White paper "From Big Data to Big Busine$$"

1. WHITE PAPER FROM BIG DATA TO BIG BUSINE$$ PAPER 1 : Fad or Performance Enhancer ?

2. Chapitre 1 - p.01 LIVRE 1 - LIVRE BLANC DU BIG DATA AU BIG BUSINESS

3. PAPER 1 : Fad or performance lever ?

4. Table of Contents - p.04 FROM BIG DATA TO BIG BUSINESS - PAPER 1 Why this paper ? A shift in management thinking Big Data definition Disruption or evolution ? Big Data history Form follows function Processing weak signals Big Data Projects p.06 p.07 p.07 p.08 p.08 p.08 p.09 p.10 CHAPTER 01 Big data, background and principles p.06 A closer look at existing, untapped information repositories Proliferation of raw, internal data available Proliferation of public, external and purchasable data Open Data Monetizing data Full-scale data cross-referencing p.20 p.21 p.23 p.24 p.24 p.25 CHAPTER 03 The new uses created by Big Data p.20 p.13 p.14 p.16 p.17 p.18 Big Data’s 3Vs Beyond the 3Vs: the 5Vs Beyond the 5Vs: the 3Ps Data, information, knowledge and wisdom… What is the difference, what is their value ? The accumulation of data seemed meaningless but use has changed all that. CHAPTER 02 Data p.11 p.26 p.27 p.29 p.30 p.30 p.30 Big Data hardware architecture specifications Big Data software architecture specifications Big Data database specifications No ready-made database Many tools, each specialized in one field Big Data architecture, specific to Big Data? CHAPTER 04 Architectures and algorithms p.26

5. What to remember about Big Data? The 10 key points p.39 p.40 CHAPTER 07 How to convert Big Data into Big Busine$$ p.39 p.32 p.32 p.32 p.33 p.34 p.35 The return of EIM (Enterprise Information Management) How to get started? Valuing data by dedicating it to business The new Big Data jobs From “punishment” to career prospect The relationship between MDM and Big Data CHAPTER 05 The Big Data jobs p.31 p.36 p.36 Heads: the hope for a dynamic sector that will boost the whole economy Tails: the privacy debate CHAPTER 06 Big Data or Big Brother? p.36 p.05 - Table of Contents PAPER 1 - FROM BIG DATA TO BIG BUSINESS

6. 1 Especially since Big Data involves new forms of reasoning such as, forms of inductive reasoning (see page 8). We can quite safely refer to Big Data as a new philosophy and as whole a revolutionary approach to marketing. CHAPTER 01 Big Data, background and principles Why this paper ? Big Data literature abounds; this is a sure sign that Big Data’s importance is strongly felt by the market as a whole and across the world. Howe- ver, even in cases where documen- tation is of high quality, it is usually mostly descriptive in nature and fo- cused on exaggerated, near-apoca- lyptic concerns associated with the exponential growth in data and data sources. These approaches do not help understand the real challenges posed by Big Data or how companies can exploit them. Even though, at this point, it is hard to predict what the future holds, we are convinced that Big Data1 will impact heavily on companies and civil society in a number of increasingly changing ways. By rapidly “cutting their teeth” on Big Data, currently still in infancy, businesses will not only master the phenomenon, but also learn, how to make the most of it. The aim of this white paper is to provide companies with key insights that will help them approach Big Data, not as a “mythology”, but as a power- ful performance optimization tool that can be adapted to their specific contexts. We hope that this will help readers lay, or maybe even validate, the foun- dations for a smooth, controlled in- tegration of Big Data into their com- pany’s ecosystem.

7. A shift in management thinking The Big Data evolution is extremely important to companies. More than a simple trend, it is a revolution in thinking, a crucial contribution to the management arsenal that will funda- mentally change the world of business. Its obvious impact on marketing should not obscure consequences of a perfect command of the subject on other fields such as analysis, management, production, supply chain management and RD to name but a few. This major revolution in thinking is however ill-served by an abundant literature that tends to either censor the new field’s too-technical vocabulary in order to conceal its complexity, sometimes to the very detriment of compre- hension; or, on the contrary, to go too deeply into this complexity and its even more technical 4 components (business function, technology, databases and statistics) and lose the reader. The risk then is for readers to erroneously think that the Big Data subject is either too generic to constitute a real innova- tion or too innovative for useful application to day-to-day business. As a result, Big Data2 “is perceived as a problem by companies” when in fact, it is a solution. The aim of this paper, the first of a series of 6, is to describe the impact and uses of Big Data in a simple and clear manner. Instead of impoveri- shing the narrative, we will explain the jargon in order to make the advantages of these new tools accessible to all. This first paper is dedicated to the Big Data phenomenon in general, with each chapter detailing aspects of the subject a little further. Subjects that will be dealt with in subsequent chapters are: • Paper 2 on data, Big Data’s essential fuel • Paper 3 on Big Data uses • Paper 4 on Big Data architectures algorithms • Paper 5 on Big Data professions • Paper 6 on data confidentiality, user protection and ethics We will provide both a global (in this paper) and detailed (in subsequent papers) view of Big Data in order to make it accessible to all professionals who want their business to benefit from this new approach and its tools. Big Data3 definition The term Big Data refers to a new discipline that is at the crossroads of seve- ral others: statistics, technology, databases and business functions (marketing, finance, HR, etc.). A new discipline that owes its existence to technological power has rende- red possible things that until now re- mained in the realm of the theoretical. The things that we talk about here are mainly associated with two challenges: data volume and data complexity. 2 For more information go to Tendances Trends.be 3 Although both the singular and the plural are often used to qualify data, Big Data is mostly referred to as a mass noun, i.e. a singular phenomenon of mass information. Consequently, Big Data will always be referred to as a mass noun in the singular in this document.

8. 6 See article on the New York Times blog: http://bits.blogs.nytimes.com/2013/02/01/the-origins- The objective of Big Data is to tap into exponentially increasing volumes of data that have become near impos- sible to process, using traditional database management and information management tools4 , and to handle complex data in a timely manner. According to the works and words of the 451 group and Gartner; the aim of Big Data is to achieve competitive advantage through data collection, analysis and use methods that, until now, could not be used due to the econo- mic, functional or technical constraints associated with the volumes, processing velocity and variety of data invol- ved. Disruption or evolution ? Big Data is sometimes presented as a disruptive phenomenon that challenges the very foundation of everything done in the past in terms of decision support or, on the contrary, simply as the next evolutionary step in business intelligence organizations and systems. Though this may seem like a mere semantics debate, it is not. In- deed, depending on their opinion on the matter, the scenarios put in place by companies will be very different. Big Data history Big Data’s meaningful history clearly highlights the field’s specific nature; if the term Big Data was first used by analyst firm Gartner in 2008, Big Data’s origins can however be traced much further back. In a way, concurrent with the rise of information technology, the concept-like all other innovations-simply took time to become widespread and to refine itself. Gil Press sets this new discipline’s first appearance in an even more dis- tant past5 (1944). But without going that far, and even if somewhat technical discussions on who actually coined the term “Big Data6 ” persist, we can however trace the earliest do- cumentation on the famous 3Vs (Vo- lume, Velocity and Variety) predicting exploding amounts of data and the creation of a new data processing back to the beginning of 2001, according to analysis firm Gartner. It should also be mentioned that Big Data is the culmination of the Data Mining approach, popular du- ring years 1995-2000, which itself was born out of the association of two relatively old schools of thought (trends), i.e. statistics and artificial intelligence. Form follows function Regarding the discipline’s origins debate, the excellent O’Reilly Big Data glossary could help reach a consen- sus as it focuses on the e-merchants and other collaborative web players who actually were part of the phenomenon rather than the analysts who simply described it. In other words, it would seem that, much like for O’Reilly’s famous Web 2.0 in 2004, the phenomenon’s des- Big Data, background and principles - p.08 FROM BIG DATA TO BIG BUSINESS - PAPER 1 4 Please view also complete definition in Wikipedia’s open encyclopedia which inspired ours: http://en.wikipedia.org/wiki/Big_data 5 http://www.forbes.com/sites/gilpress/2013/05/09/a-very-

9. criptive term appeared after the phenomenon itself, as is often the case in the digital sphere. Cloud Computing, for example, stem- med from major websites’ (Amazon, Google, e-Bay, Microsoft, etc.) excess hosting capacity, a result of the origi- nal architectures they had set up to support visitor traffic as well as meet hosting “elasticity” needs. Similarly, Big Data materialized from the over- dose of data generated by Internet users’ activities on sites like Amazon, Yahoo! and much later Google. The main players have not changed which led to unforeseen innovative marketing applications7 . A mechanism has been made available to influential companies. They can now invest in technologies in order to find ways to satisfy user needs in an extremely short period of time. The Internet bubble made experimenting in real-time and with unlimited resources possible…the ul- timate researcher’s dream! Logic has been overthrown: data co- mes before use, business initiative before research, function, in a sense, precedes form…and the uses created by these technological advances are numerous, as described in the chapter on uses (see Chapter 3: page 5). Before thinking about whether Big Data is a disruption or an evolution, let us present the new developments triggered by the phenomenon in the following 4 categories: data, uses, work methods and tools. The fact that Big Data helps take advantage of weak signals does not mean that weak signal detection is a new subject. Philippe Cahen, author of “Uncertainty Marketing”, explains how weak signals impact marketing and why they are so important. What is a weak signal? Philippe Cahen gives the following definition in his book “Everything there is to know about uncertainty marketing”: “A weak signal is a lateral thought- provoking piece of information […]. A weak signal is not a small fact that gives information about the future. It would be too easy, somewhat naïve, to think that information about the future would be so freely available. It is how you mentally approach it that helps you decipher the future. Intuition plays a major role in detecting and interpreting weak signals.” “The future is a leap into the unknown, an unknown that can be either reassuringly friendly or so unbearable that all we want to do is run away. Actually living life the way it was planned is rare. Usually, it is quite the opposite.” Big Data fuels the thinking process and actions associated with weak signals; it helps derive hypotheses that can then be tested by cross-referencing data and behaviors. Source: “Marketing Uncertain” by Philippe Cahen, Kawa publishing , 2012 7 Lise Gasnier on Solucom Insight Processing weak signals (P. Cahen) p.9 - Big Data, background and principles PAPER 1 - FROM BIG DATA TO BIG BUSINESS

10. Big Data projects Big Data projects typically have four components. First, it involves Big Data technologies, hardware and software. Second, it requires a specific methodology approachthatwillbebrieflymentioned in this document and further detailed in following publications. The third component is a legal one since a perfect command of the legal framework associated with handled data and intended uses is important. Last but not least, every Big Data project includes a social component that must be taken into considera- tion. What is the ability of our so- cieties and each group of people or individuals to accept the circulation and use of their personal data? To avoid exposing one’s project and the whole area of application to risks, it will be up to companies to self-re- gulate and to legislators to adapt to these new technology-driven contexts and possibilities. Big Data, background and principle- p.10 FROM BIG DATA TO BIG BUSINESS - PAPER 1

White paper "From Big Data to Big Busine$$"

White paper "From Big Data to Big Busine$$"

White paper "From Big Data to Big Busine$$"

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