Using data - and profiting from data - isn’t new. Just look at the history of accounting which can be traced to ancient civilizations. What has changed, however, is the way we work with data, facilitated with new technology and platforms.
Like oil, data can be discovered, extracted, distributed, refined and delivered. Unlike oil, data is not a finite resource. Far from it. Data can generate data. Even relatively worthless data have become valuable simply through sheer volume and flow.
Let's look at the five most valuable companies in the world: Alphabet (Google’s parent company), Amazon, Apple, Facebook and Microsoft. They are worth more than $2.3 trillion combined and collectively racked up over $25bn in net profit in the first quarter of 2017 alone.
How do these top 5 companies make money?
Alphabet (Google) and Facebook are effectively online advertising companies. They're success is driven largely by their use of data in targeted advertising. Together they are predicted to make $106 billion from advertising in 2017. This amounts to almost half of the world's digital ad spend and one-fifth of global ad revenue.
Amazon is mostly an eCommerce business but again, uses data to great effect to maximize CUSTOMER EXPERIENCE (CX) focusing on; value, convenience, selection and speed. This strategy has enabled it to expand its business model to include e.g. AWS and now original television Content - on which it will spend $4.5bn this year, second only to Netflix!
Apple is a hardware company playing data catch-up. A notable example is their recently announced partnership with IBM to facilitate the development of health-related mobile apps. Keep an eye on the apple watch and see how data supports that business and future desire for the watch. I know my partner - Sunita Shroff will buy the Apple watch purely for its health data.
Finally, Microsoft is arguably the laggard - it was the most valuable tech company in 1996 when I started my career - but has since fallen to 3rd place. Is this why it bought LinkedIn for $26bn? By acquiring the world's largest professional social network, Microsoft bought the data from more than 433 million LinkedIn members. It paid $60 per LinkedIn user.
See these graphs on the revenue streams for the big five tech companies here.
And the footnote.
So, these companies are building their fortune - and data is playing a key part.
Data is not the only part, obviously. Take data away from Apple, for example, and you still have beautiful products people want. But data enables these businesses to be top of their game - and without it, I'd speculate they'd quickly lose their ground.
So, what is the value of data?
Can we easily quantify this?
Microsoft, for example, paid $60 per user when it bought LinkedIn. When Facebook bought WhatsApp for $18.4bn, it worked out at $42 per address book. Can we use this information to place a dollar value on bits and bytes?
Unfortunately it's not that simple. There's a lot more to the business models that drive data value. Volume obviously has an impact - the greater the volume, the higher the value - but this relationship is not necessarily linear.
As Confluent states on its website, "In a data-driven enterprise, how we move our data becomes nearly as important as the data itself. With greater speed and agility, data’s value increases exponentially".
Confluent's vision of data moves value from this, to this:
I plan to explore the value of data and how organizations are monetizing data in my next few blog posts. This is a hot area. Data is the fuel behind the digital revolution and 4th industrial revolution.
Jaron Lanier (an American computer philosophy writer) called the info-harvesting platforms (Facebook, Amazon, Netflix, Google etc) "siren servers," - internet companies that "depend on accumulating and evaluating consumer data without acknowledging a monetary debt to the people mined for all this 'free' information.” This is an important consideration for businesses when it comes to business risk. If the product is the consumer, then the value of the business may be in jeopardy if the rules of data change. Again, this shows it is the processing of the data which has value - not the data itself necessarily. I'll explore this in future blogs...