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Persuasion Trumps Dissuasion, Data Builds Value in Networked Systems

by Jodee Hale-Schmid

September 8, 2010

Authored by David Kuketz, EIM Client Partner – Southeast, Utopia, Inc.


 

Ever heard of the “network effect?” It’s a principle coined by John Hagel, III (co-author of “net gain” and “Net Worth” with Arthur Armstrong and Marc Singer, respectively). It’s based on Metcalf’s Law (value of a network is related to the square of the number of connected users as follows V = n(n-1)/2 = n**2 –N / 2). It means simply that there is an “increase in the value of a product or service to future users as the number of other users adopting the product or service grows”.

Data is that kind of product and when provided as a service, it’s that kind of service.

Hagel introduced the “amortization effects” principle ,which is that which reduces “the average cost to develop, produce, or deliver a product as the number of customers expands.” Data has amortization effects.

Finally, Hagel said that “Learning effects drive down operating costs as businesses over time discover new ways to operate at lower cost.” Data has learning effects.

So let’s look graphically at how data has created value over the last century …


In the “old days” people traded shells for goods or bartered 1-on-1 their time for something of need. These are 1-to-1 transactions. Large-scale business was not possible, unless each participant had deep pockets. Data was “1 shell” for “1 handful of potato seeds”. You have a unit of measure, a description and a price. The transaction involved the exchange of hard goods for hard currency – high friction.

Next, forging way ahead to the time of Alexander Graham Bell, we have business-to-business and business-to-consumer transactions over the phone line, still 1-to-1 styled transactions; however the connectivity could be 1-to-many, in discrete 1-on-1 events over time.

Because of other simultaneous inventions, such as trade credit, hard currency no longer needed to be part of the transaction which of course helps speed up business and reduce the friction (transaction costs).

When only two phones existed there was one connection, with five phones there were ten, with ten there were forty-five, and with 100e+6 (phones) there are 4.99e+15 (connections) – but nobody can actually make a call like that! Seems more like Web 2.0, right?

Then comes the personal computer and Al Gore’s internet – or was it (D)ARPA(NET)? Anyway, this enabled “B2B” and “B2C” transactions that were nearly many-to-many, in nearly real-time – using credit cards (Pcards) the cost of transactions begins to drop significantly. And there’s still a unit of measure, a description and a price for each transaction. You see data growing in prevalence in all business functions – purchasing, sourcing, operations, maintenance, sales, and marketing, accounting, etc.

Here there is a divergence in the emphasis on where investment should be; brick-n-mortar or click-n-order; applications, hardware or data; web sites and ecommerce and social networks? Truth is, every form of business model requires good data, and the right blend across the spectrum of media. People have become discouraged from participation in the next generation due to fear, uncertainty and doubt – but look around and you see the transformations, you see the successes.

Then along came highly interconnected businesses, VPN networking, private online marketplaces, public markets online, Amazon, eBay, iTunes, and so forth – highly connected networks with highly complex, collaborative streams of all kinds of data; data that is creating incredible value, extreme value.

Because of huge advances in computing, networking, applications, banking, finance, business processes, government intervention (or liaise faire), people and policy, we have the ability to mine data, create decision support systems in ‘real-time’, and even apply predictive algorithms to inventory management or even marketing. Wow!

Data is persuasive, rich in content and in context – and it can be shaped in a way that considers our past behaviors to help us make decisions on wh at to do next. Sometimes it is too persuasive and the suggestiveness and psychology of the message needs some fine tuning. Sometimes the data simply isn’t proper so while the applications may be right, you get garbage in – garbage out results.

Data has come a long way as have the systems that surround it – and together have created trillions of dollars (shells) in GDP, economic expansion, and better quality of life for mankind – well, maybe we’re not there quite yet, but if we do it right (sustainably) then hopefully QUALITY will rise above QUANTITY in how we humans measure our successes and failures, versus just chasing the dollar, which after all is just a promissory note written on paper. Paper currency is going the way of the dodo.

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