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Solving Data Dilemmas to

Derive Business Growth

Big Data

Debate Topic: What is the future of the “Big Data” management

technique?

ourse: Management Idea Factory

!a"# $%&'

(uthors: Adriana Petre, Dimitra Skoulaki and Anastasia Zenetzidaki

ourse oor)inator: Dr* Stefan +eusin,vel)

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Content Table1.Introduction

2. Theory

2.1 Big Data Terminology

2.2 Emergence, Dissemination and Adoption of Big Data

2.3 Riss and Implications

 3. !ethodology

3.1 "!I analysis of the e#olution of the management idea

3.2 Te$tual analysis of a core te$t promoting the idea %Big Data a re#olution that &ill transform

ho& &e li#e &or and thin' !ayer (chon)erger and *uier, 2+13

3.2.1 (ummary of the Boo 

3.2.2 Record Analysis

3.2.3 *ontent Analysis

-. !ethodology II

-.1 Inter#ie& Analysis

-.2 imitations

-.3 Results of the /ualitati#e research' Inter#ie&s

0. *onclusion

'References'

Appendi$ 1 *riti/ues to&ards the BI DATA management idea'

Appendi$ 2 Inter#ie& Transcripts

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&*-ntro)uctionBig Data ha#e )een declared a ne& class of economic asset4, lie currency or gold %5orld

Economic 6orum, 2+12. A ne& &orld of information is emerging, due to the simple fact that

 people are using computers and cell phones. 7o&e#er, )ig data, generated )y e#eryday actions,

has led to a cultural, technological and scholarly phenomenon %Boyd 8 *ra&ford, 2+12.Big

Data Analytics intend to change the &orld as &e no& it, and therefore it undenia)ly constitutes

a ne& management idea that attracts our attention.

Big Data refers to the managing of no&ledge &ithin organi9ations &orld' &ide )y /uantifying

/ualitati#e data. It does so )y e$tracting, transforming, analy9ing, synthesi9ing and distri)uting

this tacit no&ledge into an e$plicit form )y disco#ering patterns in large amounts of 

unstructured information %TATA *onsulting, 2+13. Although many of today:s )usiness ;ournals

and pu)lications intensi#ely refer to the use of Big Data Analytics as a tool created for 

companies to ha#e a )etter understanding of customers, marets, ser#ices and operations, fe& of 

the practitioners actually no& the e$act meaning of the term, it<s pro#enience, use and

implications.

It has )een argued that Big Data is around e#er since the )eginning of technology, &hen

scientists used supercomputers4 to analy9e large amounts of data. 7o&e#er, in today:s )usiness

en#ironment, the term Big Data4 is used as a ne& management idea, that in contrast to pre#iousyears &hen technology &as not accessi)le to e#eryone, it is no& a#aila)le to all )usiness

intelligence user companies %!ayer' (chon)erger and *uier, 2+13.

This paper e$amines Big data as a management idea and aims at facilitating the understanding of 

no&ledge production and Big Data in the (er#ice Industry. 5e chose to focus on ser#ices due to

the need to demonstrate #alue through synthesis of high amounts of /ualitati#e data in today<s

a)undance of information a#aila)le on and off'line. Therefore, in this analysis &e &ill pro#ide

information a)out managing valuable  no&ledge in their organi9ations &hich &ill allo&employees to efficiently and effecti#ely implement Big Data.

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As a management idea, )ased on a /uic maret research, Big Data &ill ha#e a su)stantial

impact on the functioning of organi9ations. Therefore, the e#olution, creation, dissemination and

adoption of this idea &ill )e e$amined in this paper in order to assess its future impact on the

 )usiness en#ironment. (ince managers ha#e an emergent collecti#e preference for ne&

techni/ues4 %*lar and reat)atch, 2++- -++, introducing this ne& management idea4 seems

to produce interest from the client=company side.Therefore the topic &e intend to address

through this paper is

5hat is the future of Big Data Analytics as a management techni/ue4

2. Theory

2.1 Big Data Terminology

Big Data is a no#el research area and is still a #ague and a)stract concept in the scholarly &orld.

Big data has )een #ariously identified )y scholars %!ayer'(chon)erger 8 *uier, 2+13>

*haudhuri, 2+12, &hile the name of Big Data4 itself has caused dou)t and confusion.

Throughout this paper, &e generally define Big Data as datasets produced )y multiple sources, at

such a scale that cannot )e stored and processed )y usual dataset soft&are %*hen, !ao 8 iu,

2+1-, &hile Big Data are officially identified )y ID* %International Data *orporation in 2+11

as a ne& generation of technologies and architectures, designed to economically e$tract #alue

from #ery large #olumes of a &ide #ariety of data, )y ena)ling the high'#elocity capture,

disco#ery, and=or analysis.4 Both definitions underline the three characteristics of Big Data,

usually referred to as the 3?s model.

The a))re#iation of 3?s stand for the main three characteristics of Big Data #olume, #elocity

and #ariety. As analy9ed in the 7ar#ard Business Re#ie& %2+12, #olume o)#iously descri)es theinconcei#a)le mass of data produced e#ery day, and more precisely, as of 2+12, a)out 2.0

e$a)ytes of data are created each day4%p.@2. ?elocity refers to the need for timely and rapid data

collection in order to efficiently utili9e the #alue of Big Data, &hile #ariety represents the

multiplicity of produced data, )oth structured and unstructured %*hen, !ao 8 iu, 2+1-. Apart

from the 3?s model, !cinsey 8 *ompany primarily highlighted through their report%2+11 the

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#alue that through potentially efficient e$ploitation of Big Data, could )e created for 

organi9ations. In )rief, companies that use Big Data Analytics ha#e noted higher profita)ility,

impro#ed producti#ity, increased maret #alue and positi#e impact on customers. %!cinsey

uarterly, 2+11.

2.2 Emergence, Dissemination and Adoption of Big Data

Big Data Analytics is an inno#ati#e and #ague management idea &hile its proponents are still

trying to consolidate its management #alue, so the e$amination of ho& )ig data, as management

techni/ue, emerged appears to )e necessary in order to assess its future.

Big Data is clearly a management trend that e#ol#es in tandem &ith the underlying technology

%!cinsey uarterly, 2+11, and its emergence should )e discussed in relation to technological

achie#ements. Data e$isted since 1C+s, &hen the first data)ase machines4 appeared, and their 

organi9ational use has )een intensified in late 1CC+s, &hen the )enefits of a parallel data)ase

system )ecame officially recogni9ed. %*hen, !ao 8 iu, 2+1-. The ey point is the e#ol#ing

moment of Data into Big Data, and it too place after the de#elopment of a ne& generation of 

computing tools that could gather, manage and process massi#e data, leading to the gro&th of 

ne& inferential data techni/ues that created ne& conte$t for the organi9ations. %Bollier, 2+1+.

5hat remains to )e e$amined is the reason )ehind the esta)lishment of Big Data Analytics as a

fashion trend.

According to the maret model suggested )y A)rahamson %1CC@, 1CC1, &hich consists of the

circles of demand and supply, in the creation stage of management ideas the fashion setters sense

incipient preferences guiding fashion demand and create many management

techni/ues4%p.2@-, and guide the supply. A possi)le application of A)rahamson<s !aret model

on The Big Data case &ould possi)ly lead to the conclusion that the de#elopment Big Data

Analytics represents the response to the constantly gro&ing industry of dataset tools and

technology products. In other &ords, the incenti#es of the e$plosi#e gro&th of Big data Analytics

are possi)ly financial %ID*, 2+11, con#erging &ith ne& technologies and )usiness tools.

E$ploring the rationality of choosing to promote Big Data Analytics, ang 8 hana %2+12

suggested that multiple management concepts only emerge from the need of organi9ations to find

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techni/ues and tools to resol#e pro)lems they currently face. Therefore, managerial thining

comes in &a#es, much in the same &ay as aesthetic fashions do4 %p.F2, and that could e$plain

the popularity of Big data Analytics. This e$amined concept may has emerged due to the

organi9ational need for ne&, disrupti#e )usiness models to cope &ith the e$plosi#e production of 

data. %!cinsey uarterly, 2+11, responding to progressi#e normati#e e$pectations, meaning

that old techni/ues &ill al&ays )e replaced )y more inno#ati#e ideas.%*lar 8 reat)ach,

2++-.

Big Data Analytics ha#e not solely emerged, selected and promoted, )ut ha#e also )een

successfully diffused in the management &orld )y fashion setters4 %A)rahamson, 1CC@.

"ractitioners, academics, management gurus and consultants ha#e all contri)uted through

massi#e pu)lications that created a Big Data stream in mass'media. !ass'media ha#e )een

characteri9ed as the gateeeper of inno#ation and means of idea dissemination. %7irsch,1C2.

Regarding Big Data pu)lications ha#e )een increasing at an impressi#e pace since 2+11 %see

"!I analysis, &hile consultants groups ha#e pu)lished special issues on this phenomenon and

its #alue and implications %e.g !cinsey uarterly, 2+11> ID* I?IE5, 2+11.

Despite the increasing pu)lished literature on the Big Data concept, recepti#eness and adoption

of ideas is mostly reflected through managers< )eha#ior and reactions %ang and hana, 2+12.In

e#ery case t&o important /uestions need to )e in#estigated &hy and ho& management ideas are

adopted. Applying the maret model suggested )y A)rahamson %1CC@, the reason )ehind idea

adoption appears to )e the demand created mostly )y fashion setters.

Another reason of )road acceptance of Big Data could )e identified )y A)rahamson through a

sociological e$planation %originated )y (immel, 1C0, &hich underlines that managers of higher 

reputation organi9ations adopt fashiona)le management ideas in order to distinguish themsel#es

from lo&er reputation organi9ations. In case of Big Data Analytics, international highly'

recogni9ed companies, such as oogle, Ama9on and IB!, in#ested millions of dollars in Big

Data technology at a #ery early age and su)se/uently enhanced the adoption of the ne&

management trend %!cAfee and Bryn;olfsson, 2+12.

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6inally, in times of organi9ational change, no#el management ideas ha#e a larger corporate

impact %ieser, 2++, since they may offer #alua)le solutions that may facilitate the process of 

strategic or organi9ational change %ang 8 hana, 2+12. Big Data Analytics are e$pected to

create #alue for all industries, highlighting effecti#e decision'maing, )etter ris management

and impro#ed financial and product performance %!cinsey uarterly, 2+11, and all that in a

constantly changing, economically tur)ulent )usiness en#ironment. !oreo#er, the

aforementioned corporate )enefits ha#e )een fueled )y the decreased cost of data ac/uisition and

the de#elopment of the underlying technology %*haudhuri, 2+12.

2.3 Risks and Implications

The process of creating and disseminating management ideas follo&s the model of the maret

%A)rahamson, 1CC@ in &hich demand and supply are usually also affected )y e$ternal factors

and maret logic %ang 8 hana, 2+12. A negati#e aspect of the marets operation is that

organi9ations are not acti#e actors, and this may lead to the adoption of management ideas,

&hich are not appropriate for each company<s needs. This organi9ational danger is also depicted

in the case of Big Data concept, since the misconception of Big Data may lead to a #ariety of 

negati#e effects for companies.

Initially, the most threatening ris of Big Data is the threat of personal and societal pri#acy. Go&adays indi#iduals are identified through a set of data, &hich are a#aila)le for use to many

sources %*a#ouian and Honas, 2+12.7o&e#er, society and the legal system ha#e not yet

ad;usted to the Big Data era &hich means they are not ade/uately prepared for the impact of Big

data, such as transparency, correlation and aggregation4 %Da#is, 2+12. (u)se/uently, the lac 

of transparency in the &ays and the purposes of analy9ing and aggregating personal data, could

easily )e considered as #iolation of personal information. %)oler, 5elsh 8 *ru9, 2+12

The generated re&ards of Big data for organi9ations has )een highlighted )y multiple scholars,

sur#eys and management consultants, )ut in many cases they omit to highlight that the ade/uate

implementation and interpretation is crucial to achie#e positi#e results. Bollier %2+1+ underlined

that ra& data are not self'e$planatory, and the results recei#ed out of their analysis may also )e

 )iased, &hile Boyd and *ra&ford %2+12 pinpoint the danger of misleading data errors at a large

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scale. (u)se/uently, the adoption of effecti#e data analysis techni/ues is a critical factor of 

success of the management idea.

3. ethodology

3.1 !I analysis of the e"ol#tion of the management idea

“Big Data is not a new or isolated phenomenon, but one that is part of a long evolution of 

capturing and using data” (Best-Selling Author, Kenote Spea!er and "eading Business and 

 Data #$pert% Bernard &arr'

 

Before di#ing into the print media analysis of the )oo, &e &ill tae a loo at the "!I of Big

Data4 in pu)lications, presented as an organi9ational concept for It an IT' oriented pu)lic and

then for companies and )usiness' oriented people through time. 5e &ill try to assess its

e#olution and if the popularity of this idea in print &ill stic after its intense presentation to the

general pu)lic %7eusin#eld et al., 2+13. 7ere, &e refer to Big Data rather as a management

techni/ue than a management concept or idea e#en if there three terms all encapsulate the &ay

)ig data4 &as presented %7eusin#eld et al., 2+13 since it offers a tool that )usinesses can use

to reach a desired goal )ut only after its rigorous interpretation %uitney and Rainie, 2+12.

Interpretati#e #ia)ility4 is a characteristic of concepts that aim to )e successful in catching the

 pu)lic eye and ultimately gro& into mass adoption %Benders 8 #an ?een, 2++1. In the case of 

Big Data4, this characteristic has implications for organi9ations since, in its idiosyncratic nature,

a )usiness has uni/ue features and can therefore interpret the notion of data4 in a manner that

 )etter suits its needs. This is aligned &ith the conceptual am)iguity4 %ieser, 1CC of Big Data

that is designed to )e attracti#e and inherently fit the needs of the user &ithout offering clear 

steps that must )e follo&ed to effecti#ely mae use of the data for )etter decisions and

 performance %ieser, 1CC. This is also a critic to the )oo as &e &ill later demonstrate.

As form of data collection, for more empirical insight, &e used the most rele#ant academic

data)ases for reference ABI=Inform' "rouest, 5e) of (cience and the nline *ontents %*

%Benders et al., 2++@.

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6igure 1 summari9es the findings from these three academic data)ases from the year 1CFC until

 present %2+10. 5e decided to choose this time frame to demonstrate that the term and concept of 

)ig data4 &as mentioned in fe& articles )efore the technological )oom happened, ho&e#er 

these articles are not as rele#ant as the ones currently pu)lished that discuss solely the idea from

a managerial perspecti#e.

nine *ontents is focused on articles, &ith 32 pu)lications &ith the search terms )ig data

analytics4 &hich is used more recently for a more IT' oriented pu)lic %1 st article pu)lished in

2+11 and &ith 11F- pu)lications &ith the general term )ig data4 &hich &e focus on in our 

illustration %1st  article pu)lished in 1CFC. 5e) of (cience stores academic ;ournals, cross'

culturally and inter' disciplinary. 5e found 1C+1 pu)lications &ith the term )ig data4, dating

from 1CC3 until present &hile &ith the terms )ig data analytics4 &e found 1+F pu)lications

starting also in 2+11. ABI Inform lo)al, focused mostly on English language press4 %Benders

et al., 2++ found 1F- articles dating from 1C2 a)out )ig data4, although the first article &ith

more rele#ance, taling a)out the concept is ploring the &orld of parametric analysis4,

 pu)lished in 1CC+ and presents a program plore4 &hich maes use of )ig data4.

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As 6ig.1 sho&s, &e are no& at the pea= surge of Big Data pu)lications, since the conte$t of 

application, e#en if #arying )et&een sectors, is )uilt on the same fundamental idea of data

gathering and interpretation %7eusin#eld and Benders, 2+++. Also, as &e &ill sho& in the ne$t

section, the popularity of )ig data4 also coincides &ith the management fashion discourse

 presented )y the )ig data4 )oo. 5e therefore strongly )elie#e that in our case, the conte$t is

changing the content and also,)ig data4 no& is more a)out the presentation and selling of the

idea &here the content is o)#iously follo&ing the pu)lic thirst of constant inno#ation and

adoption of any techni/ue capa)le to upsurge potential gains.

After Big Data technologies are adopted and

implemented )y companies as core resource

units %lie R8D it is e$pected that the idea

&ill fade a&ay since it found it<s gro&th as a

conse/uence of the technological )oom and

data storage ad#ancements. In return, &e can

notice that the pu)lications and promotion of 

Big Data follo& the trend of the lo)al

Information (torage *apacity4 &here mega

computers are no& a)le to store and

transform unimagina)le amounts of data into codified information %7il)erst and ope9, 2+11.

5e can identify the trend of )ig data4 in print media )y looing also at the non' academic

articles and pu)lications in ne&spapers and maga9ines or at the )u99 created )y consultancy

firms to spar interest in the clients and create demand for their ser#ice %Berglund 8 5err, 2+++.

!cinsey8 *ompany pu)lished F@- articles a)out #arious topics related to the importance of 

Big Data starting in 2+11 &ith Big data The ne$t frontier for inno#ation, competition, and

 producti#ity4. In a similar fashion, Bain8 *ompany, The B* and "! are constantly

 pu)lishing articles for their current and future clients.

Big Data4, in its comple$ity and a)stract definitions, is a clear e$ample of a management

fashion )uilt )y companies to create clients and a sense of urgency for change in the maret. It

seems lie no)ody no&s e$actly ho& to use this concept, and the only difference it made &as to

mo)ili9e people to in#est more capital into ad#anced technologies, e$pert consultants and ne&

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human resources that might no& ho& to interpret the data. Appendi$ 1 pro#ides sources that

critici9e the Big Data4 idea.

3.2 Te$t#al analysis of a core te$t promoting the idea %Big Data a re"ol#tion

that &ill transform ho& &e li"e &ork and think ' (y ayer')chon(erger and

*#kier, 2+13

“Big Data refers to our burgeoning abilit to crunch vast collections of information, anale it 

instantl, and draw sometimes profoundl surprising conclusions from it” (&aer Schonberger 

and )u!ier, *+, .reface'

In order to tae the analysis of the Big Data4 management

techni/ue e#en further and understand its recent popularity and

responsi#eness from )oth &riters and pu)lic, &e must turn our 

attention to&ards the important te$ts that contri)uted to these

factors. As proposed )y Benders, Gi;holt and 7eusin#eld

%2++@, &e &ill perform a record and content analysis4 on the

most influential )oo in the dissemination of the )ig data4

concept Big Data A re#olution that &ill transform ho& &e li#e

&or and thin4 %!ayer (chon)erger and *uier, 2+13. It can

 )e noticed also from the "!I analysis that after the pu)lishing

of this material, the interest in )ig data surged in print media,

&ith a pea in 2+1-.

3.2.1 )#mmary of the (ook 

Big Data, A re#olution4, )y !ayer'(chon)erger and *uier, is a Ge& Jor Times )est seller 

and constitutes a timely introduction and o#erall re#ie& of the Big Data Analytics phenomenon.

The authors introduce the concept in the )eginning of the )oo, )y ela)orating on )asic

definitions and ideas and shortly report the e#olution and change of Big Data until today. In the

follo&ing chapters the authors amplify the main characteristics of Big Data to pro#ide the

audience a more complete sense of the topic.

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The )oo starts )y underlining the messiness and imprecision of unstructured data and ho& it is

conduci#e to a )roader picture. Ge$t, !ayer'(chon)erger and *uier clarify the terms of data

*orrelations and *ausality and pinpoint ho& Big Data offer insights for &hat rather than &hy.

The follo&ing chapter sees to e$plicate Datafication, &hich is optimi9ed through e#eryday

e$amples and applications. ?alue of Big Data and Implications for organi9ations are analy9ed in

t&o e$tensi#e chapters, &hich compose the main idea of the )oo.

5hile implications for the future are presented from a positi#e perspecti#e, the ne$t t&o chapters

e$plore the dar side of the concept through an e$tensi#e analysis of potential future riss and

ho& they could )e controlled. 6inally, a discussion around the future and endless possi)ilities of 

Big Data pro#oes the audience<s thoughts regarding the changes and the impact this fascinating

 phenomenon may produces in e#ery facet of e#eryday life.

3.2.2 Record Analysis

6irst, in our case, )y analy9ing the target audience of the )oo, &e can )etter comprehend

&here the organi9ational concept Kof Big DataL may ha#e had significant differences in impact4

%Benders et al., 2++@ F20. (ince the )oo is highly approacha)le and easy to read, &e can

conclude that the target audience is the general pu)lic, more specifically, all )usiness and IT'

enthusiasts &ith a thirst for understanding ho& the future of organi9ational functioning &ill loo 

lie. This )roadens the scope of the )oo and allo&s for the creation of mass' a&areness and

responsi#e )eha#ior therefore impacting the &hole )usiness &orld differently %due to

indi#idualities of each organi9ations and reader as mentioned pre#iously %*lar 8 reat)atch,

2++-. This mass'appeal can e$emplify the reasons for the e$pansion of pu)lications a)out Big

Data4 after the )oo &as presented %see 6ig, 1.

(econd, &e can loo at the presence of the editorial )oard %Benders et al., 2++@. 7ere, the )oo 

is edited )y Eamon Dolan and .G. *uier, one of the co'authors, &ith an e$tensi#e presentation

on )oth authors %)i)liographical notes of the )oo plus the contri)ution of a large num)er of 

 people. 6or a 2++ pages )oo, &e &ill adopt a critical perspecti#e and say that the content, as &e

&ill e$plain in the ne$t section, does not match the e$pectation )uilt )y of an accumulation of so

many ideas and support. Also, the )oo is still poorly edited &ith many repetiti#e instances on

the )enefits or use of )ig data4 &ith lac of su)stance, criti/ue and in' depth analysis. It seems

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that the )oo is presented more as a managerial discourse4 to create hype, )roadcast and

#alidate the idea as a fashion and attract the pu)lic )ut also promote the &riters %7eusin#eld

8Benders, 2+++ 2-+. In terms of e$tensi#e referencing4, &e can loo at the )oo as an

academic material %ho&e#er, the references are not present in' te$t )ut only at the end of the

 )oo, e#en if the feeling of reading the )oo leads us to&ards an impression of practitioner'

oriented ;ournal4 %Benders et al., 2++ F20

5e find oursel#es still in a period of uncritical euphoria4 &here the Big Data4 fashion seems

as a rationale, progressi#e cure' all4 concept a)le to lead progress4 in organi9ations

%7eusin#eld 8Benders, 2+++ 2-1. 7ence, the presence of a &e)site that endorses the &riters

and the )oo e#er since its pu)lication in 2+13 until present times and, through the &e)site, gi#es

the audience %in &hich a sense of urgency is implanted the possi)ility to )oo4 the &riters for 

eynote conferences and to spea at e$ecuti#e retreats across the glo)e e#ery year4 %Big Data

Boo' 5e)site. 5e )elie#e that the concept of Big Data4 &as a )ig4 opportunity grasped )y

the authors in its early stages and paced in an appealing &ay.

3.2.3 *ontent Analysis

5e &ill no& turn to the print' media analysis of the )oo content &here &e measure thereada)ility of the te$t, the Mdifficulty in implementation< or concept and Mfear' inducing< in the

 pu)lic %*arson et al., 2+++.

(chon)erger and *uier use a simple language throughout the )oo to descri)e illustrious cases

of large companies that )enefited from the use of Big Data4 and fe& start' up companies that

used )ig data for success in their )usiness %i.e.*lear 6orest, p.3> Decide. *om, p.123> Hana, p.

C1. 6or e$ample, they start )y e$plaining the &ell' no&n e$ample of oogle 6lu4 and

continue to discuss e$amples such as Nynga, 5almart or Target. They loo at ho& thesecompanies used correlation and data analysis from either the tracing of regional searches on

different su);ects or customer )eha#ior in order to forecast demand or modify and customi9e

 platforms. 7o&e#er, this is not in any circumstance ne& content for the pu)lic since these are

#ery famous e$amples largely de)ated )y the media. 5e )elie#e that, )y reducing the num)er of 

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repetitions a)out companies and )enefits of )ig data4, the )oo could )e easily summari9ed in a

fe& chapters.

(econd, the authors use strange and #ery )road statements or metaphors and definitions &hich

confuse the reader. 6or a )oo that promotes a )usiness and IT concept, &e e$pected more

technical terms and practical information on the &ay Big Data4 can )e effecti#ely analy9ed and

used in the future. 5e do not )elie#e that for scientists and data e$perts, information simply

speas for itself &ithout any strong theoretical or practical )acground. 6or e$ample, the

follo&ing statements seem )road and tri#ial for the purpose of the )oo

“/he data can reveal secrets to those with the humilit, willingness, and the tools to listen0 (p1 2'

“03ne wa to thin! about the issue toda - and the wa we do in the boo! - is this% big data

refers to things -one can do at a large scale that cannot be done at a smaller one, to e$tract new

insights or create new forms of value, in was that change mar!ets, organiations, the

relationship between citiens and governments 41115” (p1 6'

“3ne of the most basic pieces of information in the world is, well, the world1 475 8e need a

method to measure ever s9uare inch of area on #arth” (p1:;'

“<acts come in one end of the digital assembl line and processed information comes out at the

other end-data this is starting to loo! li!e a new resource or factor of production0 (p1+'

Third, the manner in &hich &ay the concept is presented in the )oo appears easy to grasp and

implement in organi9ations %*lar 8 reat)atch, 2++-. By simply gathering all the possi)le

data, a )usiness can adopt an nO all4 approach and mo#e from causation %deducti#e, tested

hypothesis )ased approach, &hich is s more costly and slo&er process, to computer' generated

correlations %inducti#e approach that determines meaningful patterns in data %(chon)erger and

*uier, 2+13 1F. 7o&e#er, the )oo had to ela)orate on the implications of the concept since

this process has also costly implications for companies as they need to implement ne& )usiness

units &ith IT e$perts and also specialists in different su);ect areas %customer )eha#ior>

mareting> logistics etc. that can interpret the findings. 8ithout interpretation what is data good 

 for=

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To conclude, Big Data )ecame a ne& management techni/ue as it &as paced and sold )y

consultants or )usiness ad#isors through the use of print media or other means of promotion )y

management gurus4 %conference or direct ad#isory as in our case %*lar 8 reat)atch, 2++-.

5e e$pect a decline in the popularity of this management techni/ue follo&ing its current

attention as after it &ill )e adopted in organi9ations, the term )ig data4 &ill most liely )e

repaced under a different terminology related more to the analysis part of the data and not so

much its )ig4 #olume.

-. ethodology II

-.1 Inter"ie& Analysis

In the second part of our analysis &e &ant to e$amine ho& Big Data Analytics are actually

implemented, diffused and operated &ithin organi9ations. 5e therefore selected critically to

mae an assessment in t&o different firms.Jin %2++3 and (tae %1CC0, 2+++ emphasi9e the

importance of esta)lishing a specific theoretical frame&or that structures data collection. ur

frame&or, since our /ualitati#e research &as limited in 3 inter#ie&s &as our literature and )oo 

analysis that helped us a lot to )uild a semi'structured inter#ie& and try to dig in depth in the

&ay that Big Data Analytics are implemented &ithin the companies. In addition, &e )ased ourstructure of the inter#ie&s on our Research uestion that &as mentioned a)o#e in our effort to

ac/uire /uality responses that could after&ards )e re#ie&ed and lead to a conclusion. !oreo#er,

&e used as conte$t the cycle of no&ledge creation %A)rahamson, 1CC@> &e &anted to e$amine

ho& Big Data Analytics &ere introduced and )y &hom in these t&o companies and then to assess

the processes of codification,dissemination and implementation &ithin the t&o firms.

5e chose in purpose t&o totally dissimilar firms in order to conduct our research> &e tried to

understand )y e$amining t&o different organi9ations that use Big Data Analytics for different

 purpose and in different &ays to ac/uire a more complete and )road #ie& of the implementation

of this ne& management idea and all the implications that may follo& in each case. !ore

concretely, &e inter#ie&ed the manager and o&ner of an inno#ati#e consulting firm and then &e

inter#ie&ed a Business Analyst and a user= employee of easing company. In the first case, &e

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sa& that Big Data is the ultimate tool of deli#ering #alue to the customers and the primary

 )usiness intelligence tool through &hich the company targets at competiti#e ad#antage. In the

second case, &e sa& that Big Data Analytics is a solution that the company used in order to

impro#e its performance and in order to re#eal ne& insights and opportunities of de#elopment.

(o, in one case &e ha#e Big Data Analytics as an end and in second case as the means to an end.

In order to )e a)le to get inside and mae sense of the processes descri)ed &e created semi'

structured inter#ie&s &hich helped us de#elop understanding of the &ay that managers mae

sense of, and create meanings a)out this ne& management idea and its implementation in their

 ;o) en#ironment. In that &ay &e tried to interpret their )usiness &orld and )e critical to&ards

their statements %(ch&art9mann,1CC3. Through the methodology that &e adopted for our

/ualitati#e research, &e tried to )e fle$i)le, accessi)le, intelligi)le and more importantly capa)le

of disclosing significant and commonly hidden aspects of human and organi9ational )eha#ior.

%#ale and Brinmann, 2++C

5e follo& a com)ination of approach )ased on Ryen %2++210 &ho states that 4After one has

inducti#ely identified a theme, one goes on to try #erifying or confirming the finding %deducti#e,

&hich again gi#es an inducti#e loop.It is legitimate and useful to )oth start &ith conceptual

analytical categories, that is deducti#e, and to gradually de#elop them, that is inducti#e4

After ha#ing conducted the inter#ie&s, &e follo&ed a second round of assessment )ased on

hidden aspects of the &ording and e$pression used. 5e did not use the data collected as ra&

material )ut &e tried to re'interpret it )ased on the general attitude of the inter#ie&ees and the

comple$ities that emerged related to the implementation of the idea.% Gicolai, A., and Daut&i9, H.,

2+1+ 6urthermore an additional aim &as to comprehend the reasons of adoption and the fitting

&ith the company. In the second case, &e too t&o inter#ie&s in order to o)tain a holistic #ie&

and capture the different perspecti#es of the manager and the employee=user to&ards the idea.

6inally, &e also emphasi9ed on the attitude of managers as clients %5ilhem and Bort, 2+13> and&e tried to understand &hich stance they tae and ho& #ulnera)le or critical can )e to&ards the

ideas %eleman, 2+++

-.2 imitations

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Through our research &e e$perienced a couple of limitations that &e consider significant to

mention )efore the analysis of our data..Ii the first case that &e inter#ie&ed the manager of the

consulting company our data collected is one'sided comparing &ith the second case that &e had

the opportunity to capture a more complete and o);ecti#e attitude to&ards the idea. Therefore,

&e tried at a degree to )e more critical and interpret the data in order to encounter all the possi)le

aspects that may )e sipped such as implications and constraints of the idea. !oreo#er as &e are

the only persons tried to mae sense of the material collected &e lac of a )roader interpretation

and therefore of di#erse perspecti#es.

-.3 Res#lts of the /#alitati"e research' Inter"ie&s

!o#ing for&ard to the outcomes of our /ualitati#e research, &e came across se#eral noticea)le

aspects, findings and implications that &e &ant to present in this paper. 6irstly, &e are going to

display the findings, case )y case, gi#ing sufficient a)stracts to mae the paper more

comprehensi#e and then &e are going to compare &hat &e found and try to dra& some

conclusions.

In the first case, &here)y Big Data analytics are used as the primary tool for the consulting firm

to deli#er its ser#ices &e e$perience from our inter#ie&ee a totally engaged attitude. 7e &asreally passionate and enthusiastic a)out the idea and that &as e$pressed )y almost e#ery single

response in our discussion.

 >t is firstl the technolog that we have develop to manage !nowledge, there is no other 

 platform li!e the one that we have1 8e combine ver smart algorithms with a Big Data in a

conte$t that serves ever field of consultanc1 /his combination ma!es us competitive1”

-manager of )onsulting firm1

As it gets clear )y this e$ample, the adaptor of the idea is e$tremely de#oted to it and he

considers it to )e the core of their competiti#e ad#antage. 5hen &e attempted to e$amine in

more depth, &e ased a)out the &ay that their team decided to implement in this &ay and )uild

their &hole #alue proposition )ased on this idea

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  ?ow have ou decided to adapt and implement Big Data Analsis and innovate through this

concept= Did someone or something impact ou on this decision@step=”

“8ell, at a point we had foreseen the upcoming transformation in consulting industr and falling 

hourl rates, so we were see!ing for a wa that would change the traditional business model1

/his immediac in the business would onl obtained b the limitless power that Big Data

 Analtics can offer ou1 As consultants alread he found ourselves a lot of times before in ccles

that this management idea was discussed and presented and we believed in it and here we are

now”- manager of consulting firm

The inter#ie&ee e$plicitly said that the decision came through the consulting en#ironment and

from their passion to inno#ate someho& in their field. The manager also mentioned that there

&as no special need for dissemination of the management idea, as the company is )asically a

startup &ith 1+ employees. In the process of implementation though, he mentioned that

significant support from IT specialist &as crucial to )uild and de#elop the solution that it &ill

e#entually mae them competiti#e.

 8e believed in this idea because we had and we still have as main purpose of what we do, to be

different and to innovate in what we do and how we do it1 /o develop our Big Data we needed a

lot of time because we refer now to platform of around 21+++ inputs1 So, the support of the >/ 

and data scientist was undoubted if we wished to create something special 4

In that part of the discussion &e felt that the effort and the time that they consumed in order to

 )uild the )usiness intelligence tool &as considera)ly high. The inter#ie&ee mentioned that the

idea &as perfect and fitted the re/uirements e$actly. 7o&e#er )ased on the general comments &e

concluded that the idea &as in need of a lot of moulding and formation in order to fit the

o);ecti#es of a consulting company and not to mention the sun cost that &as relati#ely high.

Therefore e#en if the statements of the manager are different &e argue that in this case the

approach &as rather strategi9ing4 %N)araci, !, 1CCF as the highlighted at first the

opportunities that they could ha#e from the implementation of this idea in that conte$t and then

they did recogni9a)le effort to fit it for their o&n interest.

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In the second case, &e managed to ac/uire a more complete o#er#ie& of the implementation of 

Big Data and the &hole process and engagement &ithin the company. In the first inter#ie& that

&e had &ith the manager=Business Analyst &ho is particularly administrati#e of the Big Data

Analysis tool> &e had a comprehensi#e understanding a)out the capa)ilities and the

functionalities of the idea due to the enthusiastic description that &e got. The manager is engaged

&ith the idea and he a)solutely could )e called a supporter4 of it.

 Big data aids at management decisions because it increases analtics and ou get a better and 

bigger picture of what is going on in our business1 Also, what customers are doing is coming 

 from big data, more analtics is happening in all companies and the benefits are numerous1 8e

even tr to import new K.>s with the amount of available data1”

In our effort to comprehend the actual reasons for adopting this management idea, &e sa& that

the Admin of the Big Data Analysis tool &as clearly influenced )y the consultants &ho

introduced the tool to the company as he stated too actually. /he came at the organiation and 

before we made an decisions on the program that we will use, the were here and the showed 

us everthing 4. At that point our inter#ie&ee mentioned also ho& these consultants created

dou)tful feelings a)out the system that until then the company &ere using. They sell the idea

&ith the most )rilliant &ay> )y presenting the insufficiency of the current tool that they &ere

using &hich ,as &e disco#ered later, has more or less the same functionalities. %(turdy 2++- But

the sense of creating insecurity and #agueness to the managers of the company made it much

more easier for them then to sell the idea to #ulnera)le managers %eleman, 2+++> ieser. 2++2

Another argument that supports the reasons )ehind the adoption of the idea as the manager 

declared &as the use of successful case studies and implementation of the system, &hich &ere

not missing from the portfolio of the consultants. That cases had determinant impact in the final

decision.

Since others the can do this successfull, we thought that we could this too, but in the end was

not that simple”- Business Analst 

ur inter#ie&ee slightly referred to some ind of lac of significant resources, more from IT side

and some miscommunication that caused a lot of issues that had to )e sol#ed then.

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!ore clear statements a)out the comple$ities of the implementation of Big Data Analytics &e

got though for the finance employee and current user of the system. (he mentioned that she &as

enthusiastic a)out the functionalities of the system that the technical issues constantly emerging

and the lac of no&ledge of the rest of the company had created an unengaged attitude to&ards

the system &hich &as close to )e disappro#ed )y the other departments.

 #verone who is part of the implementation li!e the <.CA team , the all !now that is a great 

tool but the rest of the regions that the are not involved the cannot see that because the have

a lot of issues li!e the sstem is crushing or the are not getting the right numbers , so with the

lac! of !nowledge that the have in this specific tool the cannot trust it and understand its

capabilities1 “ -emploee@user of Big data Analtics

A nota)le point of this inter#ie& is that e#en though the employee stated clearly that there &as

lac of ade/uate no&ledge and that the rest of the regions could not understand )ecause they

&ere not in#ol#ed in the implementation process> &hen &e as her if she &ould consider import

to engage them &ith a more acti#e &ay, she seemed to hesitate and )e unsure a)out it. (he did

mentioned that she &ould change anything )ecause of the resistance to change that it is

 predominant &ithin the employees and of the incapa)ility to pre#ie& the )enefits..

“&ost of the times if ou as! our people, our emploees if the want to change something most 

of the times the will be resistant to change, the do not li!e change even if the have gains from

it1 So > thin! is better to begin with a proect team not of course onl managers “

That &as really une$pected as an ans&er, so &hen &e try to understand &hat she &ould do if she

had to )e in charge of the implementation, she taled a)out training sessions and sharing of 

no&ledge. (he considered coaching as the primary ey of a successful adoption and

implementation of the idea.

ast point that &e &ould lie to heighten, is the emphasis that &as gi#en in the leadership team>ho& crucial and decisi#e for efficient and successful stories is to )e dri#en )y a natural leader 

and a team of passionate inno#ators that can inspire the rest of the team and engage the &hole

company to reach a colla)orati#e outcome.

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'* onclusion

5e e$amined the management idea of Big data through a lot of perspecti#es and al&ays &ith a

critical eye in order to address the research /uestion. The correlation &ith theory, and the

e$amination of Big data emergence and dissemination enhanced our theoretical understanding of 

the concept. The "!I analysis assisted the comprehension of the reasons for the idea<s

 popularity, &hile the inter#ie& analysis contri)uted in the understanding of practical

implementation. 7o&e#er, )uilding on this aggregated no&ledge for this inno#ati#e

management idea, &e feel optimistic for the future of Big Data and its #alue.

The theoretical frame&or of Big data Analytics is still ne& and there is space for e$tensi#e

future research on almost e#ery aspect of it from the emergence of the idea and the cause of its

 popularity to the de#elopment of specific tools and techni/ues and its e$pected #alue for

organi9ations. 6urther research, and especially on the prere/uisites of efficient application of

this concept, &ill promote the no&ledge around the positi#e impact of Big Data on companies

and &ill hopefully esta)lish it as an official and #alua)le management tool. !oreo#er, its

#alidation &ould increase if more scholars engage in related research, rather than consultant

firms and management gurus.

n the other hand, the dar side of Big Data, and the possi)le riss stemming from its

implication should )e taen into consideration. "rotecti#e mechanisms should )e created either

 )y official )odies, such as go#ernments, or )y the companies themsel#es in order to achie#e

organi9ational, indi#idual and societal data pri#acy. In that &ay, concerns around the ne&

concept &ill diminish and the possi)ilities of recogni9ing Big data Analytics officially as a tool

&ith countless applications and significant generated #alue for organi9ations &ill increase.

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(ppen)i/ &0 ritiques towar)s the B-G D(T( management i)ea0

Ernest Da#is, Department of *omputer (cience, *ourant Institute of !athematical (ciences

Web sites

● Data ustice● Data ! Society Institute● Mat"babe #log $at"y %&'eil(

)eneral $riti*ues

● AAP%+ American Association -or Public %.inion +esearc"/

AAP%+ +e.ort on #ig Data, Feb( 01, 1203( Summary o-

recommendations by $at"y %&'eil, mat"babe blog, Feb( 04, 1203● Matt Asay )artner on #ig Data: 56eryone&s Doing It, 'o %ne

7no8s W"y readwrite.com Se.tember 04, 1209(

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● dana" boyd and 7ate $ra8-ord, $ritical uestions -or #ig Data:

Pro6ocations -or a $ultural, ;ec"nological, and Sc"olarly P"enomenon,

Information, Communication, and Society 03:3, 1201, <<1=<>?(● 7ate $ra8-ord, ;"e @idden #iases in #ig Data, @ar6ard #usiness

+e6ie8 #log, A.ril 0, 1209(

● 7aiser Fung, ;o8ard a more use-ul denition o- #ig Data, undated(

● ;im @ar-ord, #ig data: Are We Making a #ig MistakeB Financial

Times, Marc" 14, 120C(● o"n @organ, So Far, #ig Data is Small Potatoes , Scientic

American blog, une ?, 120C(● Matt"e8 ones, Data ! @ubris, guest blog, $olumbia Data

Science $lass, 'o6ember 1<, 1201(● )ary anger )ro8ing Doubts about #ig Data, A#$ 'e8s, blog(

A.ril 4, 120C(

● )ary Marcus, Steamrolled by #ig Data The New Yorker (online),A.ril 9, 1209(

● )ary Marcus and 5rnest Da6is, 5ig"t 'o, 'ineE/ Problems 8it"

#ig Data %.=5d, New York Times, A.ril >, 120C(● $at"y %&'eil, ;"e #ursting o- t"e #ig Data #ubble, mat"babe

blog, Se.tember 12, 1209(● $at"y %&'eil #ig Data is t"e 'e8 P"renology mat"babe blog,

February 1<, 1203(● $at"y %&'eil Four .olitical cam.s in t"e big data 8orld, 

mat"babe blog, A.ril 11, 1203

● S(P(, Se.arating t8eet -rom c"a, The Economist  A.ril 0, 120C(● Megan Scudellari, Scientists uestion t"e #ig Price ;ags o- #ig

Data, Newsweek, uly 1C, 120C(

Social and legal criti*ues

● Da6id Auerbac", Gou are 8"at you click: %n microtargeting, The

Nation Marc" C, 1209(● Solon #arocas and Andre8 Selbst, #ig Data&s Dis.arate Im.act

Social Science esearch Network , %ctober 0?( 120C(● ;( #lanke, )( )reen8ay, ( Pybus and M( $otH, Mining Mobile

 Gout" $ultures, 1nd I555 International $on-erence on #ig Data,Was"ington,120C(

● Data and Society +esearc" Institute, Data ! $i6il +ig"ts: W"y

#ig Data is a $i6il +ig"ts Issue, $on-erence, %ctober 92, 120C(● +ose @ackman, Is online sur6eillance o- black teenagers t"e ne8

sto.=and=-riskB

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● Gian ( Mui, ittle=kno8n rms tracking data used in credit scores

!ashin"ton #ost, uly 0<, 1200(● 'at"an 'e8man, )oogle, 5bay, Amazon, and Ga"ooE ;eam J. to

)ut $onsumer and Pri6acy a8s, Data ustice block, A.ril 1>, 1203(● Fokke %bbema et al( $"ina +ates its o8n $itizens, Including

%nline #e"a6ior die Kolkskraant, A.ril 13, 1203(● $at"y %&'eil, ;"e Dark Matter o- #ig Data, mat"babe blog, une

13, 120C(● $at"y %&'eil, ;"e Police State is already "ere, mat"babe blog,

A.ril 1>, 1203,● Frank Pas*uale, ;"e Dark Market -or Personal Data New York

Times, %ctober 0>, 120C(● Don Peck, ;"ey&re Watc"ing Gou At Work(  $tlantic %onthly

'o6ember 12, 1209(● Matt Petronzio, @o8 %ne Woman @id @er Pregnancy -rom #ig

Data, Includes a 6ideo o- t"e ;"eorizing #ig Data .anel at ;"eorizingt"e Web, 120C(

● Da6id +obinson, @arlan Gu, and Aaron +ieke, +obinson ! Gu $i6il

+ig"ts, #ig Data, and our Algorit"mic Future( +e.ort, Se.tember, 120C(● +oom -or Debate, Is #ig Data S.reading Ine*ualityB 'G ;imes,

August <, 120C(● 'atas"a Singer, 'e6er Forgetting a Face, 'e8 Gork ;imes, May

0>, 122C(● atanya S8eeney Discrimination in %nline Ad Deli6ery, A$M

ueue, 00:9 02=14, 1209(

● Matt Stroud, ;"e minority re.ort: $"icago&s ne8 .olice com.uter.redicts crimes, but is it racistB The &er"e Feb( 0?, 120C(

● 'ic"olas ;erry, #ig Data ProLies and @ealt" Pri6acy

5Lce.tionalism (● Zeyne. ;u-ekci, 5ngineering t"e .ublic: #ig data, sur6eillance,

and com.utational .olitics First %onday  Kol( 0? 'o( >, uly >, 120C(● Zeyne. ;u-ekci and #rayden 7ing, We can&t trust Jber, New York

Times, December 4, 120C(● anet Kertesi, My eL.eriment o.ting out o- #ig Data made me

look like a criminal, Time %a"a'ine, May 0, 120C(

Social Media

● +a6i6 $o"en and Derek +ut"s, $lassi-ying Political %rientation on

 ;8itter: It&s not 5asyE Se6ent" International AAAI $on-erence on

Weblogs and Social Media, 1209(

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● Daniel )ayo=A6ello, I !anted to #redict Elections with Twitter

and all I "ot was this ousy #a*er+ A #alanced Sur6ey on 5lection

Prediction using ;8itter Data Jn.ublis"ed ari6 .a.er(● Daniel )ayo=A6ello, 'o, Gou $annot Predict 5lections 8it" ;8itter,

Internet Com*utin", IEEE 6ol 0< no( < 1201/: ?0=?C(

● anger Associates, Social Media and Public %.inion #rieng.a.er(

● Derek +ut"s and Nrgen P-eer, Social media -or large studies o-

be"a6ior, Science, Kol( 9C< 'o( <109, ..( 02<9=02<C, 'o6ember 120C(● )rant Sc"oenebeck, Potential 'et8orks, $ontagious

$ommunities, and Jnderstanding Social 'et8ork Structure, 1209(● Zeyne. ;u-ekci, #ig uestions -or Social Media #ig Data:

+e.resentati6eness, Kalidity, and %t"er Met"odological Pit-alls,

#roceedin"s of the International $$$I Conference on !elo"s and

Social %edia 120C, to a..ear(

#reaking Pri6acy

● ( S8eeney, A( Abu, ( Winn, Identi-ying Partici.ants in t"e

Personal )enome ProOect by 'ame SSN 1209(● A( 'arayanan and K( S"matiko6( +obust De=anonymization o-

arge S.arse Datasets Security and #ri-acy, 1224(● M( 7osinski, D( Still8ell, and ;( )rae.el, Pri6ate traits and

attributes are .redictable -rom digital records o- "uman be"a6ior( #N$S

1209(

5ducation

● $arol #urris, Princi.al unco6ers a8ed data in "er state&s oQcial

education re.orts !ashin"ton #ost, 'o6( 11, 120C● $at"y %&'eil, Kalue=added model doesn&t nd bad teac"ers,

causes administrators to c"eat mat"babe blog, Marc" 90, 1209(

@iring

● $at"y %&'eil, Work.lace Personality ;ests: a $ynical Kie8 ,

mat"babe blog, A.ril 0<, 1203(

● AleL +osenblat, ;amara 7leese, and dana" boyd, 'et8orked5m.loyment Discrimination Data ! Society Working .a.er, %ctober

120C(

Science and #ig Data

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● ames Fag"mous and Ki.in 7umar, A #ig Data )uide to

Jnderstanding $limate $"ange: ;"e $ase -or ;"eory=)uided Data

Science i" /ata, Se.tember 120C(

)oogle Flu ;rends

● Da6id Auerbac", ;"e Mystery o- t"e 5L.loding ;ongue: @o8

+eliable is )oogle Flu ;rendsB Marc" 0?, 120C(● Declan #utler, W"en )oogle got u 8rong: JS outbreak -oLes a

leading 8eb=based met"od -or tracking seasonal u(, Nature, 

C?C:>C9<, February 09, 1209(● 7aiser Fung, )oogle Flu ;rends Failure S"o8s )ood Data R #ig

Data, @ar6ard #usiness +e6ie8 #log, Marc" 13, 120C(● $"ris )onsal6es, )oogle u trends and t"e -uture o- #ig Data 

$+', Marc" 90, 120C(● Da6id azer, +yan 7ennedy, )ary 7ing, Alessandro Kes.ignani,

 ;"e Parable o- )oogle Flu: ;ra.s in #ig Data Analysis, Science, 9C9,

Marc" 0C, 120C(● o"n 'aug"ton )oogle and t"e Flu: @o8 #ig Data Will @el. Js

Make )igantic Mistakes The 0uardian A.ril 3, 120C(

$itation counts and Im.act Factors

● #ruce Alberts, Im.act Factor Distortions , Science, 9C2 .( >4>,

May 0>, 1209(● ior Pac"ter, ;o some a citation is 8ort" 9 .er year #its o-

D'A blog, %ctober 90, 120C(● San Francisco Declaration on +esearc" Assessent● Per % Seglen, W"y t"e im.act -actor o- Oournals s"ould not be

used -or e6aluating researc"( , #M: #ritis" Medical ournal 90C, no(

>2>? 0??>/: C?4(● Per % Seglen, $itations and Oournal im.act -actor: uestionable

indicators o- researc" *uality, $ller"y  31:00, 0232=023<, 0??>(● $"ristiano Karin, Manuela $attelan, and Da6id Firt" Statistical

Modelling o- $itation 5Lc"ange among Statistics ournals, ari6

.re.rint ari6:0901(0>?C 1209/(

56idence=#ased Sentencing

● Massimo $alabresi, Attorney )eneral 5ric @older to %..ose Data=

Dri6en Sentencing, Time %a"a'ine, uly 90, 120C● uis Daniel, ;"e dangers o- e6idence=based sentencing

mat"babe blog guest .ost, %ctober 10, 120C(

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● SonOa Starr, Sentencing by t"e 'umbers 'e8 Gork ;imes %.=5d,

August 02, 120C(● 5ileen Sulli6an and +onnie )reene, States .redict inmates& -uture

crimes 8it" secreti6e sur6eys, AP, February 1C, 1203(

W"ite @ouse +e.ort● #ig Data: Seizing %..ortunities, Preser6ing Kalues, 5Lecuti6e

%Qce o- t"e President, May 120C(● essica Mc7enzie, W"ere t"e W"ite @ouse &#ig Data& +e.ort Falls

S"ort tec".resident(com, May <, 120C(● $at"y %&'eil, Inside t"e Podesta +e.ort: $i6il +ig"ts Princi.les o-

#ig Data mat"babe blog, May >, 120C(

 ;"e Facebook Mood Mani.ulation 5L.eriment

 ;"is "as generated an immense literature o- res.onses in a 6ery s"ort time(A 6ery eLtensi6e bibliogra."y is "ere:

● ames )rimmelman, ;"e Facebook 5motional Mani.ulation Study:

Sources The aoratorium(

I list belo8 only a com.arati6e -e8 t"at I read and t"oug"t interesting(

● Adam $"andler, ;"e Many +easons to Dislike Facebook&s Mood

Mani.ulation 5L.eriment( The !ire, une 14, 120C● 7ate $ra8-ord, ;"e ;est We $an === and S"ould === +un on

Facebook, The $tlantic uly 1, 120C(● o"n )ro"ol, $omments on 5motional $ontagion on FacebookB

More ike #ad +esearc" Met"ods, .syc"central(com blog, une 14B/,

120C(● Adam D(I( 7ramer, amie 5( )uillory( and erey ;( @ancock,

5L.erimental e6idence o- massi6e=scale emotional contagion t"roug"

social net8orks, P'AS, 6ol( 000 no( 1C, 120C, 4>44=4>?2(● Adrienne aFrance, 56en t"e 5ditor o- Facebook&s Mood Study

 ;"oug"t It Was $ree.y The $tlantic %a"a'ine une 14, 120C(● +obinson Meyer, 56eryt"ing We 7no8 About Facebook&s Secret

Mood Mani.ulation 5L.eriment , The $tlantic %a"a'ine, une 14, 120C(● anet D( Stem8edel, Some t"oug"ts about "uman subOect

researc" in t"e 8ake o- Facebook&s massi6e eL.eriment, Scienti1c

 $merican lo", une 92, 120C(● Zeyne. ;u-ekci, Facebook and 5ngineering t"e Public, 

medium(com, une 1?, 120C(

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● 7aty Waldman, Facebook&s Jnet"ical 5L.eriment, , Slate, une

14, 120C

Social Media Mani.ulating 5lections

● Mica" Si-ry, W"y Facebook&s Koter Mega."one is t"e +eal

Mani.ulation to Worry About, Personal Democracy Plus, uly 9, 120C(● Mica" Si-ry, Facebook Wants Gou to Kote on ;uesday( @ere&s @o8

It Messed Wit" Gour Feed in 1201( %other 2ones, %ctober 90, 120C(

5L.osure to ideologically di6erse ne8s and o.inion on Facebook

● 5szter @argittai, W"y doesn&t Science .ublis" im.ortant met"ods

in-o .rominentlyB crookedtimber(org, May >, 1203(● 'at"an urgenson, Facebook: Fair and #alanced Cyor"olo"y  May

>, 1203(

● $"ristian Sand6ig, ;"e Facebook It&s not our Fault Study, SocialMedia $ollecti6e, May >, 1203(● Zeyne. ;u-ekci, @o8 Facebook&s Algorit"m Su..resses $ontent

Di6ersity Modestely/ and @o8 t"e 'e8s-eed +ules Gour $licks, 

medium(com, May >, 1203(

@ealt" Sur6eillance

● ose." Walker, $an a Smart."one ;ell i- Gou&re De.ressedB A..s,

%t"er ;ools @el. Doctors, Insurers Measure Psyc"ological Well=#eing,

Wall Street ournal, an( 3, 1203( $omment by $at"y %&'eil, mat"babe

blog, an( <, 1203(

#ook +e6ie8s

The Formula+ 3ow $l"orithms Sol-e $ll 4ur #rolems 555 $nd Create %ore by ukeDorme"l

● uke Dorme"l, Algorit"ms are great and all, but t"ey can also

ruin li6es( eLcer.t -rom book/( Slate 'o6ember 0?, 120C(

/ataclysm by $"ristian +udder

● $at"y %&'eil $"ristian +udder&s Dataclysm mat"babe blog,

Se.tember 0<, 120C(

The lack o6 Society+ The Secret $l"orithms that Control %oney and Information by Frank Pas*uale

An eLtremely ne book( Also, t"ere is a 8ealt" o- -urt"er re-erences in t"e

-ootnotes(

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● Da6id Auerbac", ;"e $ode We $an&t $ontrol Slate, an( 0C, 1203(

Social #hysics+ 3ow 0ood Ideas S*read 555 The essons from a New Science bySandy Pentland

● William #utz, Stressing Patterns o- 5Lc"ange, Science, 9CC:02?>,

 une <, 120C(● 'ic"olas $arr, ;"e imits o- Social 5ngineering A.ril 0<, 120C(● $at"y %&'eil, 'o, Sandy Pentland, let&s not o.timize t"e status

*uo mat"babe blog, May 1, 120C(

The Si"nal and The Noise by 'ate Sil6er

● )ary Marcus and 5rnest Da6is, W"at 'ate Sil6er )ets Wrong, 

'e8 Gorker online, an( 13, 1209,● $at"y %&'eil, 'ate Sil6er con-uses cause and eect, ends u.

de-ending corru.tion, mat"babe blog, December 12, 1201(

!ho7s i""er8 by Ste6en Skiena and $"arle8 Ward

● 5rnest Da6is, +e6ie8 o- !ho7s i""er8 !here 3istorical Fi"ures

eally ank  by S( Skiena and $( Ward( SI$% News Marc" 120C(● %li6ier ecarme, +e6ie8 o- !ho7s i""er8 in Com*utin" e-iews,

T0C10C<, A.ril 4, 120C(● $ass Sunstein, Statistically, W"o&s t"e )reatest Person in

@istoryB W"y *uants can&t measure "istoric signicance, The New

e*ulic December 9, 1209

+andom

● 'ick #ilton, Friends and Inuence -or Sale %nline, New York

Times, A.ril 12, 120C(● Mike #oe"m, )oogle&s 8rong in-ormation about M%$A misleads

museum=goers( os $n"eles Times, uly 00, 120C(

Satire

● )oogle 'est● %rdering Pizza in t"e Future, A$J, 122<(

Appendi$ 2' Inter"ie& transcripts

I0TERIE TRA0)*RI!T %anager

'Do you consider Big Data a management ideaP

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' I am not sure &hat you mean )y management idea, )ut I can tell you &hy I find )ig data useful

for management decisions. I thin )ig data aids at management decisions )ecause they increase

analytics and you get a )etter picture of &hat is going on in your )usiness. Also, &hat customers

are doing is also coming from )ig data, more analytics is happening in all companies. And no&

you see &ith IG6R BI tool &e find ne& &ays to import, and the ne& "Is is )ecause &e are

collecting more data. 5e are getting more information, such as ho& many customers are

choosing a particular useful asset at a time4. And then from that you can get and tal to your 

customers in a different &ay. Jou see, let<s say, that all these customers from this industry are

 )uying trac trailers. 5hen see this &e go to management and sho& them &hat &e found, and go

to the mareting department and they say o let<s concentrate on the mareting for these

customers in this specific asset types4

'Therefore, &hat factors=criteria did guide you to the decision to implement these systems of Big

Data analytics, especially compared to other programsP

' The main reason for us to s&itch o#er from our pre#ious systems, the insufficiency of the

 pre#ious program and ne& systems )een created> that is the change of companies, this is the

cause of e#erything and that is the cause of the system s&itch'o#er. And then from then they

analy9e &hether they &anted to )uy the updated #ersion from the system &e &ere using )efore

for the company itself, or they &anted to s&itch to a different system. And that meant the

management team looed all the different products, and they had sales pictures from all the

different companies and KpitchedPL pretty much for all the different functionality that the

 programs used, the prices. And they came to the decision of IG6R and I can no& that 2'3

main )ig things of IG6R &ere )etter o#er (A6ARI and (A" &ere the )uilding of reporting and

consolidation system, is one of the )iggest things in producti#ity sa#ings.

'Do you thin the decision &as affected )y any consultantsP

' Definitely

' 7o& ha#e you heard a)out IG6RP Any consultants or management gurusP

' 5hat &e had &as IG6R consultants coming in, and &e )efore &e made any decisions on the

 program, they came in and sho&ed us e#erything. (o it is pretty much IG6R salespeople. They

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came in, and lie it &as &ith (A6ARI and (A", they sho&ed us the program and &hat people

can do, and they sho& us &hat other companies can do. And this is ho& the management team

decided on the program

'Go&, during the implementation phase, &hich means do you find important for effecti#ely

implementing a ne& idea, a ne& systemP

'The )iggest thing I can thin of, from my e$perience, is getting all the feed)ac and information

from different sources from all around the &orld. Because, &hat &e ha#e seen in the past &ith all

the other systems is that the core group, in charge of implementing it, has not done enough

research for the use of it, or ho& they use it, or &hat they ha#e pro)lems &ith. And yes they

sol#ed the pre#ious eras, )ut there is ne& ones happened, )ecause they ha#e not gathered all

information. (o I thin this one thing &e need to )e careful of, is collecting all the information so

this ena)les the info team to )uild something that is actually useful for the company. (o it is all

a)out the information, and data collecting, researching the old processes. 5e should sit in these

departments, and &atch them do e#erything and tae notes of all the processes. And from there

&e no& e$actly &hat they can do, instead of ha#ing a #ague picture, and implement a )etter 

system.

'7o& do you introduce such an ideaP

'Definitely it is useful to create #agueness for the pre#ious program and point out all the

negati#es, in order to create a more positi#e attitude to&ards the ne& program. The most

important thing is to in#ol#e the users of such a program, )ecause they are going to use the ne$t

system as &ell. Especially in some regions in other countries, people in the )ranches feel lie

their #oice doesn<t get heard. They see a lot of things and changes happening, and they said this

doesn<t help us. This is something &e are trying to change &ith IG6R> getting them in#ol#ed in

the process and getting their opinion you mae sure you capture all their re/uirements and needs

from such a program. Jou mae e#ery department to feel important in a &ay.

And ;ust on the other /uestion> yeah, &e ha#e to in#ol#e them, )ut I thin in training as &ell.

And that is one thing &e may ha#e missed a little )it.

7o& did you manage to diffuse and communicate the idea &ithin the companyP

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'Jeah, you refer to the dissemination process.

'Because &ith the finance reporting, it got rolled out #ery /uicly. 5e had to replace the old

system and &e did not ha#e time for training. Gone of the other people in other regions really got

any training. (o they see that they are gi#en a ne& product and they ha#e to use it, )ut they thin 

that no one told &hat to do &ith this, no one told me ho& to use it.

'Did the salespeople that came to present the idea use any case studies for already implemented

casesP

'JeahQ And &hat they do, the consultants offered to help us &ith things &e didn<t no& in the

IG6R. The salesmen go to a ne& company and they present the program and a lot of times they

refer to other companies. They said they also use it in "5* and they used in such a &ay, so &e

no& it is possi)le.

And referring to your other /uestion, &hen &e chose the system, &e had consultants coming in

and sell it to us. And I thin &e need to do the same thing to the regions. 5e ha#e to go there and

sell it to them, )ecause it &ould )e pointless implementing this if they ;ust carry on doing &hat

they already do.

'(o you first ha#e to implement it here and then sell4 it to the other regionsP

' I thin yeah, &e ;ust need to sell it )etter. 5e forget that the decisions already ha#e )een made

to use this, )ut in the regions it has )een made yet. And )ecause of the change they do not really

no& &hat to do, and instead of IG6R they may use the E$cel.

'(o are you thining to use these consultants again, in order to create a positi#e attitude to&ards

IG6RP

' Go, pretty much it &ill )e me and Ian, going to the regions. But again I thin &e forget that &e

are going to sell it to the other users as &ell. This &ill impro#e e#erything and people &ill use it

correctly. 5e need a portfolio of good stories and case studies.

Inter"ie& transcript %Big Data employee

 

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1.  5hat is your impression of this mgt idea a ne& Big Data Analysis, a ne& )usiness

intelligence toolP

!y first impression is that it has rather remara)le functionality as a system )ut during the phase

of implementation that &e are no& they ha#e emerged a lot of issues ha#ing to do &ith more

technical stuff lie the ser#er and the IT resources. (o generally I thin that this management

idea, IG6R , is a great tool )y itself to mae analysis of Big Data )ut &e should ha#e more

resources in order to do it effecti#ely> E#eryone &ho is part of the implementation lie the 6"8A

team , they all no& that is a great pro)lem )ut the rest of the region that they are not in#ol#ed

they cannot see that )ecause they ha#e a lot of issues lie the system is crushing or they are not

getting they right num)ers , so &ith the lac of no&ledge that they ha#e in this specific tool

they cannot trust it and understand its capa)ilities. They do not so use it no& )ecause they ha#e

al&ays pro)lems that are related mostly &ith their lac of no&ledge.

2.  Alright so , if you are lie a manager for this pro;ects &hat &ould you change in the &ay

that this management tool &as introduced, disseminated and implementedP

I thin the main pro)lem came from the IT resources and another important factor &as that in the

 )eginning of the implementation of this )usiness intelligent tool, IG6R, &e had another pro;ect

manager lie &e ha#e no& 7ugo , that he left the company and he too a lot of no&ledge &ith

him , ha &as in charge of a lot of things relating to the pro;ect implementation and re/uiring a lot

of no&ledge> (ome people set different functional specs for IGfR )ut nothing &as

documented so &e had to reteach oursel#es and redo a lot of things , so this cause a lot of 

dissatisfaction and frustration.

3.  Jou thin that IG6R as intelligent tool fits in the conte$t of the company or that they try

to tae it as tool and fit it for more strategic reasonsP

I thin it fits for the 6"8A department that I represent )ut I am not /uite sure ho& is going to fitfor the rest of the departments and ho& is going all of our &arehouse )e inputted into IG6R. It

&ill re/uire /uite a lot of &or and critical thining. 6or finance do I thin is #ery good )ut for 

the other departments , I am not sure.

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-.  Do you thin that the managers should ha#e something lie a sur#ey or another &ay to

as, in#ol#e and engage people in this pro;ect or you are in agreement &ith the &ay that &as

introducedP

I thin the &ay they did it &as correct , )ecause most of the times if you as your people, your 

employees if they &ant to change something most of the times the &ill )e resistant to change,

they do not lie change e#en if they ha#e gains from it. (o I thin is )etter to )egin &ith a pro;ect

team not of course only managers )ut different positions &ithin the company, lie on senior, one

 ;unior, some employees, some IT etc. and then these people )e used lie agents to diffuse this

no&ledge &ithin the different departments, )ut no not to engage e#eryone in this.

0.  5hat do you thin &ould )e a &ay of effecti#e dissemination in order to implement the

 pro;ect then &ith a more engaged en#ironment of peopleP Do yourself feel engaged in the

 pro;ectP

5ell, I am not /uite sure, yes myself I feel in#ol#ed )ecause I am part of this pro;ect from the

finance department> )ut I thin if they ha#e more training from e$perts lie IG6R consultants

&ho &ill no& in deep all the functionalities of the program it &ould )e #ery important and

decisi#e I thin, )ecause no& &e ha#e the team of the pro;ect )ut &e ha#e no e$perts so &e

in#estigate issues and the rest of the company cannot de#elop an ade/uate trust to this BI tool

 )ecause they do not ha#e some)ody to ans&er all of their issues. If &e ha#e lie I said some

consultants for si$ months at least to transfer us their no&ledge and then &e eep going on our 

o&n it &ould )e #ery effecti#e.

@.  Do you thin that the use of other success stories of other companies &ould help in the

engagement of the peopleP Do you use such storiesP

Jeah, I thin it is #ery important> &hen &e &ere informed for e$ample that 7eineen is using the

same BI tool , &e appreciate it a lot and &e thin that &e can do it too and it can actual &or andyou start to feel more confident a)out it.