roadblocks crumbling: midmarket companies see early...

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

COPYRIGHT AND DISCLAIMER NOTESCompetitiveEdgeResearchReports,asubsidiaryofTrianglePublishingServicesCo.,doesnotmakeanyguaranteesorwarrantiesastotheaccuracyorcompletenessofthisreport.CompetitiveEdgeResearchReportsshallnotbeliabletotheuseroranyoneelseforanyinaccuracy,errororomission,regardlessofcause,orforanydamagesresultingtherefrom.InnoeventwillCompetitiveEdgeResearchReportsnorothercompaniesorthird-partylicensorsbeliableforanyindirect,specialorconsequentialdamages,includingbutnotlimitedtolosttime,lostmoney,lostprofitsorlostgoodwill,whetherincontract,tort,strictliabilityorotherwise,andwhetherornotsuchdamagesareforeseenorunforeseenwithrespecttoanyuseofthisdocument.Thisdocument,oranyportionthereof,maynotbereproduced,transmitted,introducedintoaretrievalsystemordistributedwithoutthewrittenconsentofCompetitiveEdgeResearchReports.

©Copyright2014CompetitiveEdgeResearchReports.Allrightsreserved.

Thenamesofactualcompaniesandproductsmentionedhereinmaybethetrademarksoftheirrespectiveowners.

ELECTRONIC VERSION AVAILABLEToseeoruseanelectroniccopyofthisdocumentinPDFformat,pleasevisitthefollowingWebsite:http://www.triangle-publishing.com/competitive_edge_research.html

http://www.triangle-publishing.com/competitive_edge_research.html

http://www.triangle-publishing.com/competitive_edge_research.html


Executive Summary


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







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%




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).






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%


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%


Astheglobaleconomymovesfromdownturntocautiousgrowth,

moremidmarketfirmsarelookingtousebigdataanalysistogrowtheir

businessesratherthanjustfindwaystocutcosts.“Intheageofdata,

companiesofanysizecangainamarketadvantagewithbetterdata,”

saysWayneEckerson,directorofresearchandfounderofadvisory

firmEckersonGroup.“SMBs(smallandmidsizebusinesses)havethe

opportunitytoleapfrogtheirbiggercompetitors,butmorethanlikely

theyfearfallingbehindthebigguysevenfurther.”

Thosewhohavegonebeyondplanningtoimplementationofbigdata

projectsreportsatisfactioninanumberofareas,ledby:

uFasterdecision-makingandenhancedmarketing.

uImprovedproductandservicequality.

uAbetterunderstandingofcustomerneeds.



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%




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











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



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%


FIGURE 5

http://goo.gl/9xSFGV



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%


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.



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%


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.

roadblocks crumbling: midmarket companies see early...

Documents