The document discusses how most enterprises are investing in big data and real-time analytics initiatives to gain competitive advantages, but many IT organizations lack strategies to align these technologies with business goals. It describes how new data sources can provide richer customer insights and how real-time analytics can enable more timely operational decisions. However, organizations must evaluate whether their specific use cases require real-time data or would benefit more from traditional BI.

1. E-Guide
REAPING THE
BENEFITS OF BIG
DATA AND
REAL-TIME
ANALYTICS
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REAPING THE BENEFITS OF BIG DATA AND REAL-TIME ANALYTICS
he majority of enterprisesareeitherem-
barkingoninitiativesrelatedtobigdataor
intendtodosointhenearfuture;however,
most IT organizations do not have an ar-
ticulatedbigdatastrategythattiestechnologicalsolutionstobusi-
nessgoalsandobjectives.ThisE-Guideexploreshoworganizations
can embrace big data and real-time analytics to deliver immediate
improvements in business performance and gain a competitive
edge on rivals.
T

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BIG DATA ANALYTICS: ARE TODAY’S IT ORGANIZATIONS
EQUIPPED TO REAP THE BENEFITS?
George Philip
Market-leading enterprises have been investing heavily in analytical appli-
cations to derive competitive differentiation, market growth, revenue maxi-
mization, cost optimization, and to address aspects like regulatory reporting
requirements. They make thousands of decisions every day at different levels
oftheorganizationwithdifferingdegreesofcomplexity,impact,frequencyand
predictability.
Typicaloperationaldecisionsarehighlystructured,repeatableandmadeat
a higher frequency based on well-documented decision logic and a structured
decision-making process. Due to this, the degree of automation of these pro-
cesses tends to be high with the exception of human collaboration in decision
making that is too complex to automate. Operational decisions individually
havelowimpactontheoverallorganization;however,theycanhavesignificant
impact if viewed collectively.
Ontheotherhand,strategicdecisionstendtobehighlyinteractivebasedon

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non-routine, unstructured and ad hoc analysis. Though higher-level decision
processescouldbedefinedforsomeofthedecisionsofarepeatablenature,the
degree of automation possible is low given the high degree of collaboration re-
quiredamongthedecisionstakeholders.Thesestrategicdecisionstendtohave
a low degree of repeatability and frequency, yet individually they have a high
impact on the organization. Though these decisions tend to be made at differ-
entlevelswithintheorganization,theyarehighlyinterconnected.Identifying,
capturing, analyzing and managing these interconnections tend to be critical
for overall decision-making effectiveness.
It is because of these interconnections that organizations are beginning
to look beyond standard business intelligence (BI) and reporting to take bet-
ter advantage of emerging data sources, optimize business processes, gain
deeperinsights,addresscomplexity,reduceregulatoryrisksandevaluatenew
business scenarios in order to leverage competitive differentiation. While BI
implementations are typically meant for tracking and managing business key
performance indicators (KPIs), advanced analytics implementations provide
more far-reaching organizational benefits. Typically, operational decision
making is supported by descriptive and diagnostic analytics that tend to focus
onthepastandpresentwhilestrategicdecisionmakingneedstobesupported

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with predictive and prescriptive analytics approaches that can look into the
futuristic business aspects.
NEW DATA SOURCES
Traditionally, organizational transaction data with limited attributes tended
to be the mainstay of business intelligence and advanced analytics. Today
there are new, burgeoning sources of data available that can enrich limited
organizational data with additional attributes that can describe an entity in
a much more detailed manner. For example, it is now possible to enrich the
information available about a customer from within the enterprise transac-
tion systems with additional customer attributes that have been gleaned from
external social engineering interactions, marketing interactions, customer
service interactions, complaints interactions, maintenance interactions and
othersimilarsourcestogivearicherpictureofthecustomerandhis/herinter-
action with the enterprise. This enhanced understanding about the customer
typicallyimprovesbehavioralmodelingcapabilities,resultinginbetterpredic-
tion of organizational interventions and likely outcomes. This data increases
the organizational ability to attract and retain more profitable customers who
will improve both top line and bottom line growth.

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SHORTCOMINGS OF TRADITIONAL APPROACHES
These new sources of data tend to be of much higher volume, velocity,
variety and complexity and include unstructured text, weblogs, voice data,
machinedataandothervarietyofnon-traditionaldatastructures.Traditional
approaches for tackling these new big data sources tend to be inefficient, and
organizations are gradually being forced into the adoption of relatively new
data management approaches like Hadoop, HDFS, MapReduce, stream ana-
lytics and related architectural constructs that can programmatically analyze
these data structures for faster decision making.
Major business opportunities for big data are not limited to decision mak-
ingorincrementalimprovements.Bigdatacantransformthebusinessanddis-
rupt the overall industry through asking and answering highly computational
questions that were never possible before. This has the potential for radically
changing existing business processes, and opening the door for the introduc-
tionofnewprocesses,newproductsandservicesthatareaimedattheprover-
bial “market of one” customer segments that were previously out of reach.

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BIG DATA STRATEGY
While line of business is highly enthused about the deeper business benefits
foreseen from these additional information sources, most IT organizations
today are facing a challenge in adopting big data concepts to augment their
existing information management paradigm. Though most enterprises are ei-
ther embarking on initiatives related to big data or intend to do so in the near
future, most IT organizations do not have an articulated big data strategy that
ties technological solutions to business goals and objectives.
Many big data initiatives are originating from within business units, and
this increases the pressure on IT organizations to get adequately prepared to
support them. Low-cost big data processing solutions are permitting deploy-
ment of such solutions at a much faster pace than previous technological ad-
vances in this area. Many IT organizations also tend to primarily look at big
datafromavolumeperspective.Volumebyitselfistransitoryinnaturewithad-
vancing hardware architectures able to consume them at a faster pace; hence,
morefocusneedstobeplacedonaddressingthevariety,velocityandcomplex-
ityaspects.Businessbenefitstendtobehigherwhileaddressingtheseaspects.
In this scenario, it is the right time for IT organizations to take the lead in
developing an enterprise-wide big data strategy. One of the first steps in this

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journey is the evaluation and selection of all architectural and infrastructural
componentstoensureabilitytoadequatelysupportanticipatedgrowthinvol-
ume,velocity,varietyandcomplexityaspectsofdata.Datagovernance,policies
andcontrolsneedtobeaugmentedtoensureallbigdatasourcesandusecases
are adequately managed to prevent emerging business and regulatory risks.
Because big data implementations require specific data management, quality,
preparation and analytics skills, specific focus needs to be on obtaining these
skills at the right time.
Once a carefully planned enterprise-wide big data strategy is in place, the
floodgatesofnewadvancedanalyticscapabilitiescanbeopenedtoleveragethe
enhanced business value derived from these new data sources.
GEORGE IS currently Vice President & Global Practice Head of Analytics & Information Management
at Mindtree. George has decades of hands-on experience in architecting, managing and delivering
analytics, business intelligence, information management, information governance, data warehousing,
performance management, master data management, CDI, data quality and metadata management
applications for large global clients across industries including telecom, banking, financial services,
insurance, retail, technology, government, education, media, entertainment, manufacturing, electronics,
and services. He is an active speaker at international conferences such as the Gartner BI Summit and
plays an advisory role in various industry forums. George holds a post graduate diploma in business
administration and a bachelor of technology in computer science.

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REAL-TIME ANALYTICS BRINGS BI DATA DIRECTLY INTO
BUSINESS OPERATIONS
Beth Stackpole
Looking to generate immediate improvements in business performance and
gainacompetitiveedgeonrivals,companiesincreasinglyaretryingtotakead-
vantageofbusinessintelligenceandanalyticstoolsnotjusttogarnerstrategic
insights,butalsotodriveoperationaldecisionmakinginrealornearrealtime.
Real-time analytics -- or operational intelligence, as many prefer to call it
--hasbeenheraldedasthenextlogicalprogressionforBIdeployments.Never-
theless,consultantssayreal-timetools arestillintheearlystagesoftechnology
maturityandthatimplementationsarefarmoreprevalentinlargercompanies
with sizeable IT budgets and deep benches of BI and analytics professionals.
"Simple kinds of real-time or near-real-time analytics are within the bud-
get of virtually every company," said Roy Schulte, an analyst at Gartner Inc.
in Stamford, Conn. As an example, he cited a business dashboard that gets
refreshed every five minutes to monitor the volume of incoming customer
callsreceivedatacorporatecontactcenter.But,Schulteadded,morepowerful

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real-time analysis systems "can require hundreds of thousands of dollars in
softwarelicensefeesandseveraltimesthatinstaffcoststodevelopanddeploy."
Schulte said real-time BI technology gives business users or automated
systems immediate access to operational data. The goal is to enable analytics
applications to be applied to business processes that have limited time win-
dows or require rapid reactions to events and changing conditions.
SALES OFFERS ON THE SPOT WITH REAL-TIME TOOLS
In that scenario, a real-time BI system might help a customer service repre-
sentative make a cross-selling or up-selling offer to a customer based on his
recent activity on the company's website or something said during the phone
call. "Any large company has a number of applications in which their business
processes would be smarter and more effective if they were using real-time
analytics," Schulte contended.
In fact, with the cost of processing power coming down and more tools be-
coming available, it's going to become easier to make a business case for real-
timeanalytics beyondthetraditionalusecasesincallcentersandfinancialand
tradingapplications,saidJohnMyers,seniorBIanddatawarehousinganalyst
atEnterpriseManagementAssociatesInc.,aresearchandconsultingcompany

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in Boulder, Colo.
"With the barrier to entry lowering, it starts to open up a whole new realm
of things people can do," he said. For example, a retailer could push real-time
data analytics to the cash register level, according to Myers. In such an appli-
cation,theanalyticssystemcouldserveupasimplealertthatwoulddirectthe
cashiertomakespecificofferstocustomersbasedonwhattheywerebuyingas
well as their previous purchases and risk scores assessing their payment and
credit histories.
Being able to make BI-driven decisions as events unfold can be especially
important when customer satisfaction is at stake. "Companies used to be able
to look at [key performance indicators] every three months to see how they
weredoing,"saidJohnCrupi,chieftechnologyofficeratJackBeCorp.,avendor
of real-timeanalytics softwareinChevyChase,Md."Now,ifthere'saproblem
impactingcustomersandyoudon'tfindoutaboutitforafewweeks,youwon't
have your customers for too long."
REAL-TIME NOT ALWAYS THE RIGHT BI FIT
Evenwithallitspotential, real-timebusinessintelligence andanalyticsiscer-
tainlynotamatchforeverycompanyorevery BIbusinesscase.Organizations

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need to think through whether providing end users access to the most current
data will actually change business outcomes and results -- or just flood them
with more data to no great effect.
"Noteveryinstancerequiresreal-timedata,"saidLyndsayWise,president
andfounderofWiseAnalytics,aToronto-basedconsultancythatfocusesonBI
and data visualization deployments in midmarket companies. "It depends not
on how quickly you need the data, but what are you going to do with the data
and where are the action points."
An online retailer might benefit from using real-time analytics to help it
betterservicecustomersormanageproductinventoriesmoreeffectively,Wise
said. But giving business executives updated views of sales data in real or near
real time isn't so useful if that information doesn't precipitate any immediate
actionsontheirpart.Toproperlyassesstheneedforreal-timecapabilities,she
added, organizations need to look closely "at the cause and effect" of proposed
deployments.
BETH STACKPOLE is a freelance writer who has been covering the intersection of technology and business
for more than 25 years for a variety of publications and websites.

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