What Is Data? Part 4 : A is for ‘Another Way Of Looking At DATA’

Here’s a clue

Imagine what a commercial organization knows about you through

  1. the data you have provided directly to them
    • name, address, email
    • maybe birthday
    • innocuous information like favorite color
    • in the case of social media companies, the richness of the information is unparalleled.
  2. the data they have collected about you through your engagement on their web site
    • what you bought previously and when you bought it
    • repeat patterns of buying
    • delivery address
  3. the data they have collected about you through your engagement on other web sites [3]
    • ‘don’t use name or password, make it convenient, sign on with Facebook/Google/Twitter
    • don’t even get me started what those companies know about you.
  4. the data they have bought about you from organizations like Equifax and Experian.
  5. the data that has been shared with them by their partners.

And then in those giant ‘farms’ of data in ‘lakes’ and ‘warehouses’, that data is constantly munged and morphed so that the data is ‘harvested’.

I find it objectionable that the data industry has taken terms from farming to describe what they do (yet another example of hijacking language to suit a corporate purpose), but that aside, imagine what they know …. or at least think they know.

Back to that sweater

Blue isn’t my favorite color. Every time I get those questions – I answer them differently. (Keeping a note of my answers in my password manager, just in case ‘ ‘for my security’ – they ask me what was my first car …).

They have noticed I buy a lot of sweaters …so I’m bound to want another one … right?

  1. NO. A couple of years ago I bought a lot of sweaters because I was living in Scotland, during the Winter. I now live in California. The data is old, irrelevant. Data lives. That’s why data warehouses are flawed.

Advertised right along with those sweaters is a special deal on a new Yoga book. (They don’t know that I bought all those Yoga books when I first moved to San Diego – when in Rome and all that – but gave it all up 6 months ago.)

Etc etc etc.

‘They’ think they know me. ‘They’ do not know me. The data they have, the profile they built is flawed to begin with and is frozen in time. The data needs to be cleaned and ongoing corporate responsibility that is both mind-numbing, pointless and expensive.

In short, they know the data as of a specific moment in time, with very little context.

They don’t know all the data. And the connections and trends are weak at best. Going back to the water analogy, imagine looking at a frozen glass of water. And recording that frozen glass of water 5 times in a single year, without the following;

  1. Knowing the geographic coordinates
    • Is the glass in the desert or the south pole?
  2. Knowing the time of day that the image was recorded
    • Is it midday or midnight? The variation of temperature is considerable
  3. Knowing the altitude
    • Is the winter a glass of water will freeze at the top of Haleakala on the island of Maui in Hawaii.

Assumptions are made, scenarios built, storytelling developed to profile you so that you might be convinced to act on something that is based on the frozen glass of water – all the time missing that none of the above mattered, (in this case) because the glass of water is in a freezer!

And that is the problem. I go out of my way to confuse the data warehouses and still they come at me. And curiously, despite all my precautions, still with an awful lot of accurate information.

From my side, a lot of their emails disappear into spam, or never arrive – I use a LOT of email addresses, filters, and rules switching them around constantly when I deal with companies online. And if they do get me, they just annoy me.

BUT – Corporations do this because there is a return. They might only get 1% response – but 1% of ten million is still 100,000 responses and any fraction of those responses that actually buy is revenue they wouldn’t have if they did nothing. And the cost of doing what they do is minuscule.

But there’s more. The use of data in this way is complex. So complex that we have entire industries devoted to looking at data and analyzing it. Palantir (who you might not have heard of), and Facebook,(who you probably have), being example companies that make up the ‘data analysis complex’.

If you want to know how Facebook knows so much about you .. take a look at this

Credit to Ben Thompson’s ‘Stratechry’. Read his post to better understand how Facebook knows so much about you. And they are doing that in full open view. Imagine what the two thousand person privately held company Palantir is doing behind closed doors!

There has to be a better way.

We need to step back from this whole debate of what data is because right now it is primarily being discussed only in terms of what it means to a business. We need to define it on our terms.

As our own individual energy is unique to each of us, so too is our data. It too is energy and can only be described with context, connection, color and a whole host of other ‘measures’ that I continue to document.

There is a better way. We know how it is going to work. The technologies to deliver a solution for people, not businesses are being built. And my next article will be about that but as a taster …

Imagine if every single person on the planet had their own dashboard that allowed them to indicate their needs, desires, wants and flag it so that anyone who felt that they could satisfy those needs, desires and wants could respond with an offer human-readable terms of the contract, pricing, expected timelines, etc.

Would that be interesting? I know a lot of people who are not just thinking that way – but are building systems that way; people-centered systems with people in control of their data.

The solution, needless to say, centers on the idea that data is energy. (The developers don’t think of it that way … but it is.)

  • [1] Please refer back to the caveat I offered at the beginning of part two and add ‘musician’ to the list.
  • [2] See the data parable in Part One.
  • [3] If you are a Prime customer and shop at Wholefoods with the Prime App, what part of your shopping experience doesn’t Amazon know about you?


John Philpin
John Philpin
JOHN'S career spans 30 years, 2 continents, and organizations as diverse as Oracle, Citibank and GE. A Mathematics graduate, John moved to California in 1990. He helps technology companies create, develop and deliver their story for fund raising, market development and influencer programs. He also works with businesses to ensure they understand, and are ready, for the ever accelerating changes that technology is bringing to their industry. John is a co-founder of Expert Alumni and gleXnet and long before futurists and industry watchers were writing about the impending challenges that industries were going to be facing, they predicted a perfect storm of issues like skills gap, declining work forces, the gig economy, people trained to do work no longer needed, demographic shifts, economic and social change, market upheaval and rapidly changing ways of doing work. From the beginning they have promoted the idea that massive change was coming to how organizations should think about their workforce, with a singular focus on simplifying the interface between people and their work. Understanding the challenges ahead of the curve, the solution was built to arrive at a better understanding of the greatest restraint to business operations - competence, not capital. gleXnet provides unparalleled insights into an organizations people and operations by flipping the problem from the perspective of people, not the business.

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