Peter Leonard considers the asset value of data and the way to make sure it truly has value
It is often said that data is ‘the new oil’. However, this catchy epithet is misleading. The methods of valuation of most things, including of scarce commodities such as oil, gold and clean water, do not capture many unusual characteristics of data. These unusual characteristics taken together make data unique, different to other forms of property, both tangible (chairs, dogs and pencils) and intangible (software, books, trade marks and patents).
The greatest paradox of data is that it is the most important asset class of the 21st century, but it is not an asset class at all. Indeed, data cannot be ‘owned’ in the traditional sense of proprietary rights recognised in common law (i.e. my chair, dog and pencils) or conferred by statute (i.e. copyright).
And although data is not given a value under generally accepted accounting principles, the market capitalisation of both ‘unicorns’ and ‘data giants’ show that public financial markets and venture capitalists see value otherwise.
There are many other odd features of data.
Most property derives its value through being closely held, an aspect of managed scarcity. But today data often derives value through sharing. At least some data sharing within multi-party data ecosystems is required today to deliver almost all online services (and particularly internet of things applications) and most offline supply of products and services.
Data can be infinitely reproduced and shared at effectively zero replication and sharing cost. Data does not derive its value through scarcity. Data often derives value through created value of ‘discoverability’: transforming raw data to enhance capability to find it and to link it to other data to share it and explore combined data sets for correlations, such as attributes of a user to whom the data relates, or to link to that user’s other transactions. Often in data analytics projects 75% of the cost is cleansing and transforming raw data to make it discoverable: the high-end work of then analysing it is the smaller part of the program budget. Discoverability may be created within a privacy protected environment: in many cases, substantial data value can be created whether or not particular individuals are identifiable.
Increasingly, data derives value not through direct application, instead informing development of methodologies and algorithms for use on other data, such as the use of ‘training data’ to inform machine learning applications.
Data has little inherent value. Data value is derived not by what it is, but by what can be done to create value with it and then endurably capture that value by denying others the ability to do those things while not also exciting regulatory intervention that strips value. Possible value depleting interventions include enforcement of competition (antirust) laws, statutory creation of new ‘consumer rights’ over data, and enforcement by individuals of rights of access to, or portability of, personal information about them as held by businesses.
A legally enforceable right to deny others the use of data that is controlled but not owned may be created by contract, through application of principles of equity as to protection of confidential information, or by exercise of statutory rights in trade secrets. However, these are rights to deny others access to or use of data not proprietary rights.
So what is ‘data value’? Data derives value to the extent that a data custodian has both technical capability and legal right:
? to capture useful data points of sufficient granularity and number in a readily usable form, and
? to bring together that data (ie make the data ‘discoverable’), and
? to transform and then analyse the data, to derive meaningful insights and to enable actionable decisions to be made by the data custodian (or by another person with whom the custodian shares the information); and
? through first mover or other advantages and operating under the cloak of confidentiality or trade secrecy safeguards buttressed by contractual provisions, to deny others the ability to do those things, while not exciting regulatory interventions.
To summarise the current state of data science as applied to customer data:
? The value of data is derived through increased availability of data capable of aggregation and merger with other data sets to provide information in a form readily assimilated and used by humans (such as visualisations and other value-added presentations).
? Through data analytics methods of increasing sophistication, data acquires additional value. For example, algorithmically generated customer segmentation analyses and tools enable service providers to improve their ability to define and then target increasingly granular customer segments and to differentiate as to price and other terms offered to those customer segments.
? Data is also more readily available for analysis because it is more ‘discoverable’ as data taxonomies are standardised and as data extraction tools refined. There is more data that can be discovered and used. There is a rapidly expanding better range of already developed and tested data analytics tools and methods.
? Data is more readily available because businesses interact with each other and with consumers through increasing flows of consumer data and because each businesses is increasingly algorithmically driven in its own operations (therefore requiring better integration and availability of data across the business).
? Data value can be realised through improved ability of business to understand characteristics (attributes, preferences and interests, inter-relationships) of their customers and to infer characteristics of ‘lookalike audiences’ of prospective customers.
? Tools and methods for analysis and presentation of actionable insights are now widely readily available, including powerful tools readily available to consumers in the form of apps on smart phones or access to service comparison engines.
As new competitors and intermediaries emerge, every service provider will need to make, and regularly review, a number of the key strategic decisions about customer data that they collect.
? To what extent should the provider share the value that the provider can derive through data: for example, by empowering the provider’s customers to make better, more informed decisions, and meeting demands by customers for enhanced access to data to facilitate customers analysing that data themselves?
? Should a provider respond only to regulatory compulsion, or should a provider strive to differentiate itself from its competitors by addressing demand by (some) customers for greater access to, and control over, data about them?
? Is it viable to keep customer data within a provider-controlled data ecosystem? To what extent will customer demand or regulatory intervention be such that certain customer data should be proactively made available into a more open data environment? If so, how much data, how made available, and to whom?
? How does a provider make data more available while adequately protecting its customers from security risks? In what circumstances should customers be taken to know and assume the consequences of such risks? More specifically, how does a provider verify that a customer properly understands the risks of conferring agency upon an intermediary to conveniently access sensitive financial data about a customer? How does a provider verify that an intermediary (such as a price comparison engine) purportedly authorised by a customer to access certain sensitive financial data about that customer is so authorised and will handle that data with due care?
? Where data is shared, and particularly where value is created through joint endeavour, is it jointly ‘owned’ (in the legally incorrect sense of control and ability to deny to others) and if so, what does this actually mean (given that business people, commercial lawyers within jurisdictions, and different legal jurisdictions, exhibit radically different views as to attributes of ‘joint ownership’).
Achieving good information management and negotiating fair and balanced data deals is not easy.
That is why transparency of rights and use and good information management will be a key differentiator of industry leading data businesses of the future, regardless of the trajectory and pace of development of global privacy laws.
Operators with corner-cutting or slapdash data management processes will wither away through actions by private litigants or regulator action, mistrust of business partners or erosion of consumer trust.
The business stakes are too high not to do data deals and data management really well.
Peter Leonard is a data, content and technology business consultant and lawyer and principal of Data Synergies. He was a founding partner of Gilbert + Tobin. Following his retirement as a partner in 2017 he continues to assist Gilbert + Tobin as a consultant.