The Information Recession

By Chris Gledhill on The Capital

Chris Gledhill
Jan 15 · 8 min read
Bales of garbage data

I predict we are heading towards a global Information Recession this decade. A financial recession happens when a GDP (Gross Domestic Product) of a nation or region contracts over a period of two quarters, like in the 2008 Credit Crunch. An Information Recession happens when the GDP (Gross Data Product), the value of information produced by a nation or region, contracts in a “Data Crunch”. Let me explain…

🗄📈 Exponential Data

For those tasked with predicting the hard-to-predict, futurists themselves are an ironically predictable bunch of people. If you invite a futurist to give your team a presentation you already know the slides you’re going to get. There will be the “Time taken to 1m customers” slide showing how it used to take years to get 1 million customers but now <insert trendy app of the day> can do it in 2 seconds. There will be the “Smartphone vs Apollo 11 slide” telling you how the tech in your pocket is more powerful that the guidance computer that took us to the moon. There will be the “Crowd of People” slide showing some scene in the past of people doing people stuff, versus the same scene today with everyone staring at their phone screens. There will be a handful more slides showing stuff like a grainy black and white picture of some guys needing a crane to lift 1MB of data storage, some inspirational quote from someone like Steve Jobs and there will always be something from Henry Ford about faster horses. All these slides are intended to show the exponential growth of technology and how the impact of such a change can be far-reaching and hard to predict. My all time favorite slide though is the “Internet Minute” graphic.

There are tons of different versions of this slide (probably one created every internet minute!) but they basically show some mind-blowing stats about how much data is created by all of us humans collectively every minute of every day on the interwebs. How many opinions are tweeted a minute, how many avocado toasts are Instagrammed, how many woah’s are hit on TikTok etc. The amount of data being created is so huge it is driving whole fields of study in data science and machine learning to try and make sense of this data with utopian/dystopian projections. But is data the same as information?

🗄💰 Gross Data Product

You have probably heard the phrase “Data is the new old”. It is actually quite an old phrase, apparently coined in 2006 by Clive Humby, but has grown in popularity these past few years and was projected into popular lexicon in 2017 when The Economist ran a front-page feature on the value of data calling it “The world’s most valuable resource”.

It confirmed what we have come to understand, that data has value. Ever since then there have been many attempts to quantify the value of data, realize the value of data, decide who owns the data and protect the data. Much of this has happened over the heads of us lowly consumers. We as individuals have yet to realize the value of our own data but it seems large tech firms have grown expertise in harvesting our data.

One thing that was clear early on is that data itself has limited value. This is most eloquently put by the famous Gaping Void data/knowledge graphic and subsequent spin-off memes.

This graphic extends the metaphor of how data on its own is like crude oil straight out of the ground. In its most basic form, it has some value but refined and processed like oil into substances like petroleum it is more valuable. However, where the oil analogy apparently breaks down is in the supply and demand. Whilst Oil is a fossil fuel and therefore a finite resource that is hard to obtain, Data is apparently freely available and increasing exponentially. We will never run out of data, right?

Well not exactly. We’ll never run out of raw data given our ability as humans to create mountains of data just by living and breathing (quite literally if you have a fitness tracker!) but the value of the data we have at our disposal may not always track with the volume of data. It is perhaps better to leave the Oil analogy behind and consider data as the new money. We are getting close to this model, whereby “money” becomes a container for value, which could just as easily contain fiat currency or data. When economics is thrown into the mix we get a better understanding of the value of data holistically but also the factors that determine that value. I call this the Gross Data Product and I calculate it as follows:

F = Value of freely-available information  
P = Value of restricted information
c = Cost of acquiring P
b = Pace of societal change
t = time

What this essentially says is that the sum of all data produced is the value of “free” information in the public domain plus the value of “paid for” information (e.g behind a paywall or private API or in a private DB) minus the cost of acquisition of paid-for info. An exponential decay function is then applied given that data, like a radioactive element, has a half-life. Just cash in a vault will get devalued through inflation, data at rest loses value over time. A simple example of this would be something like a customer profile. As time passes the customer will change in their personal preferences and circumstances and so their old profile becomes less relevant.

🗄📉 The Data Crunch

So what might cause a data crunch, i.e. what might restrict the flow of data or diminish the value of data?

We heard this year about Russia’s plans to segregate itself off into a separate country-wide Intranet “Runet”. We know about China’s “great firewall” that limits the flow of information to/from the country and China’s new social credit system that adds consequence to creating ‘disagreeable’ data. We heard how India shut down their own internet 95 times last year citing public order reasons. We’ve witnessed news information sources shift to paywalls in an attempt to keep monetizing their content. We have seen many other media and entertainment sources move to “freemium” business models where customers have to pay for anything above the basic access. We are seeing a demographic shift of younger consumers away from more public social networks to closed social networks as they begin to understand online personal reputation management. At the same time, we’ve seen traditionally public social networks like twitter start charging for access to their APIs and toying with platform usage restrictions. We have seen in the academic arena, particularly in the US, a battle against the rise of academic paywalls. We have had governments and regulators implement tougher laws and controls on the handling of data like the new GDPR rules. We have witnessed the rise of “Fake news” and state-sponsored disinformation campaigns. We are getting to grips with emerging field of Deepfake where AI can be used to generate apparently legitimate information.

All this starts painting a worrying picture. Access to data being increasingly restricted and the value of that data itself diminished through erosions of trust and quality. Where the internet began as a worldwide web of information it is increasingly becoming a segregated mashup of walled gardens and shadowy places. This is the Data Crunch and I predict this decade we will enter into the first information recession that humankind has witnessed since the Dark Ages.

🗄💻 The Data Economy

I would have liked to have titled this section “The Data Renaissance” where I talk about a utopian future where we overcome the Data Crunch and get back to freely sharing trustworthy information for the good of humankind. Unfortunately, that’s not going to be for a while. Where we are heading towards is a prolonged period of a Data Economy, that is a global digital ecosystem in which data is created, processed, and exchanged by a network of large vendors for the purpose of deriving value from the accumulated information.

In the Data Economy, new businesses will emerge whose sole purpose is to create data (e.g sensors & imagery) and sell it into the data market. Other businesses will learn to manage data flow like they do cashflow and they will need to come to terms with the concept of not mastering their customer's data but instead pulling it from a variety of sources. E.g. Today a takeaway delivery company might build customer and restaurant profiles. In a Data Economy, the customer profile will be imported, the restaurant lists and details will be imported. The Takeaway delivery company won’t actually need to store customer records or details of restaurants — it’ll be pulled in real-time and they will simply act as a broker.

It is not hard to see which firms are currently leading in this new economy (See Above Table 2 from UN Report on the Data Economy) but there will, of course, be new firms emerging in this area. With my #FinTech hat on I see this as a huge opportunity for banks to move into this space. As the value of individuals’ data surpasses the value of their monetary deposits, it would make sense for banks to provide a place to deposit one’s data.

There is quite a considerable upside to all this from a consumer perspective. Not having one’s information spread about the internet could improve the security of that information. The ownership of information will be re-balanced towards the consumer and Master-Data-Management or Single-Customer-View will become consumer umbrella services, not managed at a company level.

Image Sources: The Internet Minute by Lori Lewis and Officially Chadd, Bundles of Garbage by Alex Fu, The Economist Front Page, The Gaping Void Data/Knowledge Illustration, Dominant Players in the Data Economy Table.


The Capital

The Capital (former Altcoin Magazine) is a social financial news aggregator powered by Bitcoin

Chris Gledhill

Written by

#FinTech Influencer, Keynote & TEDx Speaker, Writer and Advisor. #FinServ #InsurTech #WealthTech #PayTech collaboration@chrisgledhill.com

The Capital

The Capital (former Altcoin Magazine) is a social financial news aggregator powered by Bitcoin

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