Business books reviewed

 
Reviewing Economics & Business books, 

as input for Big Data Economics

Let me share with you some titles:

-1) Planet Google by Randall Ross
This book reviews the growth of Google, as an addition of goals followed through, starting with indexing the information of the Internet to make it searchable, and going through Youtube, Googlemap, etc...
The book is quite well structured, allowing to understand the systematic pursuit of business objectives rooted in facts (science & engineering, market). The exploration and chartering of the world's information, as completely as possible, performed by google is an amazing piece of work, and this book describes it very well.
Interesting read for anyone interested in the economics of big data, naturally...

-2) Googled, the end of the world as we know it, by Ken Auletta
This book has a very different approach to the one above. It is more a classical business story told well, with details of interest. I have liked the beginning where the author sketches a biography of the two founders, and their family background in advanced mathematics for one (father lecturing on Riemannian geometry, mother with advanced mathematics & biology degrees) and computer science for the other (two parents university professor & lecturer).

-3) An Introduction to Sustainable Development, by Peter P Rogers, Kazi F Jalal & John A Boyd
This book has a few chapters which connect well with the problems of starting economic analysis where market prices may not be available or not be the only criterion:
their chapter 9 on the economics of sustainability, chapter 10 on externalities, valuation and time externalities, and chapter 11 on natural resource accounting.
However, it is not a toolbox from which one can extract what we need for Big Data Economics, at best an eye opener, and an encouragement to develop models in certain directions, proven to be usable in a domain different from Big Data, with the commonality that it still has some "terra incognita" features yet to be explored and mapped.

-4) Fighting the banana wars and other Fairtrade battles by Harriet Lamb
This book may interest you because the Fairtrade scheme brings a new set of economic standards and criteria in the food market ( and other) arena: respect the planet, respect the people (producers or consumers), introduce sustainability and risk reduction in an otherwise fierce competition with commodity price volatility.
Why is it relevant to the analysis of economics for big data? For the reasons above, but also and probably more importantly because big data as source data (source data sets, flow) is the commodity of the digital age, and it is interesting to build on the experience gained in the area of physical commodities and ways to address their price volatility (and potentially chaotic availability depending on crops, good or bad weather, natural disasters).

-5) Marx, the key ideas, by Gill Hands ("teach yourself" series)
Do not smile before you know why I put this book here.
I started from the reflection that today the economics of the digital markets is governed by a production equation adding the costs of software to the costs of networks, and most of the time ignoring data costs or not paying much attention to them.
Marx may be criticised: his ideas may have led to human catastrophes. However as an economist he managed to convince everyone that Labour aspects (labour costs, workers' condition, etc) needed extra care in the age of the industrial revolution. He added Labour as a key variable into the production equation where other costs could be called "Kapital" and Assets.
Hence if we want to highlight "source data" in a context where "my software is valuable and your data needs to be free to me" or where yet another conflicting view says "my network is valuable, and your software needs to pay for consuming it", we may learn from how Labour as an economic parameter was recognised as a key driver of the coal & steam age.

DATA OWNER

This description of a data "input" value chain assumes that data is owned by someone or by an organisation. The ISO-IEC JTC1 Study Group on Big Data has been very clear that there should be a universal attribute to data specifying its owner(s).

The data owner could be an individual: for instance, consider the case of personal data owned by a person. More broadly the data generated by objects owned by a person are likely to be owned by this person: for instance the current geographic position of my car. This means that there are expanding circles around people, with data in such circles. This creates a natural link across the areas of the Internet of People, where people communicate and interact with each other or with "the Internet", and the Internet of Things (IoT) with sensors and actuators, and machine intelligence all connected to serve (hopefully) the needs of humans.

It starts with the core, the body, with body area sensors, continues with anything wearable, and beyond to anything owned, within physical or virtual reach.

The data ownership could be shared by a group, call it social, with a defined aim, for instance producing CBPP (Common Based Peer Production) as in the world famous Wikipedia. Note that the P2P value project of the EU addresses the topic of organisation and mechanisms at play in CBPP, with over 300 such social groups studied.

An other interesting case of data ownership is the Industrial Internet, where companies generate for their own operation data, which they use internally (mostly), in schemes such as a supervised distributes system, using typically a control room. Today a telecom network, a transport network (railways in particular, but also metro, road air and sea transport), an energy network are in this category. Some subsets of such operations data may be eligible for the company to release it for specific use.

Data generated by wearable devices is also a category of interest to the business and consumer communities, with multiple purposes being envisaged already (sports, well-being, health, new forms of communications) and many more to come.

DATA COLLECTOR

This can be one of the few large Internet brands. This can also be any company in operations such as the ones above. This can also be an individual aggregating their own data in multiple ways, for multiple purpose: current and future (forensic data, the extension of the collection of post cards and pictures into the general data domain).

Governments are data collectors. Organisations: public or private, acting in pursuit of business or social goals are data collectors.

Even when the data is accepted as not being subject to a price tag, its use must conform to established rules and laws.

A data collector builds consistent and structured sets from individual potentially unstructured data vectors.

This entails quality control of the source, or to use other language the "veracity" of the information. 

The aim is to prepare the input of an efficient data processing.

DATA USER

A data user is typically an organisation or an individual performing analytics on data sets. For this purpose they need to either directly collect data sets, or buy rights to access such dats sets for their defined purpose and scope from data suppliers, which are the data collectors, or data brokers acting on behalf of the data collectors (retail role).

Data users needs data sets suitable for their need. This is the demand-side in economic terms, and the data collector or data broker is the supply-side.

Note that the use of data through analytics may lead to decisions, with in turn such decisions producing data sets in the command domain, for remote and distributed execution of such commands implementing the decision taken.

For instance real time systems with a feedback loop, also called automated systems, or optimal control, do not only "observe the world" through IoT sensors, but they act on the world through actuators, and supervised control, typically with supervision in a control room, as explained above.

SCADA systems (supervisory control and data acquisition) are an important case of operational data use.

DATA PATH

Naturally, when the data collector gathers data, and forms data sets, initial data D0 is transformed into D1 and the set accessed by a user for a specific purpose and scope is D1* (optimised or limited for this use).

Hence a data path from extraction, collection, shaping, homogeneising and fitting to user purpose.

PAYMENT PATH

Users purchase rights to use the data for their own purpose and scope, and payment flows possibly through the data collector, with part of the payment remunerating the data owners.

The organisation of payment and retail is being studied, and a publication addressing this subject is being prepared.

Copyright R. Di Francesco, 2015 

VALUE: Progressing the economics of source data

I will discuss briefly here how the fuel of any big data system, source data, can receive the needed attention, from an economic modelling perspective.

Interested readers are referred to:

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

Naturally, big data systems are engineered as Information Technology solutions, with the associated cost engineering, on a project by project basis. My assumption is that as big data becomes pervasive, the re-use of data and sharing across multiple use ranges, will make a lot of sense, and let the suppliers and users benefit from economies of scale and critical mass effects.

QUESTION 1: THE VALUE of data

The first question to ask is about the value: are these data I am using of value? What is the value of the data I have extracted? What would be the value of additional data, and where could I get them?

Economists following in the steps of Adam Smith distinguish two types of value:

• the value of use
• the exchange value

Obviously, a good which you need for a certain purpose, has a use value to you. If you are thirsty, you need water, for instance.

As for the exchange value, this is a good which you can sell, or buy, because it is traded, and comes with a price on a market. For instance cocoa was an asset used as money in Precolombian civilisation of Latin America. The classical example is diamond.

For data, let us give examples in each of these two categories of value:

-Use value

An automated system, supervised from a Control Room (say a train network, an electrical grid, a telecommunication network) uses for its own purpose industrial data. This data has an obvious use value, however the data owners seem to be little keen on releasing such data, even for a price, to third parties. This data use case is perceived to fit under a dominant "value of use" and not any identified "exchange value" yet.

-Exchange value

An entertainment content such as a movie, materialises (virtually :) ) as a file, which is a data-set. It has an exchange value: rights are sold to cinemas, TV channels, and end users, for viewing this content. In package media form (Bluray, DVD), it can be resold even, by consumers.

Naturally there are many more questions regarding "big data economics" and leading "towards data market places". The following book I have published recently on Amazon/Kindle Editions addresses some...

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

Debate on the Economics of Data, at TELECOM PARIS workshop on 12 January 2015

Here is a summary of the lively Debate on the Economics of Data hosted by Telecom Paris university on 12 January:

Industrial data?
Currently industrial data are part of closed systems, and the suppliers and users of such systems are very protective about sharing with third parties.
However, some intents are being made.

Luxury data?
Some data can be seen as Giffen good, where high price is expected and desired as part of the value proposition (luxury car, etc). Some "gem" data exist.
High value financial information is part of this category.
Beyond any open economy, State Security data has a somewhat comparable exclusive status.

Key sectors?
Business intelligence is a very active market for big data solutions already.
Health Care and Care for the ageing population is an other area, where big data solutions could:
-support the people in care
-support the carers
especially in the Ambient Assisted Living framework.

Data management?
This is a key question. In particular ensuring that data owners keep control of multiple, possibly cascaded use.

Big data economics, 

Workshop at Telecom Paris Tech, 

12-1-2015, 

Paris, France

Event
100+ registered attendees
Introduction by Patrick Duvaut, director of research at Telecom Paris-Tech
Presentation by Renaud Di Francesco
Speech by Pierre-Jean Benghozi
Participation of Yves Poilane, director of Telecom Paris-Tech




Presentation summary

The scope of big data, is broader than business intelligence, and extending towards:
-real world to digital, analytics AND decision, feedback to real world
-real time

A change in needed technology portfolio is happening, beyond NoSQL and search technologies, with other technologiues determining success:
-signal processing
-maximum likelihood decision methods
-optimal control
-real time system engineering

The digital economy relies on three pillars, two of which have identified pricing schemes and economic mechanisms:
-software
-network
however, the third one, data, does not always have recognised value, and economic mechanisms.
For instance, what is the price of an electrocardiogramme as usable data? What is the price of my geographic position?

Nevertheless in some sectors and categories, data can have pricing schemes and economic mechanisms:
-content (e.g. movie) industry
-news
-loyalty schemes
-etc...

Starting from these chartered territories of big data, one can start considering adapted economic schemes for new data categories, which are not yet priced and covered by economic schemes.

The software licensing scheme offers a starting framework for data contracts, which cover rights on data.
The enforcement of rights is helped by Digital Right Management systems granting authorised access to the data.
The target for a data economy to work efficiently is the development of data market places, where data collectors, data owners, data users, and data processors, meet as data offer has to meet data demand.
The raw material or commodity market places established for physical goods give a reference framework from which data market places can be derived.
Moreover, in some categories, digital data market places are already in operation. For instance Getty Images buys and sells pictures, which are a special case of data.

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