Why measuring the value of data really matters

As George Bernard Shaw once noted, “Paradoxes are the only truths.” When it comes to data, it truly is a paradox. Data is both a key driver for value creation and business performance, but also one of the most underleveraged and misunderstood assets corporations possess. It’s largely unmeasured, mismanaged and underutilized. Its vast potential is not being realized.

The paradox of how data is – and isn’t - monetized, measured and managed is both an opportunity and a risk. While 90% of a company's valuation lies in its intangible assets, such as intellectual property, patents, brand, reputation and customer trust, there is very little agreement on how to quantify the value of its most important intangible asset: data.

Current economic and accounting practices simply are ill equipped to measure data’s value contribution because they don't factor in its unique attributes that make it different than traditional goods and services. Data is different than labor, capital or oil. It can be copied and shared infinitely at a cost approaching zero, can be used simultaneously for multiple purposes, doesn’t get used up, and generates more of itself when used. Data is what economists call a non rivalrous, non depleting, progenitive asset. There is no other asset with these incredible characteristics.

It's fair to say that the collaborative use of data to address some of the world’s toughest challenges may be facing some headwinds. While the evangelists from the demand side, largely from international donors, technology startups, forward looking economists, and academics continue to push forward, there's been less progress from the supply side, the private sector data holders.

Two chronic challenges are inhibiting public-private data exchanges: the lack of trust and the lack of evident and sustainable economics. Both data holders and data subjects have deep concerns about data privacy, security and its appropriate use. Even more, the measurable economics of public-private data exchanges have yet to be formally established.

It seems that the old axiom of "what gets measured gets done" may be at the root of why the private sector is still on the sidelines when it comes to actively engaging with public-private data exchanges. As a recent World Economic Forum report on data valuation notes, "The inability to measure both the actual and potential value of data results in complacency in data governance, analysis, insight sharing, and ultimately the inability to realize the data's full value".

Sadly, when it comes to public-private data exchanges, there's very little first mover advantage. Risks outweigh rewards, particularly at the outset. Failed public-private data collaboratives roughly outnumber those that succeed by a margin of nearly 10 to 1. The biggest constraint is the struggle to measure the value of the data that businesses contribute. As a recent MIT Sloan Review article states, "Organizations tend to focus more on the costs of storing, protecting, accessing, and analysing massive amounts of data than on transforming it, quantifying its business value or sharing it”.

What's needed are new approaches for enterprises to measure the value of the impact public-private data exchanges can deliver as well as the data therein. Developing a better understanding of how data collaboration can create shared value is vitally important. The increasing commitment from the global community of CEOs to advance the principles of stakeholder capitalism with impact oriented business models represents a perfect place to start that journey. As corporate boards increasingly prioritize environmental, societal and governance factors, business leaders are being told to deliver on sustainable outcomes for the planet and shared prosperity for its people.

So where do we start to more fully unlock the full potential of data? The mindset of enterprise data holders needs to shift in three ways:

1. One dimension of change is for business leaders to broaden the definition of what is meant by the term "data monetization". The question of "How can we generate value from data?" must be expanded beyond the narrow notion of selling data in return for direct revenue compensation. As the book Infonomics notes, data monetization needs to include the wider variety of both direct and indirect ways that data can measurably improve business operations, strengthen government relations, address cybersecurity concerns, enhance brand reputation, tighten customer relationships, and engage with local communities.

2. Industry leaders also need to pick a problem. Markets form when shared risks are collectively lowered. Providing access to new types of data can provide new insights and improve collective decision making for managing complex global challenges. With more commitment to addressing longterm systemic risks and less on data driven solutionism, the sooner we will all be able to deliver measurable impact to address the world's most complex challenges.

3. The third area business leaders need to focus on is to expand their vision beyond narrow corporate boundaries to the broader environmental and socioeconomic impact they can have on society. Some of the most impactful use cases for public-private data collaboration occur when the shared goal is to deliver measurable outcomes for the common good (and in ways that are equitable, inclusive, and accessible for all stakeholders).

New data exchange pilots in Colombia and India – which are being implemented at the World Economic Forum's Center for the Fourth Industrial Revolution Network – provide an opportunity to see the shift in the mindset of business leaders in the assessment of value of data. A new whitepaper from the Center for the Fourth Industrial Revolution Colombia, highlights a new public-private data exchange called Project Moonshot and how it is anchored on the commercial incentives for accelerating the net zero transition for the country's energy and utilities sector.

Likewise, in India, a new sustainable agriculture data exchange is identifying the commercial incentives for large scale data holders to equitably compensate smallholder farmers for use of data generated by them. By leveraging a common set of tools for collectively agreeing to shared outcomes, new approaches for identifying the business, operational, legal, technical and social risks in the collaborative use of data can be more explicitly identified.

It is imperative for business to develop a better understanding of the unique nature of data to enable shared value creation. By broadening their definition of value beyond revenue to include positive socioeconomic impacts in society, businesses will enjoy an array of collective social, commercial, and environmental benefits that will come with it. As noted in the new book from MIT Connection Sciences entitled Building the New Economy: Data as Capital, "We are in transition from big data to shared data." Public-private data exchanges will play a major role in accelerating that transition.