We all know that the daily amount of data generated is astounding, more than 25 quintillion bytes (that’s 30 zeros) according the World Economic Forum. A number that’s grown 10X since 2018. Every business relies on data for better decision-making. It underpins investment decisions, M&A deals, performance metrics and performance reporting. With data growing so fast, and it being a source of lasting competitive advantage, proven by businesses from the Rothschilds, in the 1700s, to Amazon today, how an organisation approaches and manages data is critical.
With so much of it around, it’s inevitable that the way data’s managed and governed will evolve, and fast. Underpinning how we manage and govern data is the way we approach it, or the framing we see it through. Vaccines are presented as being 90% effective, rather than not working for 10% of people, because the positive frame encourages adopting, the negative frame invites scepticism and refusal. When it comes to data, big data is the widely used and accepted frame.
Big data, to crudely oversimplify, implies all data as a commodity, in a vast cloud or lake, that’s intelligently investigated to reveal new truths. This isn’t true. Past records of address history are not as useful as current address data. Data is not a commodity, in the sense that it’s all the same, because not all data is equally valuable. Data, once poured into the lake, or present in the cloud, won’t automatically become clean and connected with other data, that requires work. And work is the real issue here, from the perspective of someone leading the charge for data in their organisation. No-one has limitless resources, so a data leader needs to consider where to focus their resources. Yet the framing of big data implies, to the non-experts who typically sign off budgets and agree promotions, that all data is equal, and if only we can get it all together, the value will flow.
As the volume of data grows, there are signs that a new approach to data is evolving too. One that’s a more helpful and realistic framing.
The data asset class
At The World Economic Forum in December 2020, 50 partners from 20 countries (including 10 governments), announced the Data for Common Purpose Initiative (DCPI) to produce a governance framework to enhance the societal benefit of data.
The project, led by Nadia Hewett, brings together many private and public sector individuals, like senior data leads from VISA, Fujistu, and PWC, along with data-influencers like Doug Laney of Infonomics fame (all at Anmut are fans), and members of academia.
The initiative aims to accelerate the use of data for common good, while enabling better degrees of user control over, and monetisation of, their personal data. They’re exploring a market-based approach with government-led data initially. At the heart of their work is the concept that data is an asset class, so they are exploring ways of valuing, taxing, and monetising data assets. As they say – “This would allow you to get paid at the moment of consumption, and simultaneously enable financial authorities to clarify taxable income. Under certain circumstances, this process could reduce bias in datasets and provide streams of income over time.”
An asset class is a group of distinct and different objects (physical or conceptual) that share the same characteristics. Framing data as an asset is closer to the truth and enables more sophisticated discussions and thoughts. It also, as the DCPI are doing, makes data fit into the way the world currently works – one founded on marketplace principles. This doesn’t just apply between organisations, in terms of taxation and trading, it applies within them too.
Stripped back, the job of leaders in a business, is to choose which assets and activities to allocate limited capital and resources to, to get the desired results. Data is just one of those assets, a powerful one because it enhances all the others by making decisions better. But if data doesn’t easily fit with the way a business allocates capital, then the value of data is going to be harder to realise.
Data asset governance
Other, far more powerful and influential entities are moving in the same direction. The EU Data Governance Act, for example, uses the concept of data assets throughout, including the concept of data altruism and the reuse of data as intellectual property.
In the US, the Department of Defense Data Strategy lists data as a strategic asset as the number one guiding principle. They are looking to leverage data to bring lasting military advantage, specifically stating data is not an IT asset, but an integral and essential part of their missions, which is of course their core business.
At a federal level, in the US Federal Data Strategy 2020 Action Plan the ‘data as an asset’ approach is also shaping their work. In their words – ‘the plan identifies initial actions for agencies that are essential for establishing processes, building capacity, and aligning existing efforts to better leverage data as a strategic asset.’
The UK Government’s new National Data Strategy is designed to enable the UK to better manage and realise the value from its data assets. Its mission is to build a world-leading data economy while ensuring public trust in data use.
The German National Data Strategy explicitly talks about making data a visible asset to deliver more value. Among its recommendations is a study of whether data as an intangible company asset should be shown as an asset on the balance sheet to create a different understanding of the economic value of data. This includes the quantity, quality, and uses of the data. Context is fundamental in determining the value of anything.
Data Asset Management
Whether it’s stated or not on a balance sheet, it’s clear that data is incredibly valuable. More than a third of Amazon’s sales come from its sophisticated recommendation engine, according to McKinsey. Netflix says its algorithmic recommendation saves the company $1 billion per year from service cancellations. Data is clearly one of their most valuable assets.
The official sign that data is being treated as an asset, in the private sector, would be its listing on the balance sheet. The balance sheet aggregates all of a company’s assets, liabilities, and shareholders’ equity. Data, as an asset, albeit an intangible one, should therefore be on the balance sheet. Yet accounting standards say internally generated intangible assets can’t go on the balance sheet, only the intangible ones that have been bought by the business.
A business seeking to formally put data on the balance sheet as an asset will have to wait for many years for accounting standards to catch up. Yet, as we’ve seen, the concept of treating data as an asset is widely seeded and spreading fast, especially in the public sector. There’s a shift beginning, away from data as a commodity, to data being an asset class that needs the governance frameworks in place so it can be managed to realise the most value, with the least risk and harm. This will impact on the private sector over time. Just like the accounting standards will change, over time.
Rothschilds and Amazon are two great examples of businesses using data to huge advantage. They are also great examples of how, speed of action drives advantage too. Most businesses born before the digital age face a choice. Do they wait for the world to push them in the right direction with accounting standards and government strategies, or do they move ahead sooner and earn the advantages that doing so bestows?