Why data will be so valuable in the future

‘Data is the new oil’ is an attempt to suggest how valuable data is. It’s an analogy that works when we consider data isn’t valuable in its raw form, it needs to be processed to become something more valuable. Oil is valuable because so much can be done with it, just like data. But there the analogy breaks. Oil is a limited resource, and once processed it cannot be reversed. Data is constantly and continuously being generated, and using it does not use it up. The same data can be reused repeatedly in different ways.

In many ways data is unique and non-analogous. Data is both what is processed by computers and what they use to run the processing. Nothing else in the world uses fundamentally the same thing to do the processing and be processed. Data is an abstraction of the real world, which means all data is interrelated with every other piece of data. Nothing else in the world is as connectable.

Conceptually, all data is connected. Someone buys a desk.The measurements of the desk are on a spec sheet used by the manufacturer, along with the safety standards the desk meets and what materials it’s made of. The manufacturer holds data on the employees that built the desk, including how much they were paid, when, etc. That employee’s bank also has data on how much and when they were paid, but also what they spent their money on. The retailer they spent their money with knows some of what they did with their money, including that they bought a desk. But because all these data sets are independent no one person or system sees how they connect. 

As more data becomes more interconnected its usefulness increases exponentially. But in order to achieve the interconnectedness of datasets and make them useful, collection, storage and processing have to be decoupled from each other. When competitive advantage comes from the collection, storage and processing of smaller specific datasets that organisations use to draw insights only relevant to themselves, interconnection is prevented. If data collected from numerous sources is stored in a way that is equally available to everyone then competitive advantage can only come from processing. Those organisations that have the capabilities to utilise insights from the analysis of huge aggregated datasets win out, but require an intermediary to store the data and prevent monopolisation.

Data Trusts work like a bank but for data rather than money. Just as no organisation keeps the money it makes, nor would they keep the data they collect. Industry standards would standardise data collection and laws would make it illegal for organisations to store data. Data would be held by these Data Trusts and made available only to those that contribute their data. Anonymised data is accessible in real time for processing by organisations to draw insights that enable them to make decisions that take account of an unimaginably huge number of data points. 

Data Trusts would specialise in particular types of data; retail, health, manufacturing, etc., creating a further layer of anonymisation and aggregation for organisations wishing to correlate datasets. Interesting new commercial models would develop around the cost of accessing data to take account of increasing returns mechanisms and the decay of relevance.

Data has a half-life 

Every piece of data that exists about a person, their behaviour, and any prediction has a half-life. Relevance decays over time.

Name, for example, might have a half-life of about a fifty years or so. In a hundred years I’ll be dead and my name will only be half as relevant as it was fifty years before when I was alive. My search history could have a half-life of between two weeks and two minutes. If I’m trying to find my nearest petrol station, the chances are that the results are most relevant between now and when I put some fuel in my car, and ten minutes later the relevance has halved and is only useful for agregating with other behavioural data. A year’s worth of transactional data about what I’ve bought each of the past fifty two weeks might have a half-life of five years if my purchase habits stay the same, but as those habits are likely to change over time the data set would also change over time with certain points decaying faster than others if I stopped buying certain items.

Understanding how each piece of data has it’s own half-life and how the relevance decays over time based on that half-life can help companies provide better personalisation and could be a means of deciding when data should be deleted to conform with evolving data protection regulations.

Catalysts of Change 

Graham Cooke – Qubit CEO

  • UK spend more per capita than any other country.
  • Demanding consumers.
  • Things changing faster than ever.
  • Does the customer drive the technology or does technology change the customer? 50/50 flipping change.
    • Internet was first big change driven by tech.
    • Cloud was tech change.
    • Mobile was consumer driven.
    • AI and block chain are the next big change, and they are tech driven.
  • Anything powered by data is like applying electricity to mechanical
  • Block chain decentralisation of trust
  • VR misses the tech/customer change drive, it isn’t here to stay.
  • Best way to stay ahead is listen to the customer’s
  • Switching industry is massive as lots of companies don’t satisfy customers
  • Competitors are a click away
  • Revenue 5% – 15% increase through Personalisation
  • Businesses need to serve customers better than ever before.
  • Data and knowledge about the customer
  • Sift through the data to make use of it.
  • 82% of businesses recognise that AI is going to be in their future
  • Stitchfix, preemptive sending of women’s clothing. Customer uses app to teach AI what they want. Customers get 40% of clothing from Stitchfix.
  • AI is about relationships between customers and automation.

Myf Ryan – Westfields Shopping Centre CMO

Using technology to remove pain points

  • If tech doesn’t remove a pain point or deliver a customer need, then we shouldn’t be using it. Don’t use it for the sake of it.
  • Discounts should be used to remove pain points, e.g. High prices, high stock.
  • Need to learn about consumers from their behaviour.
  • Provides first class customer experience.
  • Drive future engagement.
  • Amazon Go removes the pain point of queuing.
  • Are we solving a problem or enhancing the experience.
  • Reduce friction to increase conversion.
  • Keep learning.

Understanding the consumer

  • Achieving single view of the customer to deliver improved customer journey.
  • Costumers are channel agnostic.Important that people know that you know them.
  • Customers have to feel that they own their own data.
  • Challenges for achieving the single view of customer and then segmenting customers.

Daniel Murray – Grabble & Mobula

  • Future gazing on the next big mobile trends.
  • Ecommerce is built on infrastructure that doesn’t work for mobile.
  • Mobile web gets all the traffic, native apps get all the time.
  • How do we bring them together?
  • Grabble started with a social commerce website, then went to a mobile app.
  • Ecommerce has history, mobile has none.
  • It needs to inspire and delight your customers.

Rules for running an innovative startup

Rules for running an innovative startup
  • Sometimes intuition is more important than data.
  • Data and experience works together.
  • Mobile web wasn’t made for mobile.
  • Native commerce is disruptive.
  • Dwell time, discovery (on social apps), data (consumers give their data away in return for convenience), distribution, delivery (barriers torn down by mobile).
  • What’s wrong with mobile web?
  • What’s wrong with native apps? High churn, costly to develop.
  • Native apps create mini Internet for a brand.
  • Stream apps and Instant apps from Google.
  • What matters is where consumers spend their time.
  • Native in-app content for Facebook.
  • Instagram native commerce.
  • WeChat builds commerce experience in the app.
  • Mobula powers native commerce and is fully modular mobile first.
  • Don’t send customers to the website, host an experience in native apps.

Raphael Orlando – Tesco

  • Head to head with Amazon is not a strategy. How do you defend against Amazon?
  • Not really a retailer, they are a platform business.
  • Have to find pure water.
  • The basis for retailers to win: traffic, data, point of view.
  • 10% of groceries brought online in the UK
  • Sometimes digital should get out of the way and let the customer do what they want to do.
  • Think about what experience the customer wants.
  • Improvements in loyalty card and payment card on a mobile.
  • Stores of the future. Different vectors of change. Could be community focused, or marketplace focused. Community focused could be much more local and unique. Marketplace stores could provide outlets for other retailers.

Panel discussion

Panel discussion

What in technology excites you?

  • Pace of change. Shift in interface getting more rapid. Next ten years AR and VR.
  • Understanding what customers behaviours are offline.
  • Personalisation gives tremendous opportunity.
  • Understanding intent, along with behaviour. Understanding why they are doing it. Apply to look-alikes.

Biggest challenges

  • Legacy of embedded ways of working and mindset.
  • Lack of data
  • Prove value within the business.
  • Challenging assumptions.
  • How to use data to make decisions that benefit the customers.
  • If you ask a customer for a piece of data you should make use of it to give benefit to the customer.
  • The right data makes the right choices.

Catalysts of Change report 

Before we do Big Data we need to do Clean Data

Collecting, analysing, and making decisions with massive amounts of data is something digital and IT departments in all kinds of companies across all kinds of industries aspire to. But if the data being collected isn’t clean, isn’t trustworthy, isn’t reliable then it’s going to lead to questionable results or complete errors.