DATA economics: risks of pricing to ZERO

Commodities were targets of wars, of many kinds including colonial ones. This was infortunate. Today the economics of commodities is structured into commodity trading and their associated market places.
This was the physical world, and still is...

NOW comes the digital world, and the ubiquitous digital part of any economic activity in any sector...
The new commodity is DATA, or more precisely SOURCE DATA.

Recognise with me that an easy but complete model of the DIGITAL ECONOMY builds on three pillars: software, networks, and data.

https://www.xing.com/communities/posts/digital-economy-how-does-it-work-1009091263

Software is valued, so are Networks, but what about the raw data, the source of sources?
Take the case of consumers using a widely spread digital environment: they want their maps and guidance, their calendars, and written or visual communication anywhere anytime. To get this basic requirement of today's life (digital, partly digital at least) they give away their data, which are a precious input for others to make lots of money with it.
Unrefined oil is not as precious as refined oil from the gas/petrol station, but is oil free? And oil comes from the ground, not from people themselves. So why should DATA be free to those making money with it?

Remember this horrible global economic activity headquartered in Bristol, UK? Free manpower exported to the New World. Shame on mankind to have allowed for it.

Free is and should always be suspect unless it's transparently auditable, as in CBPP (commons based peer production as wikipedia). Otherwise "make it free for me" is at the source of this untransparent integrated economy which ruled Sicily, the birth place of my grandfather, for too long.

In technical terms, set a Lagrange multiplier (price can be seen as one) to zero, and the constraint which could also have revealed opportunities in economic terms, disappears.
Here is a scheme showing how the "give me your data for free" scheme works in the Digital Economy.

https://www.xing.com/communities/groups/big-data-economics-c6b9-1073836

NOW, here is a first intent to see how to price more systematically data, and progress to wards data market places as the physical economy did when it created commodity trading, and their associated market places.
This book has been published last month. It builds on use cases and data categories which have a price and economic schemes, to suggest new ways to address data pricing, using, ecosystems, etc.

http://www.amazon.com/Data-Economics-Towards-Market-Places-ebook/dp/B00QD7LMO2

XING: starting a big data economics group

Big Data Economics: THE BOOK

This book addresses the economics of Big Data, beyond a project by project cost analysis.

The fuel or input of any Big Data system is source data. In many cases of current use of Big Data, excepting Open Data, the data is obtained for one single purpose, and used by a single organisation, because of the likely absence of an open and transparent market where such data can be purchased or sold. Our objective is to analyse the objects, agents, and mechanisms at play for source data within the Big Data context, and give an economic perspective rather than an Information Technology engineering perspective to Big Data.

The nature of data will be discussed, as a good which is digital rather than material, can be replicated at virtually no cost, and is not burnt or consumed in any irreversible way by its being used in a computation.

Requirements for exchange mechanisms of source data, and associated rights of use, will be discussed, with rules for transfer and use, control retention or not by originating owner, privacy and other basic requirements and constraints on data access.

Pricing and contract requirements for categories of data rights will be described, and illustrated on a few examples.

Big Data Economics, Towards Data Market Places

Nature of Data, Exchange Mechanisms, Prices, Choices, Agents & Ecosystems

by Renaud Di Francesco, PhD

Executive summary

This book addresses the economics of Big Data, beyond a project by project cost analysis.

The fuel or input of any Big Data system is source data. In many cases of current use of Big Data, excepting Open Data, the data is obtained for one single purpose, and used by a single organisation, because of the likely absence of an open and transparent market where such data can be purchased or sold. Our objective is to analyse the objects, agents, and mechanisms at play for source data within the Big Data context, and give an economic perspective rather than an Information Technology engineering perspective to Big Data.

The nature of data will be discussed, as a good which is digital rather than material, can be replicated at virtually no cost, and is not burnt or consumed in any irreversible way by its being used in a computation.

Requirements for exchange mechanisms of source data, and associated rights of use, will be discussed, with rules for transfer and use, control retention or not by originating owner, privacy and other basic requirements and constraints on data access. 

Pricing and contract requirements for categories of data rights will be described, and illustrated on a few examples.

The agents acting on data and the related ecosystems will be presented with a model for roles and attributes.

Particular cases with already established digital commerce of data, and corresponding pricing, and right management schemes for digital objects will be discussed in several “verticals” or specific sectors of the economy.

Big Data, driving decisions:THE book

Big Data, Driving Decisions

with direct impact on the real-world,

this is the deep transformation ahead of us.

Renaud Di Francesco - Linkedin

Hello, I am Renaud Di Francesco, an Engineer and Technology Manager, with a record track in  Digital Innovation (Patents, PhD).

I would like to introduce you to an aspect of Big Data: 

-how Big Data drives decisions

With billions of humans connected through mobile and fixed networks, the challenge is now shifting to connecting our things, any thing, in a meaningful way. This is what Big Data, enabled by Machine to Machine connectivity, is all about:

• making sense of all bits of knowledge acquired in real time, digested, processed, retrieved, understood through Analytics.                   
• building a decision and action process, based on the result of this analysis.
• feeding back commands or actions to the real-world.

My book "Big Data, Driving Decisions" will take you through use cases, technologies, economics of big data, at the cross-roads of society and business. 
It is available on Amazon/Kindle Editions.

This is a blog on data economics

1) Because Big Data matters

2) Because Economists have not yet fully addressed Big Data, 

3) Economics of Data fuelling Big Data Operations

4) Demand - Supply of Source Data

5) Economic Mechanisms

6) Source Data Market Places