raft of rules and regulations, including stringent requirements governing the quality and integrity of data throughout the entire cycle from the moment they’re captured through their use and subsequent deletion. Adequate data security is also crucial. Regulators, supervisory authorities and other stakeholders are shifting their focus from mere reporting to completely transparent data integration. While this is already business as usual for players in the sharing economy, organisations with conventional models are a long way from adopting these practices.

Redefining value creation

Consumers leave their mark everywhere in the digital world. They’re present in virtual marketplaces via their smartphone on an almost permanent basis, and regularly use their credit card for internet purchases. Providers for their part bring these data together to create a tangible profile of their consumers as a basis for future offerings. That’s nothing new. What’s different about the sharing economy is that unlike conventional businesses, it’s able to embed these data in the context of a comprehensive, networked data ecosystem. The sharing economy has grasped the fact that no single player has all the data necessary to derive the greatest possible benefit from it and thus boost value creation.

Data, knowledge and value-added

Any company on the path to a partially digitised or sharing business model first has to define its role within such a data ecosystem. For example it might decide to use external data such as geolocalisation or social media activity to better understand the needs of its customers or optimise customer service and advice. It might also consider acting as a supplier of data, selling it to third parties, for example in the form of data on communication, movement or consumer behaviour.

The monetisation of data – in other words using it to earn money – is a skill that sharing economy providers like Facebook and Google have perfected. Other companies, especially conventional businesses, first have to acquire or unleash this talent, This requires courage, the right culture, uncompromising out-of-the-box thinking, and the willingness to tread completely new paths, some of which will never have been trodden before.

Defining and transcending the limits

In purely technological terms there are virtually no limits on data analytics. Most of the fundamental methods, both in statistics and in more recent disciplines such as machine learning and artificial intelligence, were developed decades before the digital revolution even started. But steadily falling costs of storage and computing capacity are allowing unimpeded growth in the volume and speed of data processing and the possibilities it’s opening up.

The limits of data analytics are more psychological in nature. The question of whether and when artificial intelligence should be allowed to take control and completely supersede human decisionmaking has complex ramifications, and there’s no straightforward answer. People used to make decisions on the basis of knowledge and gut feeling; these days decisions can also be taken using cutting-edge expert systems on the basis of data and models. In the sharing economy, Uber’s vehicles are still driven by humans. But it’s only a matter of time before they’ll be replaced by self-driving cars – cars that are brought in by the community. This dimension raises completely new questions; safety and liability are only the most obvious.