We should be paid for our Data and we should control the use of our Data.  This would upend the status quo but would deliver much improved efficiency.

Information accumulation and evaluation is not a uniquely human characteristic.  Our cousins in nature, distant and otherwise, are also blessed in this regard  but our capacity to perform complex tasks is premised on our data management skills and is a defining feature of what it is to be human.  From an early age we are acquiring data, assessing probabilities and drawing conclusions.  Data is integral to our experiences.

Some of our behaviour may be considered hard coded or instinctual but much of what we do is as a result of an internal machine that weighs, considers and then acts.  How could we evaluate relationships (from the sexual to the commercial and everything in between) without data ?  It is only natural that this data driven approach pervades almost all of our activities and extends beyond the interpersonal realm.  Who or what is to be trusted ?  Who or what may provide comfort ?  Who or what do we want to comfort ?  What should comfort cost us or earn us ?

Commerce is and has always been a data driven exercise.  What is something worth?  Who wants it?  Who has it?  How do they meet … be it widgets, services or money.  Preferences and patterns drive behaviour.  Data describes and information enables. It is the most basic of risk management frameworks and those with the data have always been able to leverage this asset for gain.

We currently occupy a “big data” space, but before we argue for the future of data let us touch briefly on its recent past.  As an example, banks lend money to those they expect to repay their debt.  How do they asses this risk?  Reputation and word of mouth – data points,  albeit “fluffy” ones, dominated the dawn of banking.  Over time the evaluation devolved into a more structured approach, with the creditworthiness machine a function of historical payment patterns and financial health.  The cost of debt, all things being equal, was lower for those who were able to feed the machine.  Data had value!  Payment patterns, purchase history and personal information is valuable.  The market craves information and prefers to target those who want it, those who can afford it and avoid all who cannot.  In our “big data” world personal data is priced implicitly (Facebook is free?), is purchased forever (Amazon never forgets?) and is broadly applicable (Facebook and Amazon are de facto supermarkets selling goods to all who they think might want them) . 

Consider a world where data is explicitly priced, purchased for a defined term and is narrowly applied.  What would this mean?

  1. Free” services come at a cost, even if that cost equals what the consumer is paid for access to their data.  This is already the case today but we would make this transparent
    • Some (perhaps many or even most) consumers may prefer to maintain the illusion of “free” or cheap services
    • However they do not lose complete control over their data
  2. Consumers control their data and it is priced and traded and subject to limits
    • I can sell my data
    • I can buy my data back
    • My data is my asset
    • Can we impose minimums and maximums in terms of price? For buying and/or selling? Would this limit abuse or constrain markets?
  3. Service providers continue to target consumers but must acquire data to be competitive. The power dynamics shift dramatically towards the individual.
    • Those who pay careful attention to their individual data market could maximise the price achieved
    • Those who pay no attention to their individual data market would still earn some income
  4. Data value will vary across many dimensions.
    • Consider the value of an undecided voter to a political campaign.
    • Consider the appeal of a cash flush interested car buyer to the auto industry.
    • Long term access to data is worth more than short term access to data. BUT not consistently or linearly (consider the examples above – the car buyer’s interest will dissipate immediately on purchase while the voter’s uncertainty may linger much longer)
    • Individual data points are almost worthless … pools of data acquire value
    • The combination of diverse and deep pools of data together with data analysis will drive value
  5. Limits create rules.
    • What if minors’ data is only usable for a period of 3 years?
    • What if ALL data expires after 10 years?
    • What access is granted to governments? In exchange for what services?  Can a government IMPOSE and INTERVENE?  If so, when?
    • What data is public?… and When?
    • Can the data buyer sell it on?
    • This is no cure-all for data abuse and the like, however it recasts personal data as an asset in the hands of the individual and it guarantees control – it is up to the individual to exercise it.
  6. The impact on incumbents who currently enjoy the benefits of poorly priced, cheap and evergreen data could be material.
    • Facebook, Google, Amazon etc. will have to explicitly pay for data … margins may suffer … but opportunities abound
    • In the absence of data exclusivity, what is the platform worth ? Is there a premium for scale ?  What are the barriers to entry when data availability and control moves ?
  7. Data structure is key. A common language for the transmission and manipulation of data is required.
    • The data repo will become the de facto instrument of information flow
    • How would we deploy the data pricing/ trading / ownership model ? Does the need call for centralised or decentralised models ?
    • Blockchain is often criticised as a solution looking for a problem …

Summary : Efficiency drives growth. The challenge of our time is to maximise the return from a limited (and perhaps dwindling) set of resources.  We believe that markets everywhere and prices for everything are a necessary condition for efficiency.  Data drives commerce and data ownership is a key allocator of value.  It is time to revisit how the cake is carved up!