APRIL 2014
Sponsored by:
An Exclusive Survey and Research Report
COMPETITIVE EDGE R E S E A R C H R E P O RT S
ROADBLOCKS CRUMBLING:
Midmarket Companies See Early Success Big
DataWITH
Big data initiatives,
enabled by new
analytics tools and
strengthening ties
between business and
IT leaders, are helping
midsize organizations
achieve the improved
product quality and
decision-making once
reserved for large
enterprises.
BIG DATA: Midmarket Companies See Early Success
COMPETITIVE EDGE RESEARCH REPORTS 2
Contents3 Executive Summary
4 Methodology
5 Introduction 5 FIGURE1:“Growing Adoption of Big Data”
5 Big Data Drivers 5 FIGURE2:“Picking the Low Hanging Fruit”
6 Success Factors 6 FIGURE3:“Most Valuable Big Data Tools”
7 Early Results 7 FIGURE4:“A Big Boost for Decision-Making”
8 FIGURE5:“Where Big Data Is Successful”
8 Challenges 9 FIGURE6:“Data Volumes, Budget Limitations Top Big Data Challenges”
9 Lessons Learned/Key Observations 10FIGURE7:“Path to Success: Strong Ties Between Business and IT”
10 Summary and Conclusion
11 Sponsor’s Statement: Succeeding in the Data Economy: An Opportunity for All
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COMPETITIVE EDGE RESEARCH REPORTS 3
Executive Summary
BIG DATA: Midmarket Companies See Early Success
u 80 percent of survey respondents agreethatthey
needtobetteranalyzetheirrapidlyexpandingdata
collections.Amongtheirtopgoals:Improveproduct
quality,seizebusinessopportunitiesandspeed
decision-making.
u41 percent have one or more bigdataprojects
alreadyinplace;another55percentarestartingone.
uBudgets will rise to an average of $6 millionover
thenexttwoyearsascompaniesinvestmorein
hardware,softwareandtraining.
uThe biggest drivers ofbigdataprojectsuccess
areIT/businesscollaboration,properskillsand
performancemanagementtogaugetheeffectsofbig
datainitiatives.
uThe biggest causes of failure inbigdatainitiatives
arelackofIT/businesscooperationandlackoftools
andskills.
uThe most influential departmentsinbigdataprojects
areITandsales/marketing.
u The most effective tools inbigdatainitiatives
arereal-timeprocessing,predictiveanalytics,data
cleansing,datadashboardsandvisualization.
Methodology pie/bar chartsRespondents by title – pie C-level or vice president: 50%Director or manager: 50%
Respondents by functional area – pie IT involvement: 67%Business: 33%
Figure 1 – pie chart[head] Growing Adoption of Big Data[deck] 96 percent of respondents say they either already have one or more big data initiatives in place or are just getting started with one. (% responding)
My organization is just getting started with a big data project: 55%My organization has one or more big data initiatives in place: 41%My organization has no big data initiative in place but has discussed implementing such a program in the foreseeable future: 4%
Base: Survey of 300 executives at midsize organizations worldwide Source: Competitive Edge Research, November 2013 Big Data Survey
Methodology pie/bar chartsRespondents by title – pie C-level or vice president: 50%Director or manager: 50%
Respondents by functional area – pie IT involvement: 67%Business: 33%
Figure 1 – pie chart[head] Growing Adoption of Big Data[deck] 96 percent of respondents say they either already have one or more big data initiatives in place or are just getting started with one. (% responding)
My organization is just getting started with a big data project: 55%My organization has one or more big data initiatives in place: 41%My organization has no big data initiative in place but has discussed implementing such a program in the foreseeable future: 4%
Base: Survey of 300 executives at midsize organizations worldwide Source: Competitive Edge Research, November 2013 Big Data Survey
COMPETITIVE EDGE RESEARCH REPORTS 4
MethodologyTounderstandwhatdrivesmidmarketadoptionofbigdataprojects,andthe
successofthoseefforts,DellSoftwaresponsoredaglobalsurveyofmidmarket
executives.Inthequestionnaire,midmarketcompaniesaredefinedasorganizations
withbetween2,000and5,000employees.Thesurveyfindingshighlightthe
technologicalandorganizationalfactorsmostcriticaltosuccessandwherebigdata
initiativescanprovidethegreatestbusinessbenefitsnowandinthefuture.
CompetitiveEdgeResearchReportsposteda15-questionsurveyonaWeb
siteaccessibletoexecutivesfamiliarwithbigdata.Thesurvey,conductedover
fourdaysinNovember2013,resultedin300responseswitha5.5percent
marginoferror.Inpresentingtheresults,weonlyhighlightdataofclearstatistical
significance,i.e.,resultsdifferingbymorethansixpercentagepoints.
The objectives of the program were to:
uUnderstandthedriversformidmarketadoptionofbigdataprojects,thecritical
successfactorsinprojectimplementationsandwherebigdataishelpingusers
realizevaluefromtheirinvestments.
uDocumentthetechnicalandbusinesschallengesthemidmarketfaceswithbig
datainitiatives.
uExplorethetoolsandtechnologiesmidmarketfirmsneedtoimplementbigdata
projectsandthelessonstheyhavelearned.
uComplementthequantitativeandqualitativeinsightsfromthesurveywith
interviewsfeaturingconsultants,analystsandotherbigdataimplementers.
uCiteexamplesofbigdataandanalyticsusersinmidmarketorganizationsthat
addinsighttothesurveyfindings.
This research program included both quantitative and qualitative
components:
uAnonlinesurveydirectedatapproximately200U.S.-basedcompanies,with
theother100respondentscomingfromEurope/MiddleEast/Africa(EMEA)
andAsia/Pacificcountries.Allindustriesarerepresented,includingnon-profit
organizations.Formoreinformationabouttherespondents’demographics,
refertothe“Methodology”chartsatright.
uRespondentsarefromorganizationsusinganalytics,usingbigdatain
combinationwithanalyticsorconsideringabigdatainitiative.
uIn-depthtelephoneinterviewswithconsultants,analystsandotherbigdata
implementers.
CompetitiveEdgeResearchReportsprovidedsupportinthedevelopmentofthe
surveyquestionnaire,inadditiontoperformingthein-depthtelephoneinterviews
andthewriting,editingandproductionofthisreport.CompetitiveEdgeResearch
Reportsandtheauthorofthisreport,RobertL.Scheier,aregratefultoeveryone
whoprovidedtheirtimeandinsightsforthisproject.
Respondents by title
Respondent responsibilities*Applicationperformancemanagement,includingcloud-basedapps
55%
ITgovernanceandpolicies,includingbudgeting 47%
ITsecuritysystems
42%
*For responsibilities, respondents were asked to indicate primary and secondary areas of responsibility.
Base:Surveyof300executivesatmidsizeorganizationsworldwideSource:CompetitiveEdgeResearchReports,November2013BigDataSurvey
Respondents by functional area
C-levelorvicepresident50%
Directorormanager 50%
ITinvolvement67%
Business33%
BIG DATA: Midmarket Companies See Early Success
BIG DATA: Midmarket Companies See Early Success
COMPETITIVE EDGE RESEARCH REPORTS 5
Encouragedbytheirearlysuccess—andexpecting25
percentgreaterbenefitsinmanyareasoverthenexttwo
years—surveyrespondentsexpectbigdatabudgetstorise
toanaverageof$6millionoverthenexttwoyears.
BIGDATADRIVERSMidmarketorganizationswanttheirbigdataprojectsto
resultinbetterqualityproductsandservices,newbusiness
opportunitiesandimproveddecision-making.Thosearethe
threetopgoalsdrivingrespondentstoact,followedclosely
byabetterunderstandingofcustomerneeds,quickly
respondingtocompetitivethreats,improvedmarketingand
predictingfuturetrends(seeFigure2,“PickingtheLow
HangingFruit,”above).Onlyafterthesetacticalnear-term
goalsdidrespondentsmentionotherbusinessdrivers,
includingreducingoperatingexpenditures;analyzingprofits
percustomer,productorlineofbusiness;andbetter
understandingconstituentsentiments.
“Peoplealwaysgrabthelow-hangingfruit.Ifyoucan
improvethequalityofaproductby5percent,orcut
youruseofmaterialby5percent,thebottomlineis
rightthere,”saysSteveKing,apartnerandanalystat
EmergentResearch,aresearchandconsultingfirm
focusedonsmallbusinesses.
INTRODUCTIONItisnowclearthatbigdatahastakenabigleapfromitsenterprise
rootsintotheboardroomsanddatacentersofmidmarketcompanies
everywhere.Accordingtothefindingsofanexecutivesurveyconductedin
November2013,midmarketorganizationstodayoverwhelminglybelieve
inthepotentialofbigdataprojectstohelpthemsolvetangiblebusiness
problems.Whatismore,theyarebackingupthatbeliefwithaction.
Considertheevidence.Eightoutof10ofthe300surveyrespondents
agreethattheyneedbetterdataanalysistomeettheirbusinessgoals.
Evenmoreimpressively,virtuallyallofthem(96percent)eitherhaveone
ormorebigdataprojectsinplaceorareintheprocessofstartingone
(seeFigure1,“GrowingAdoptionofBigData,”below).
Methodology pie/bar chartsRespondents by title – pie C-level or vice president: 50%Director or manager: 50%
Respondents by functional area – pie IT involvement: 67%Business: 33%
Figure 1 – pie chart[head] Growing Adoption of Big Data[deck] 96 percent of respondents say they either already have one or more big data initiatives in place or are just getting started with one. (% responding)
My organization is just getting started with a big data project: 55%My organization has one or more big data initiatives in place: 41%My organization has no big data initiative in place but has discussed implementing such a program in the foreseeable future: 4%
Base: Survey of 300 executives at midsize organizations worldwide Source: Competitive Edge Research, November 2013 Big Data Survey
Growing Adoption of Big Data96 percent of respondents say they either already have one or more big data initiatives in place or are just getting started with one. (% responding)
FIGURE 1
Myorganizationisjustgettingstartedwithabigdataproject55%
Myorganizationhasoneormorebigdatainitiativesinplace41%
Myorganizationhasnobigdatainitiativeinplacebuthasdiscussedimplementingsuchaprogramintheforeseeablefuture4%
Base:Surveyof300executivesatmidsizeorganizationsworldwideSource:CompetitiveEdgeResearchReports,November2013BigDataSurvey
FIGURE 2
Picking the Low Hanging FruitRespondents say big data is very important to meeting the following business goals in their organizations. (% responding)
Improvequalityofourproductsandservices 51%
Identifyandtakeadvantageofbusinessopportunities 51%
Improvequalityandspeedofdecision-making 50%
Obtainbetteranddeeperunderstandingofcustomerneeds 45%
Quicklyrespondtocompetitivethreatsorotherinputs 44%
Improveeffectivenessofourmarketingprograms 44%
Base:Surveyof300executivesatmidsizeorganizationsworldwideSource:CompetitiveEdgeResearchReports,November2013BigDataSurvey
Astheglobaleconomymovesfromdownturntocautiousgrowth,
moremidmarketfirmsarelookingtousebigdataanalysistogrowtheir
businessesratherthanjustfindwaystocutcosts.“Intheageofdata,
companiesofanysizecangainamarketadvantagewithbetterdata,”
saysWayneEckerson,directorofresearchandfounderofadvisory
firmEckersonGroup.“SMBs(smallandmidsizebusinesses)havethe
opportunitytoleapfrogtheirbiggercompetitors,butmorethanlikely
theyfearfallingbehindthebigguysevenfurther.”
Thosewhohavegonebeyondplanningtoimplementationofbigdata
projectsreportsatisfactioninanumberofareas,ledby:
uFasterdecision-makingandenhancedmarketing.
uImprovedproductandservicequality.
uAbetterunderstandingofcustomerneeds.
BIG DATA: Midmarket Companies See Early Success
COMPETITIVE EDGE RESEARCH REPORTS 6
Followingthelackofcooperationbetweenbusinessand
IT,respondentsreportatightclusteringofreasonsforwhy
bigdataprojectsfail.Somearetechnical,suchasalackof
requiredskillswithintheorganizationorinsufficientlycapable
datacentertools.Otherspointbacktowardtheneedfor
betterbusiness/ITcooperation,includingalackofuser
adoptionofdataanalysistools,incompleteorinaccurate
businessrequirements,andadisconnectbetweendata
analyticsandperformancemanagement.
Nearlytwooutofthreerespondentssaybigdataanalysishasindeed
improveddecision-makingintheirorganizations.Amongindustryvertical
markets,thoserespondentsinenergyandmanufacturingareparticularly
satisfiedwiththeirprojectresults.AccordingtoEricKavanagh,co-
founderofanalystfirmTheBloorGroup,thehighlevelofsatisfactionin
manufacturing“makesalotofsense.”Thatisbecausemanufacturers,
especiallythoseinthehigherranksoftheindustry,outfittheirproduction
equipmentwithinstrumentstohelpthempredictbreakdownsandavoid
expensiverepairsanddowntime.Thistacticgivesthemarichsourceof
real-timeproductiondatafordrivingbigdataprojects.
Althoughanalysisofsocialmediaandcustomersentimenthasgained
alotofattention,itranksrelativelylowamongmidmarketrespondents
whoareconsideringtheirprioritiesoverthenexttwoyears,meaning
apotentiallysignificantsourceofanalyticinsightstillremainslargely
untapped.Socialmedia,forexample,ranksbehindusingexternal
marketresearch,logisticsdataandgovernmentdatasourcesinterms
ofprojectimportance.Thatisnotsurprising,Kavanaghsays,because
socialmediaanalysis“isnotaneasythingtoachieve,andit’snoteasy
tofigureoutwhattodowithit.”Also,hesays,thetechnologyrequired
todoitright“isprettyexpensive.”
Inkeepingwiththepragmaticcustomer-facinggoals,customerservice/
CRM,sales,manufacturing,supplychain/logisticsandcorporate
financialsarethetypesofinternaldatarespondentsmostoftenciteas
“veryimportant”tobigdataprojects.
SUCCESSFACTORSAsinmanyotherareasofIT,collaborationbetweenITandthe
businessisawell-knownbestpracticeforbigdataprojects.Itisalso
oneofthebiggestprerequisitesofprojectsuccessdocumentedin
thissurvey.Havingtheproperskills,eitherin-houseorfromaservice
provider,isalsocitedasakeysuccessfactor.So,too,isperformance
management,which—again—speakstotheneedtotiebigdata
projectstomeasurableimprovements.
Theneedforskilledstaff—andforspecializedtoolsinareassuchasdata
cleansing—highlightsthechallengesorganizationsfaceinensuringthe
qualityofthedisparateproduct,customer,salesandotherdatabases
theyhavegatheredovertheyears.AlthoughtheITorganizationoften
takestheleadinimplementation,sales,customerserviceandmarketing
alsotakeprominentrolesindrivingbigdatainitiatives.
FIGURE 3
Most Valuable Big Data ToolsTools that provide insight into real-time data and help predict future events lead the list of what respondents regard as ex-tremely valuable now and in two years. (% responding)
Extremely valuable now Extremely valuable in two years
Real-timeprocessingofdataandanalytics 60%
60%
Predictiveanalytics 58%
58%
Datavisualizationtoconvertprocesseddataintoactionableinsights 56%
61%
Useofcloudcomputingtoprovideanytime,anywheredataandapplicationsaccessatlowercost 53%
56%
Dataaggregationthatspansmultipledatabases,includingbigdataplatformssuchasHadoop 50%
51%
Datadashboards(desktopself-servicedataintegration) 49%
57%
Base:Surveyof300executivesatmidsizeorganizationsworldwideSource:CompetitiveEdgeResearchReports,November2013BigDataSurvey
BIG DATA: Midmarket Companies See Early Success
COMPETITIVE EDGE RESEARCH REPORTS 7
Toolstoenabledatacleansingareexpectedtoseea
significantuptickintwoyears.Thisfocusondataqualityis,
ifanything,overdue,Kavanaghsays.“Thereisasenseof
realitydawninguponpeople[thatmostorganizations]have
someprettydirtydata.Youneedtohaveaverythorough
approachtoreconcileyourdifferentinformationsystems.”
Datadashboards,aswellasvisualizationtoolsthatenable
decision-makerstoseetrendsmoreeasily,arealso
expectedtoseeasignificantuptickintwoyears.
EARLYRESULTSAlthoughmanymidmarketcompaniesarejustnowgetting
startedwithbigdataprojects,theyareveryoptimisticabout
theresultstheyexpecttoachieveoverthenexttwoyears.
Inmanyareas,suchasimprovingthequalityandspeed
ofdecision-makingorquicklysensingandrespondingto
competitivethreats,respondentsexpectimprovementsof
about25percent.Howrealisticthesetargetsare,especially
inlightofthemoregradualimprovementsrealizedbytheir
enterprisepredecessors,remainstobeseen.
Nevertheless,theearlyevidenceforthevalueofbigdatato
midmarketcompaniesisencouraging.Oftherespondents
withoneormorebigdatasystemsalreadyinproduction,
anoverwhelming89percentsaytheirdecision-makinghas
improved(seeFigure4,“ABigBoostforDecision-Making,”
atleft).Only10percentfeeltheirinitiativehasnotyet
improveddecision-making.
Besideshelpingthemmakebetterdecisions,respondents
saybigdatasystemsareresponsibleforbiggains
inseveralotherstrategicareasastheymovefrom
developmenttoproduction(seeFigure5,“WhereBigData
IsSuccessful,”onpage8).Keytaskswherecompanies
reportsignificantimprovementsafterbigdatasystems
rolledoutincludeincreasesinproductquality,beingbetter
abletoidentifyandexploitbusinessopportunities,and
betterunderstandingtherequirementsofcustomers.
Ingeneral,thelargertheorganization,themoresatisfied
itiswithitsbigdataimplementation.Thereis,however,a
consistentgapof10to20pointsbetween“improvement”
and“considerableimprovement”inallareasaffectedbybig
data,asignthatcustomersstillhaveroomforimprovement.
DespiteAsia’sstrongshowinginmostbigdataareas,alackoftools
andinsufficientfundingaregreaterissuesinthisregionthaninother
geographies.Havingtoofewserversandnotenoughstoragerankas
biggerproblemsforgovernmentthanforotherverticals,perhapsdueto
particularlyseverebudgetpressures.
Giventhebusinessneedsforfasterandbetterdecision-making,itis
notsurprisingthatreal-timeprocessingandpredictiveanalyticsemerge
asthemostprizedtools(seeFigure3,“MostValuableBigDataTools,”
onpage6).Financialservices,whereamissedtradingopportunityora
delayinmakingatradecancostmillions,valuesreal-timeprocessing
thehighestofallindustries.
Examiningwhichtoolsshowthebiggestincreaseinvalueamongthose
respondentswithbigdatasystemsinproduction,ratherthanonlyin
development,indicateswhichprovidethemostvalueinactualuse.
Amongthoseareaggregationtoolsthatspanmultipledatabases,the
abilitytoprocessunstructureddata,datadashboardsandtheabilityto
searchmetadata.
FIGURE 4
Methodology pie/bar chartsRespondents by title – pie C-level or vice president: 50%Director or manager: 50%
Respondents by functional area – pie IT involvement: 67%Business: 33%
Figure 1 – pie chart[head] Growing Adoption of Big Data[deck] 96 percent of respondents say they either already have one or more big data initiatives in place or are just getting started with one. (% responding)
My organization is just getting started with a big data project: 55%My organization has one or more big data initiatives in place: 41%My organization has no big data initiative in place but has discussed implementing such a program in the foreseeable future: 4%
Base: Survey of 300 executives at midsize organizations worldwide Source: Competitive Edge Research, November 2013 Big Data Survey
Methodology pie/bar chartsRespondents by title – pie C-level or vice president: 50%Director or manager: 50%
Respondents by functional area – pie IT involvement: 67%Business: 33%
Figure 1 – pie chart[head] Growing Adoption of Big Data[deck] 96 percent of respondents say they either already have one or more big data initiatives in place or are just getting started with one. (% responding)
My organization is just getting started with a big data project: 55%My organization has one or more big data initiatives in place: 41%My organization has no big data initiative in place but has discussed implementing such a program in the foreseeable future: 4%
Base: Survey of 300 executives at midsize organizations worldwide Source: Competitive Edge Research, November 2013 Big Data Survey
Big data initiative in development but not yet in production
Big data system in production*
Improveddecision-making49%
Notyetimproveddecision-making42%
Notsure9%
Improveddecision-making89%
Notyetimproveddecision-making10%
Notsure2%
A Big Boost for Decision-MakingRespondents with big data systems already deployed report significant improvements in decision-making. (% responding)
*Totalexceeds100%duetoroundingBase:Surveyof300executivesatmidsizeorganizationsworldwideSource:CompetitiveEdgeResearchReports,November2013BigDataSurvey
BIG DATA: Midmarket Companies See Early Success
COMPETITIVE EDGE RESEARCH REPORTS 8
EmergentResearchAnalystSteveKingbelieveschancesaregood
thatmidsizefirmswillseesuchimprovementsastheirin-housestaffs
gainexperience.“Thisisareallearningcurveindustry,”hesays,andit
ishardformidsizefirmstoattractexperiencedbigdataanalysts.The
lessonstheiremployeeslearninearlybigdataprojectswillpayoffinthe
nextseveralyears,Kingpredicts.
OneearlyexamplewhereuserdataiskeyistheWashingtonPost
Co.,whichgathersusers’clickhistoryoncompanyWebsitestobuild
personalizedlistsoftopicalinterestsforregisteredvisitors.“Bigdataplays
aparticularlybigroleincollectingpastdataandthenbuildinganinterest
graph,”saysVijayRavindran,seniorvicepresidentandchiefdigitalofficer.1
AnotherexampleisThe Dallas Morning News,whichisbuildinga
dataanalyticsteamtobeginbetterprofilingcustomers.According
toPublisherandA.H.BeloCorp.CEOJimMoroney,hiscompanyis
usingallthedataitcantoimproveitsprocess—andlowerthecost—
ofcustomeracquisition,bothdigitallyandinprint.Butthere,Moroney
runsintoaproblemcommonamongmanymidmarketcompanies:
Thedataheneedsisallovertheplace.
“Wehavealloftheinstrumentsthatweneedtocreateasymphony,
butwedon’thaveaconductorwhohasthetoolstocreateblended
music,”Moroneysays.“Thequestionishowdowebringallofthe
datatogetherinawaythatwecananalyzeitagainstourcustomers
andgetafullerprofile.”2
CHALLENGESDespiteearlysuccessstories,significantchallengesremain.“Most
companiesaretryingtocollectmoredatathantheyhavepreviously,
primarilybecausetheyarestillfleshingouttheirdatawarehouses
andsocialmediastrategiesandcustomeracquisition,churn,and
retentionstrategiesbyaccessingandanalyzingallkindsofdata,”
saysanalystEckerson.
Butwhatgivesbigdatasomuchpromise—theabilitytosearch
massiveamountsofwidelyvariedinformationtofindhiddentrendsand
opportunities—isalsoitsbiggestchallenge(seeFigure6,“DataVolumes,
BudgetLimitationsTopBigDataChallenges,”onpage9).Dealingwith
a“widevarietyofnewdatatypesandstructures”isthemostoftencited
challenge,mentionedwithunusualconsistencyacrossgeographies,and
C-levelrespondentsarealmostasawareofit(35percent)asthoseatthe
director/managerlevel(40percent).Inanencouragingsignofbusiness
understandingofbigdatarequirements,asignificantnumberofbusiness
respondents(33percent)alsorecognizethisissue.
Thenextmostfrequentlymentionedhurdleis“sheervolume
ofdataslowsprocessing.”Thisresponsereinforcestheneed
forscalablehardwareandsoftwaretoaccommodatetruly
massivedatavolumes.
Budgetlimitsanddeterminingwhatdatatouseinmakingvarious
businessdecisionsarealsofrequentlycitedchallenges.These
responsespointtotheneedforclosebusiness/ITcooperationto
firstfund,andthentoproperlycarryout,bigdataprojects.The
properchoiceofdataisespeciallyimportantasbigdataexpands
beyondfamiliarstructureddata(suchasdatabases)totheuse
ofunstructureddata(suchasconsumercommentsonsocial
media,remotesensordataandWeblogs).Organizationsmust
alsochooseamongavarietyofinternaldatatypes,rangingfrom
salestoproductionandcustomerinformation,aswellasexternal
information,includingpointofsaledatafromretailers.
1,2.Depp,Michael.“LocalMediaPioneersTackleBigData.”NetNewsCheck,Oct.8,2013.http://goo.gl/9xSFGV.
Where Big Data Is SuccessfulRespondents grade themselves “very well” on strategic tasks significantly more often after big data is deployed. (% responding)
Big data in production Big data in development
Qualityandspeedofourdecision-making
50%
23%
Improveproductquality
49%
32%
Identifyandtakeadvantageofbusinessopportunities
47%
24%
Understandconstituentsentiment
47% 23%
Understandcustomerneeds
46%
27%
Predictfuturetrendsthatmayimpairachievementofbusinessgoals
44%
22%
Base:Surveyof300executivesatmidsizeorganizationsworldwideSource:CompetitiveEdgeResearchReports,November2013BigDataSurvey
FIGURE 5
BIG DATA: Midmarket Companies See Early Success
COMPETITIVE EDGE RESEARCH REPORTS 9
Giventhatmorethanhalfofmidmarketcompaniesare
justgettingstartedonbigdataprojects,successisstill
somewhathitandmiss.Onlyaquarterofrespondentsin
EMEAreportoneormorebigdataprojects,farbehindAsia
(55percent)andNorthAmerica(41percent).Differentdata
privacylawsamongEuropeancountriesandacultureofnot
sharingdataareamongthereasonslikelymakingitharder
topushbigdatainitiativesinthatregion.
Asia’sdominanceisnotsurprising.“It’samajor
manufacturingcenter”thatreliesheavilyonbigdata
analyticsforqualitycontrolandtopreventequipment
failuresandshutdowns,TheBloorGroup’sKavanaghsays.
LESSONSLEARNED/KEYOBSERVATIONSTheneedto“align”ITwiththebusinesssothatitdelivers
businessvalueisoneoftheoldestclichésintheindustry.
Whenitcomestobigdatainthemidmarket,theneedfor
suchclosecooperationhappenstobevery,verytrue.
Thetoptworeasonsrespondentsmentionforsuccess
andfailure—strongcooperationorcollaborationbetween
businessandIT,andastronglinkbetweendataanalytics
andperformancemanagement—arebothorganizational
infocusandspeaktotheneedforIT/businessalignment.
Recognizingtheimportanceofalinkbetweendata
analyticsandperformancemanagementisespecially
strongforthoserespondentswhohavebigdatasystems
inproduction,risingfrom17percentforthosewith
projectsindevelopmentto41percentforthosewithreal-
worldexperience(seeFigure7,“PathtoSuccess:Strong
TiesBetweenBusinessandIT,”onpage10).
Linkingbigdataresultstoperformancemanagement“isa
verytoughnuttocrack,”Kavanaghsays.Theproblemis
notthetools,buttheskillsandamountofworkrequired
“topopulatethosesystemswithrelevantdata,”hesays.
Thelessonlearnedbysurveyrespondents?Donotskimp
ontrainingorstaffcosts.AlackofrequiredITskillsisthe
second-rankingreasonforfailure,justashavingtheright
skillsisrespondents’thirdmostimportantsuccessfactor.
FIGURE 6
Data Volumes, Budget Limitations Top Big Data ChallengesWhen asked what are the biggest challenges facing their organizations in using big data and analytics tools to achieve their business goals, respondents mention the following issues. (% responding)
Widevarietyofnewdatatypesandstructures 40%
Sheervolumeofdataslowsprocessing 34%
Budgetlimitationstoimproveourdataanalysiscapabilities 32%
Determiningwhatdata(bothstructured/unstructuredandinternal/external)tousefordifferentbusinessdecisions
29%
Gettingbusinessunitstoshareinformationacrossorganizationalsilos 27%
Inaccuratedata 26%
Understandingwhereinthecompanybigdatainvestmentsshouldbefocused
25%
Notenoughtrainedstafftoanalyzethedata 25%
Analyticstoolsarelackingandmanypotentialusersdonothaveaccess 25%
Lackofeasy-to-use,cost-effectivedatacleansingtools 24%
Base:Surveyof300executivesatmidsizeorganizationsworldwideSource:CompetitiveEdgeResearchReports,November2013BigDataSurvey
Thisresponseisfollowedcloselybyanotherfamiliarchallenge,
“gettingbusinessunitstoshareinformationacrossorganizational
silos.”Citedbyaquarterofrespondents,“inaccuratedata”istypically
alegacyofyearsofinconsistentdatamanagementtechniquesby
diversebusinessunitswithinmanyorganizations.Thesesurvey
findingsresonateinthestrongdemandfordatacleansingtoolsto
ensurecompaniesdonotfallintothe“garbagein,garbageout”
syndromeofpoordecisionscausedbyfaultydata.
Yetanotherchallengethatallcompaniesfaceistheconstantdemand
formorestorage.Asmidmarketfirmsinvestmoreinbigdataprojects,
theyexpectdatavolumestoriseroughly60percent,toanaverageof
24petabytesintwoyears.Meanwhile,budgetsareexpectedtorise
fromcurrentratesofbetween$2millionand$5millionto$6millionin
thesameperiod.
BIG DATA: Midmarket Companies See Early Success
COMPETITIVE EDGE RESEARCH REPORTS 10
Notallverticalsandgeographiesare,however,realizing
thefullvalueofbigdataprojectsorinvestingasmuch.
Amongverticalindustries,manufacturingisfurthest
along.TheEMEAgeographieslagbehindinmany
measuresofprogressandsuccess.Someofthevery
attributesthatmakebigdatasouseful—theamount
andvarietyofdatatobecrunched—alsoposesome
ofthebiggestchallenges.Facedwithsuchchallenges,
midmarketorganizationsreportaneedfor,andan
appreciationof,toolsrangingfromreal-timeprocessingto
predictiveanalytics,datacleansing,anddatavisualization
anddashboards.
Thecombinationofnewtools,newcomputingmodelsand
atrackrecordofsuccessisleadingmidsizeorganizations
toinvestmoreinbigdatasolutions.Theirleadersare
findingthatcooperationbetweenbusinessandIT,theuse
ofperformancemanagementtoolsandtherightskillsare
centraltobigdatasuccess.Theupsidetoovercoming
challengessuchasawidevarietyandamassivevolume
ofdataiscontinuingimprovementinproducts,services
anddecision-making,aswellasmoreagileresponsesto
businesschallenges.pRecognizingtheimportancethat“businessrequirementsare
completeandaccurate”alsoshowsastrongjumpwithexperience,
from17percentto34percent.Ofcourse,successfullygathering,
andvalidating,suchbusinessrequirementsalsoimpliesstronglinks
betweenthebusinessandITsidesoftheorganization.
Thesurveyprovidesencouragingsignsofsharedresponsibilities
takingshapeamongthemanagementranks.Forexample,while76
percentofrespondentssayITismostresponsibleforimplementing
bigdataprojects,salesmanagementisnottoofarbehindat56
percent.ThefactthatsalesonlynarrowlytrailsITasarecipientof
bigdatafundingshowsanemphasisoncustomer-facingissues
that,outofnecessityifnothingelse,requiresclosecollaborationon
bigdataprojects.
SUMMARYANDCONCLUSIONAwareof,butundauntedbythechallenges,nearlyallmidsize
businessessurveyedforthisreportarepushingforwardwithbig
dataprojectimplementations.Focusedoncriticalbusinessproblems
suchasimprovingthequalityoftheirproductsandservicesand
theirdecision-making,respondentsreportsignificantnear-term
satisfactionandexpectevengreaterimprovementsoverthenext
twoyears.Asaresult,theseorganizationsarelookingtogrowtheir
budgetsandthesizeoftheirdatastores.
FIGURE 7
Path to Success: Strong Ties Between Business and ITRespondents say forging a strong bond between business and IT management is key for big data projects to succeed. (% responding)
StrongcooperationorcollaborationbetweenbusinessandIT
41%
Strongconnectionbetweendataanalyticsandperformancemanagementintheorganization 37%
Requiredskills,suchasdatascientists,arereadilyfoundintheorganization 33%
Businessrequirementsarecompleteandaccurate 32%
Serverandstoragecapacityarereadilyavailable 30%
Datacentertoolsarequitecapable 29%
Base:Surveyof300executivesatmidsizeorganizationsworldwideSource:CompetitiveEdgeResearchReports,November2013BigDataSurvey
SONSOR’S STATEMENT
Succeeding in the Data Economy: An Opportunity for AllTHE BUSINESS LANDSCAPE IS TRANSFORMING BEFORE OUR EYES. Spurred by the digitization of data and
information, the rapidproliferationofnewdata types,andthealways-advancingcapabilitiesofmodern
businessintelligenceanddataanalyticstechnologies,anewDataEconomyisrapidlytakingshape.With it
comestheopportunityfororganizationstogaindeepinsightintotheirbusinessprocessesandtheircustomers’
behaviors.Prosperousbusinesseswillbethosethatharnessandlearnfromdatatofacilitatebetterdecision-
makingandmaximizeprofitability.
Perhapsthemostcompelling
aspectoftoday’sData
Economyforcompaniesisits
applicabilitytoall.Thepotential
benefitsofadata-driven
approachtobusinessarenot
thesingulardomainof
enterpriseorganizations,and
theyneverhavebeen.Despite
whatitimplies,widespreaduse
oftheterm“bigdata”hasdone
nothingtochangethat.Whatithasdone,however,is
awakenorganizationsofallsizestothelongstandingand
fundamentalneedtobecomemoredatadrivenintheir
decision-making.AccordingtothelatestDellSoftware-
sponsoredsurveyfromCompetitiveEdgeResearch
Reports,thisisespeciallytrueofthoseinthemidmarket.
BEYONDTHEENTERPRISEIftherewereanydoubtsthatthechallengesand
opportunitiesbigdataaffordsareapplicablebeyondthe
enterprise,thisnewsurveygoesalongwaytoward
eliminatingthem.Thenumbersoutlinedinthepreceding
reportspeakforthemselves:80percentofmidmarket
organizationsagreethattheyneedtobetteranalyzetheir
dataandinformation,andastaggering96percenteither
havestartedorplantostartaformalizedbigdatainitiative
thisyear.Perhapsmoreimportantly,companiesacrossthe
boardindicatethataggressiveinvestmentsinadditionaldata
analysisinitiativesarestillforthcoming.AstheDataEconomy
continuestotakehold,thesuccessofthesefuture
investmentswillbecriticaltothelong-termprosperityand
viabilityofthecompaniesmakingthem.
ThatiswhereDellSoftwarecomesin.DellSoftwarehas
assembledaworld-classcollectionofdataandinformation
managementtechnologiesthathelpcompaniesofallsizes
manage,integrateandanalyzealloftheirdata.Dell
Software’sportfoliofeaturesarobustsetofsoftware
capabilities,includingdatabasemanagementand
optimization,applicationanddataintegration,masterdata
management,businessintelligence,advancedanalytics,
predictiveanalyticsandbigdataanalytics,allunderpinnedby
thecompany’smyriadstorage,serverandservicesofferings
andpartnerships.
FORMOREINFORMATIONInkeepingwithDellSoftware’sheritage,allofthecompany’s
informationmanagementsolutionsaredesignedwiththe
needsofthemidmarket—affordability,easeofuseand
scalability—firmlyinmind.Sowhereveryouaregoingon
yourjourneythroughtheDataEconomy,letDellSoftware
helpguideyouthere.
Tolearnmore,visithttp://software.dell.com/solutions/
information-management/.
Sponsoredby:
SPONSOR’SSTATEMENT
Matt Wolken, Vice President and GM, Information Management, Dell Software
January 2014Sponsored by:
COMPETITIVE EDGE R E S E A R C H R E P O RT S
Competitive Edge Research Reports
(CERR) is a division of Triangle
Publishing Services Co. Inc. CERR
provides objective, evidence-based
quantitative and qualitative research
about how information technologies
are used in business and society,
highlighting the benefits and
challenges of these technologies.