Essays /

15 Cscmp Research Big Data Glob Essay

Essay preview

Written by:
R. Glenn Richey, Jr., PhD
Harbert Eminent Scholar Chair in Supply
Chain Management
Raymond J. Harbert College of Business
Auburn University
[email protected]
Tyler R. Morgan, MBA
Doctoral Candidate
Culverhouse College of Commerce
The University of Alabama
[email protected]
Kristina K. Lindsey, MSM
Doctoral Student
Culverhouse College of Commerce
University of Alabama
[email protected]
Frank G. Adams, PhD
Assistant Professor of Marketing and
Supply Chain Management
College of Business
Mississippi State University
[email protected]
Chad W. Autry, PhD
Professor of Supply Chain Management
Haslam College of Business
The University of Tennessee
[email protected]
Editor: Madeleine Miller-Holodnicki
Direct Line: +1 630.645.3487
E-Mail: [email protected]
2014-2015 CSCMP Research
Strategies Committee
Morgan L. Swink, Chair
Texas Christian University
Lee Beard
Nike
Soumen Ghosh
Georgia Institute of Technology
Glen Goldbach
PwC
Susan Golicic
Colorado State University
Scott Grawe
Iowa State University
Jeffrey Jagiela
Pfizer, Inc.
David Kaduke
Limited Brands/MAST Global Logistics
Glen McIntosh
Dominos Pizza, Inc.
Gilbert Nyaga
Northeastern University
Steven Raetz
C.H. Robinson
Judith M. Whipple
Michigan State University
© 2015 Council of Supply Chain
Management Professionals

GLOBAL MANAGERIAL
PERCEPTIONS OF BIG DATA
STRATEGY IN SUPPLY
CHAIN MANAGEMENT
ACKNOWLEDGEMENTS
This research paper was executed and completed thanks to funding provided by the Council of Supply Chain Management Professionals (CSCMP) and a partnership between CSCMP and The University of Alabama. The researchers thank the many confidential business people and professors who contributed to the quality of the final product, including Professors Raj Agnihotri, Banu Bas, Haozhe Chen, Pat Daugherty, Stan Griffis, Morgan Swink, and Judy Whipple. We would also like to thank Taylor Wade and Alyssa Yoon for assistance in the data collection process and Bebe Barefoot Lloyd for making sure we completed the communication of the document in a literate fashion.

INTRODUCTION
“Big Data” seems to be industry’s hottest buzzword, but there is limited agreement regarding what it is, its purpose, and its role in the future of business. Many experts think it will revolutionize industry in a major way by enabling managers to make more informed, data-driven decisions. These stakeholders should be excited as “IDC predicts that the market for Big Data will reach $16.1 billion in 2014, growing six times faster than the overall IT market.”i More conservative companies, however, suggest that Big Data’s value is overblown and that it is little more than an extension of other “analytics” or traditional marketing research. Therefore, on behalf of CSCMP, we have undertaken a study to get to the bottom of the Big Data craze and uncover how supply chain partners view it in relation to business strategy development.

We are seeking answers to questions relating to how supply chain partners are employing Big Data in supply chain strategy, Big Data’s potential to influence performance, and obstacles to exploiting its potential. We are doing this in two phases, beginning with a qualitative assessment to lay the groundwork for a more in-depth quantitative analysis. This report provides the results of the first phase. One of the goals of this phase was to uncover a managerially agreed-upon definition of Big Data. Such agreement is needed to develop any level discussion of what managers are collecting, manipulating, and exchanging. We also developed this study to begin developing an understanding of best practices concerning supply partnerships and Big Data. Thus, we will address supply chain manager perceptions of the opportunities and obstacles Big Data creates for the supply chain.

What Does Big Data Mean in the Supply Chain?
The discussion of “what” Big Data “is” lags behind proponents’ declaration of its importance to industry. Consultants, third party research groups, software developers, and the popular press are all ripe with discussions about Big Data as the potential “silver bullet.” Some go as far as to suggest that companies that do not harness the power of Big Data will fail. But what is Big Data and where does it come from? Is it created outside or inside the firm, or both? Is it strategic or operational? The answers to these questions are few. From a supply chain perspective, consensus typically develops these answers. Thus, our first step was to develop a grounded understanding of what managers think Big Data is from an activity- or process-based perspective. In other words, what are supply chain managers using and calling Big Data in daily operations and strategic planning? Having a clear understanding of like terms has long been a challenge in supply chain relationships. It is nearly impossible for managers to work collaboratively without consistent terms and definitions. The creation of Incoterms is a great example of managing and simplifying the need for consistent terminology. Thus, it is of initial importance that we find an international consensus opinion (or definition) of what Big Data is in these relationships. This definition should be working and grounded, coming from management rather than being crafted by academia or organizations hoping to improve Big Data performance.

Obstacles and Opportunities
Following the basic conceptualization of Big Data, we sought to develop an initial understanding of Big Data’s future in supply chain relationships. From a best-practices perspective, managers often ask about the bridges and barriers to making relationship-based processes work. At the onset of a new management initiative, managers are asked to assess existing opportunities and threats (e.g., SWOT analysis). So, the next task was uncovering Big Data-related performance-based opportunities as well as disclosing obstacles to obtaining them successfully. Again, the best approach for making this type of assessment is to go directly to the managers in charge of these data-connected supply chain relationships.

Managers hope supply chain relationships will create a synergy that allows firms to increase revenue and/or reduce costs beyond what single firms can accomplish. Thus, while understanding opportunities is important, it is equally important, if not more so, to understand the potentially negative consequences that may reduce, eliminate, or decrease firm and joint-firm performance. Though important to supply chain management, these dynamics are often overlooked by software developers and managers embedded in technology departments with limited boundary spanning opportunities.

This document is available from our site and provided for your personal use only and may not be retransmitted or redistributed without written permission from the Council of Supply Chain Management Professionals (CSCMP). You may not upload any of this site's material to any public server, online service, network, or bulletin board without prior written permission from CSCMP.

2

Research Questions
The core of the first phase of our study sought answers to these questions: •

What is Big Data in a supply chain relationships context?
What are the obstacles to using Big Data in supply chain relationships? What are the opportunities for using Big Data in supply chain relationships?

This study uses a grounded qualitative interview technique to address these broad questions. These openended questions are intentionally general and provide a limited structure in order to allow respondents to answer at length, thus reducing any interviewer bias by providing too much direction. This method will be discussed briefly in the following section. Subsequent to the methods section, we will discuss our industry-based results and conclusions from a global perspective as well as from six different country settings including China, Germany, India, South Korea, Turkey, and the United States.

METHODOLOGY AND SAMPLE
As this is an exploratory study, it was most appropriate to employ a qualitative research method. Our qualitative interview method started with respondent identification (or another way to say it: identifying respondents). Using the CSCMP membership roster, we identified managers from several countries who are known for using information technology across supply chain relationships. Our goal was to develop a breadth of understanding across several countries. Using academic research and popular press articles, we identified nine countries for consideration: Brazil, China, Germany, India, South Africa, South Korea, Turkey, the United Kingdom (UK), and the United States (US). Based on existing qualitative interview conventions, we set a goal of acquiring three to six interviews in all the countries except the US, where we hoped to complete ten to 12 interviews. This approach would allow us to examine the phenomenon at multiple levels. The large US sample allowed us to examine the US in depth as a domestic market while also allowing for a comparison of US versus non-US views, as well as country versus country similarities and differences.

Sample
The final sample included three interviews each from China, India, South Korea, and Turkey, six interviews in Germany, and 11 in the US. The chart below displays basic information on the respondent firms. This data has been restricted heavily to ensure that firms remain anonymous.

This document is available from our site and provided for your personal use only and may not be retransmitted or redistributed without written permission from the Council of Supply Chain Management Professionals (CSCMP). You may not upload any of this site's material to any public server, online service, network, or bulletin board without prior written permission from CSCMP.

3

Code

Industry (Type)

Firm Employee
Count

Supply Chain
Classification

C1

Kitchen and Bath products

10,000 - 5,000

Manufacturing

C2

Automotive

50,000 - 10,000

Manufacturing

C3

Electronics and Infrastructure

50,000 - 10,000

Manufacturing

G1

Automotive and Aerospace

50,000 - 10,000

3PL/4PL/Consulting

G2

Automotive Industry

Transportation

G6

Cosmetics - Customer Packaged Goods

50,000 - 10,000

Manufacturing

I1

Technology Engineering and Infrastructure

>300,000

Manufacturing

I2

Automotive

50,000 - 10,000

Manufacturing

I3

Water and Environment Management

5,000 - 1,000

Manufacturer

K1

Research and Development

50,000 - 10,000

Manufacturing and Retailing

T3

Hardware

10,000 - 1,000

Retailing

U1

Energy

10,000 - 1,000

Manufacturing

U2

Paper Products

100,000 - 50,000

Manufacturing

U3

Automotive

1,000 - 500

Manufacturing

U4

Automotive

5,000 - 1,000

Manufacturing

U5

Automotive

100,000 - 50,000

Manufacturing

U6

Publishing

5,000 - 1,000

Manufacturing

U7

Healthcare

5,000 - 1,000

Retailing

U8

Retail

>150,000

Retailing

U9

Dairy

50,000 - 10,000

Manufacturing

U10

Energy

5,000 - 1,000

Manufacturing

U11

Retail

> 150,000

Retailing

This document is available from our site and provided for your personal use only and may not be retransmitted or redistributed without written permission from the Council of Supply Chain Management Professionals (CSCMP). You may not upload any of this site's material to any public server, online service, network, or bulletin board without prior written permission from CSCMP.

4

Sample
Our interview method differed from what CSCMP studies normally employ. We used a “going native” qualitative interview method to survey these 31 senior-level supply chain managers around the world. This technique, which is often used in sociology and anthropology research, is termed a “native categories” analysis (Harris 2000). Detail on this method may be found in the Appendix. The results follow.

RESULTS
In this section, we will discuss the results of our analysis as detailed above. The results section is broken out across our three research questions (see section 1.3 above) and into two areas: 1) A unified global perception from the respondents regarding each research question 2) Within-country domestic perceptions regarding research questions

What Supply Chain Managers Call “Big Data”
Why should supply chain managers care about the Big Data Era? One of our respondents made the following point:
“[Big] Data contain a wide variety (of information). I see so many databases made without purpose or definition. Everyone needs his or her own definition of Big Data in order to use it in a productive way.” – K3 We quickly learned that there is no practical general definition or understanding of what is being called Big Data. The respondents in this study discussed everything from unstructured “tweet-” based data to internal logistics and operations indicators. Thus, as supply chain managers we need to be careful about what we are specifically discussing when we use the term Big Data. “I am not familiar with that wording [Big Data]. My understanding for what you are after is that you want to understand how we are dealing with the one version of the truth, for example, in terms of data information. What we are using in our group is BI, which is Business Intelligence, and the different systems in our landscape, in our supply chain organization are feeding the data information and should therefore result in one version of the truth.”– G4 As previously discussed, academia and consultancy have terms that define the context of what is being studied, not what the executives are using the term to mean. This incongruity may be in part responsible for the communication problem(s) among top management, data scientists, consultants, and software manufacturers. Nevertheless, some ties do bind how Big Data is viewed globally.

Defining Big Data: Global Views
Globally, managers see Big Data management as important:
“I think it’s having access to and making sense of your industry data and taking advantage of it, as best you can, using it to drive decisions and see trends. It’s grasping at all the information, putting it into meaningful models, and using it to drive decision making.” – U11

This document is available from our site and provided for your personal use only and may not be retransmitted or redistributed without written permission from the Council of Supply Chain Management Professionals (CSCMP). You may not upload any of this site's material to any public server, online service, network, or bulletin board without prior written permission from CSCMP.

5

These types of quotations were prevalent throughout the data collection process. Yet managers seemed to all have a different understanding of what “it” is. Rather than create an academic argument, we worked to synthesize what we uncovered. Part of the problem in doing this is that respondent supply chain managers each defined Big Data according to their own needs. For instance: “To me, Big Data is more like the orders/details in supply chain, including how many shipments for each order, the route for each one, the weight, the CBNs for each shipment, things like that. So it’s the very details of our data operations in supply chain.” – C1

This is an example of an operational view of Big Data. While the internal data of the corporation is immense and thus certainly “Big,” the comment is hardly consistent with what SAS calls Big Data, namely, “the exponential growth and availability of data, both structured and unstructured.”ii Other managers take the concept to the largest macro view, perhaps beyond the SAS definition: “Compiling all data in huge databases available across countries and business unit records.” – G1 Here the managers consider truly multi-level, multi-firm, and multi-industry data across all locations and countries. This broad view encompasses what most concerns supply chain managers – how do we make this data useful? and how do we make viable what we can’t define strategically and operationally? McAfee and Brynjolfsson (2012) did an exceptional job opening the discussion on Big Data by defining it according to three specific aspects: the volume of information produced, the velocity at which it is created, and the variety of forms it takes. SAS expanded this definition to include the terms/dimensions of complexity and variability. All of these dimensions appeared in at least some way in our data. Volume was an issue of universal concern across our sample. Respondents across all countries noted the immense amount of data available.

“How can you get sensible … information out of the Big Data volumes or enough data to be able to support [and], efficiently analyze the strategies or improve the company.”– G3
“Big Data to me, and I based it mostly on what I do, is a massive amount of information and how do you analyze and store it, and then, identify trends in statistical analysis to make business decisions?” – U8

Many of the respondents shared an understanding that the volume of data being handled makes this new era different from the past. Despite disagreements about the data’s value, general agreement existed concerning the huge amount of data now available for decision making.

The respondents also globally noted the velocity of data growth as a defining characteristic that makes the era of Big Data different from past data management projects. When we consider the growing use of RFID tags, scanner data, etc., that provide instant information, we easily can see data’s multiplicative rate of speed growth. Respondent U2 noted concerns about his supply chain being able to keep up with the speed of Big Data along with growth. In fact, nearly every respondent discussed the growth in velocity of the data being produced. As will be discussed later, speed is a defining aspect of Big Data as supply chain managers consider velocity to be both an obstacle and an opportunity.

This document is available from our site and provided for your personal use only and may not be retransmitted or redistributed without written permission from the Council of Supply Chain Management Professionals (CSCMP). You may not upload any of this site's material to any public server, online service, network, or bulletin board without prior written permission from CSCMP.

6

Less understood, but openly discussed by supply chain managers, is the existence of a huge variety of sources.
“…the combination of mainly bringing all different data areas together, and having more and more diverse portfolios or unharmonized, I mean just that you bring things under market, like apps, you bring things under market like web services or web solutions. You bring this kind of stuff, let me say more and more into the operations processes. Then, you have to take care of the prep work that is connected so to say to ‘backroom or backbone,’ of not getting really spread out with this kind of data. It is also the increase of the data volume, while you have all kinds of connections, which we have from outside into our company, and from the inside out of our company. So, maybe that what we see as … all the interactions, which we have to establish and maintain and also then the collections between all of our systems.” – G5 Data today comes in various formats and from myriad locations. Most supply chain managers are accustomed to structured data. This is largely numeric data or qualitative data translated into numeric form for traditional databases or dashboards. Most of this data is operational in context and can be within a company or across partnerships. The more unfamiliar data source is considered unstructured, or data that is not predefined for a row and column data set. Unstructured data comes in the form of text documents, e-mail, audio, financial transactions, and even video. Supply chain managers are only beginning to learn how to tap the huge varieties of available data.

Our second Turkish respondent (T2) brings the first three dimensions together in his definition of Big Data by commenting that Big Data is “fast,” “big,” and “diverse.” A fourth dimension was a specific result of the “native” discussion. SAS used the term variability to discuss fluctuations in data volume. Our job in supply chain management is to handle changes in volume and usage, so it is only slightly surprising that none of our managers voiced concern with this issue. Yet variability in larger form c...

Read more

Keywords

+1 -2015 -256 -361 -68 -770 /en_us/insights/big-data/what-is-big-data.html /sites/gilpress/2013/12/12/16-1-billion-big-data-market-2014-predictions-from-idc-and-iia/ 000 1 1.3 10 100 11 12 13 14 15 150 16 16.1 17 18 19 2 20 2000 2002 2010 2012 2014 2015 2050 21 22 23 237 24 25 26 27 28 29 3 30 300 31 32 339 3pl/4pl/consulting 4 5 50 500 6 60 630.645.3487 7 70 755 8 9 90 a.s abil abl academ academia accept access accompani accomplish accord account accur accuraci accustom achiev acknowledg acquir across act action activ actual ad adam adapt add addit address adequ adjust ado adopt advanc advantag advis aerospac affect africa age agenc aggreg agil agnihotri agre agreed-upon agreement air alabama albeit alloc allow almost alon along alongsid alphabet alreadi also alter although altogeth alway alyssa amazon ambigu among amount analys analysi analyst analyt analyz and/or anonym anoth answer anthropolog anxieti anybodi anyon anyth app appar appear appendix appli applic appreci apprehens approach appropri archiv area aren argument aris around arous arriv articl ascertain ask aspect assembl assess asset assist associ attempt attent attract attribut atyp auburn audio autom automat automot autri [email protected] avail avers avoid awar away back backbon background backroom bad balanc bangalor banu barcod barefoot barrier bas base basi basic bath bear beard bebe becom begin begun behalf behind believ benefici beneficiari benefit best best-practic better beyond bi bias bid big bigger biggest billion bind bless block blow board border bottom boundari boundary-span brands/mast brazil breach breadth break bridg briefli bright bring broad broader broken brynjolfsson built bullet bulletin bunch burden bureaucrat busi buzzword c.h c1 c2 c3 calibr call came candid cannot capabl capac captur car care cascad case categor categori cautious cbns center central certain chad chain chair challeng chanc chang channel character characterist charg chart chen china chines christian cite citi city-by-city-demograph claim clarif clarifi classif cleans clear client clients/consumers cloud code collabor collect colleg colorado column combin come comfort comment commerc commit committe common communic communiti compani company-wid comparison compat competit competitor compil complet complex complianc complic compon comprehens comput conceiv concept conceptu concern conclus concur conduct confid confidenti confirm conflict confus connect consensus consequ conserv consid consider consist consolid constant constantly-upd consult consum consumer-level contact contain context continu contract contract-by-contact contrast contribut control convent converg convers convert convey convinc coordin core corpor correct correl correspond cosmet cost cost-benefit could council count countri cours cover craft crawler craze creat creation critic crm cross cross-bord cross-cultur cross-funct crucial crunch cscmp cultur culverhous currenc current curs custom cycl daili dairi damn dashboard data data-connect data-driven data-gener data-rel data-us databas dataori dataset date daugherti david day day-to-day deal dearth decad decid decis decisionmak declar decreas dedic deep deeper deepli defici defin definit degre deliv deliveri demand demograph denot depart depend depth deriv describ descript design despit destroy detail determin devalu develop development/limited devic differ differenti difficult difficulti dig dimens direct dirti disagr disagre disast disastr discard disclos disclosur discount discov discoveri discuss display dissemin distanc distinct distribut distrust divers doctor document doesn domest domino done draw drive driven driver dual dump dynam e e-mail e.g earli earlier eas easi easili econom edg editor educ effect effici effort either electron element elimin embed embrac emerg emin emot emphas employ employe enabl encompass end end-us endnot energi engag engin english enhanc enlist enough ensur entail entir entiti entri environ environment equal equat era erp especi essenc essenti establish etc ethic evalu even eventu ever everi everybodi everyday everyon everyth everywher evid evolv exacerb examin exampl except exchang excit exclus execut exist expand expans expat expect expens experi expert expertis explain explan exploit explor exploratori exponenti export expos express extens extent extern extract extrem face facil facilit fact factor [email protected] fail fair fall familiar far farm fashion fast faster fawcett fear feasibl feed feedback feel field figur fill filter final financ financi find fine finish finit firm firm-level first five flow fluctuat focal focus follow food for-profit forc forecast forefront foreign forese forest forev form format forth forthcom forward forwardlook foster found foundat four fourth frank frequent friend frustrat fulli function fund furthermor futur g g.m g1 g2 g3 g4 g5 g6 gain game game-chang gap gather general generat georgia german germani get ghosh gilbert give given glen glenn glob global go goal goe goldbach golic good googl got govern government grain grasp graw great greater greatest griffi ground groundwork group grow growth guard guess guest guidelin hand handl haozh happen happi har harbert hard hardwar harri harvard haslam head headwat healthcar heavili held help hidden hierarchi high highlight hightech hinder hint hire holist holodnicki hope host hottest hous howev huge human i.e i1 i2 i3 idc idea identif identifi ignor ii iii ill ill-defin immedi immens impact imped implement impli implic import imposs improv in-depth inaccur inadequ inc includ incomplet inconceiv incongru incorpor incoterm increas increment independ india indian indic individu indoctrin industri industry-bas industry-level infanc inflex influenc inform infrastructur infus inhibit initi innov input inquiri insid insight instanc instant instead institut instrument integr intellig intensifi intent interact interest interfac intern internet internet-bas interpret interview interviewe intimid introduct intuit inund invest involv inward iowa irrelev isn isol issu it-driven j j.s jagiela jeffrey job joint joint-firm journal jr judi judith jungl k k1 k2 k3 kaduk keep key keyston kind kingdom kitchen [email protected] know knowledg known korea korean kristina l labor lack lag land landscap languag larg larger largest last later latter law lay lead leadership leak learn least lee legal legal/ethical legisl lend length less let level leverag life like likewis limit lindsey line link linkag list liter littl lloyd locat logist long long-run long-term longer look lose lost lot low lower m machin machine-to-machin macro made madelein magnan mail main maintain major make maker mammoth manag manageri manger mani manipul mankind manpow manual manufactur map market market-bas mass massiv master materi math matter matur may mayb mba mcafe mcintosh mean meaning measur mechan member membership mention method methodolog metric [email protected] michigan might mile miller miller-holodnicki million mind mindset mine minim minimum minut minutia miscalcul misinterpret miss mississippi mistak model moment money monitor monster moreov morgan most move msm much multi multi-firm multi-industri multi-level multilevel multin multipl must mutual myriad naiv name narrow nation nativ natur navig near necessari necessarili need negat neither network nevertheless new newli news next nike nine non non-us none normal northeastern note noth notic nuanc numb number numer nyaga object observ obstacl obstruct obtain obvious occur offer offic often older one onlin onset open openend oper operationally-focus opinion opportun optim optimist option order order-stock orders/details organ origin other otherwis outcom outdat outlook output outsid outsourc outweigh overal overblown overlap overlook oversimplifi overtim overwhelm own owner ownership pace packag paper paradigm part parti particip particular partner partnership past pat path patron pattern pay penalti peopl per perceiv percept perform performance-bas perhap period permiss person personnel perspect pfizer phase phd phenomenon physic pictur piec pillar pizza place plan platform play plus point point-of-sal polici poor popular portfolio portion portray posit possess possibl potenti power practic precis predefin predict predomin prefer prep present press pressur preval prevent previous price primari primarili prime prior priori prioriti privaci privat problem process process-bas procur produc product profession professor profici profit program project promin proper propon propos protect provid public publicly-trad publish pull purchas purpos put pwc qualifi qualit qualiti quantifi quantit quantitative-bas question quick quicker quit quotat r r.g race raetz raj rang rapid rate rater rather raw raymond re reach react reaction readi readili real real-tim realiti realiz realli reason recal receiv recogn recommend reconcil record red redesign redistribut reduc reduct redund refer refin reflect regard region regul regular reinvent relat relationship relationship-bas relay releas relev reli reliabl reluct remain rememb remind repackag repetit report repres request requir research resid resist resourc respect respond respons restrict result retail retail-level retain retent retransmit retriev return reveal revenu revers review revolut revolution revolv reward rfid rhetor rich richey [email protected] right ripe risk road roadmap roath robinson roi role room roster rout row rule run s.e safeguard said sale sampl santiago sap sas satisfi save savvi saw say sbu sbus scanner scholar scientist scope scott search second section sector secur see seek seem seen select seller senior senior-level sens sensibl sensit sensor sent separ serious serv server servic set sever shanghai sharabl share sharehold sharper sheer shelf shift ship shipment shop short shortag signific silver similar simpl simpli simplifi singl site six size sizeabl skeptic skill slew slight slow small smaller smart social sociolog softwar solut solv somebodi somehow someth sometim somewhat somewher soon sort sought soumen sourc south space span special specialti specif specifi specul speed spend spent spiral spite sport spot spread squeez stage stagger stakehold stan standard standpoint start state statist status step steven steward still stock stop storag store stori strateg strategi stream streamlin strict strike strong stronger structur struggl student studi stuff stumbl subject subsequ substanti success suffer suggest suicid summar superior suppli supplier support sure surfac surpass surpris surround survey susan svot swing swink swot synchron synergi synthes system system-wid t1 t2 t3 tabl tag take takeaway taker talent talk tank tap tape task taylor team tech technic techniqu technolog technology-assist technology-driven ten tend tendenc tennesse term terminolog terms/dimensions texa text thank theme theori therebi therefor thing think third third-tier though thought threat three throughout thus tie tier time timeli today togeth ton took tool top top-manag topic total touch toward track tracking/tracing trade tradit trailer train transact translat transnat transpar transport tree tremend trend tri [email protected] true truli trump trust trustworthi truth turk turkey turkish turn tweet two tyler type typic u1 u10 u11 u2 u3 u4 u5 u6 u7 u8 u9 uk ultim unansw uncertain uncertainti unclear uncomfort uncov understand understood undertak undertaken unfamiliar unhandl unharmon unifi unimport uniqu unit univers unknown unless unlimit unnot unobserv unquestion unstructur unsur untouch untrustworthi unwieldi unwil up-to-d updat upload upon upper us usabl usag use useabl useless user user-friend util valid valu valuabl vari variabl varieti various ve veloc vendor verac vers version versus viabil viabl video view violat visual vital voic volum w wade wait wall want warehous wari wast water way weak web weigh weight well well-train west western wheel whether whippl whose wide will willing wind window winner within within-countri without word work world worri would wouldn written www.forbes.com www.forbes.com/sites/gilpress/2013/12/12/16-1-billion-big-data-market-2014-predictions-from-idc-and-iia/ www.sas.com www.sas.com/en_us/insights/big-data/what-is-big-data.html year yesterday yet yoon zone