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#IBMInsight Session presentation "Transforming your Enterprise to Get Value from BigData and Analytics: How to Get Started". Transforming Your Enterprise to Get Value from Big Data and Analytics: How to Get Started The Journey, The Value Analytics Drives, Analytics Leadership and Governance, Analytics Case Studies, Best Practices for Getting Started More at ibm.biz/BdEPRs
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IBM IOD 2012 10/24/2014
Drury Design Dynamics 1
Transforming your Enterprise to GetValue from Big Data and Analytics:How to Get StartedSession # 6371
Douglas Dow & Emily Plachy
October 27, 2014
© 2014 IBM Corporation
Transforming Your Enterprise to Get Value from Big Dataand Analytics: How to Get Started
• The Journey
• The Value Analytics Drives
• Analytics Leadership and Governance
• Analytics Case Studies
• Best Practices for Getting Started
• Conclusions
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The Journey
IBM’s Transformation Journey has been years in themaking with the analytics portion starting in 2004
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Sharing & partnering
Making things smarter
Globally integrating
• Deliver a signature IBM client experience with an engaged workforce• Build a Smarter enterprise with data, cloud and systems of engagement• Make IBM essential to clients, partners, investors and communities
Transformationdesigned to:
Early years analytics applied to physical assets, i.e. manufacturing, supply chainThen analytics applied to non physical processes / functional side i.e. salesNow expansion more broadly used across the enterprise
Analytics
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IBM’s Analytics Transformation is focused on businessoutcomes
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“Analytics will form asilver thread that weavesthrough the future ofeverything we do.” Ginni
Rometty, Chairman and CEO,IBM Corporation
Fundamental Principles
• Pragmatic approach
• Focus on business outcomes
• Analytics is a way of doing business
Basic Building Blocks
• IBM Institute for Business Value Papers
• Great base for transformation withvalue services structure
• Motivated leadership to make IBMsmarter and essential
The Value Analytics Drives
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First, what do we mean by analytics?
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IDC – Independent Financial Impact Studies
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“The median ROI for the
projects that incorporated
predictive technologies was
145%, compared with a
median ROI of 89% for those
projects that did not.”Source: IDC, “Predictive Analytics and ROI:
Lessons from IDC’s Financial Impact Study”
Update: 2011 study shows ROI for predictive analytics at 250%!
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Understanding how to create value from data has been thefocus of IBM’s analytics studies for 5 years.
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Analytics:The new path to value
Operationalizinganalytics in
sophisticatedorganizations
Analytics:The widening
divide
Mastering analyticcompetencies
Analytics:The real world use
of big data
Fundamentalsof big data
Analytics:A blueprint for value
Extracting valuefrom data and
analytics
2010 2011 2012 2013
The intelligent enterpriseand
Breaking away with BAO
2009
Defining analyticsas a strategic
asset
MIT Sloan Management Review & IBM Institute of BusinessValue teamed up in 2010
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IBM Institute for Business Value
+
•Surveyed 3,000 executives, managers andanalysts plus extensive interviews
•Respondents represent more than 30industries in 108 countries
•Interviews with IBM and MIT thought leaders
•Analysis by IBM and MIT Sloan ManagementReview team
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The use of analytics correlates to performance
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Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute of Business Value study. Copyright © MassachusettsInstitute of Technology 2010.
Top Performers are morelikely to use an analyticapproach over intuition*
Organizations that lead inanalytics outperform thosewho are just beginning toadopt analytics
*within business processes
5.4x3x
Organizational obstacles, not data or financial concernsare holding back adoption
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Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute of Business Value study. Copyright © Massachusetts Institute of Technology 2010.
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Where analytics are performed within an organization is not a singlefixed location, but rather a combination of complementaryarrangements.
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Transformed(21%)
Experienced(46%)
Aspirational(33%)
87%
71%
47%
IT department
At point-of-need
LOB analyticunits
Location analyticsperformed
Centralizedanalytic units
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Frequency ofanalytics use
Progression of analytics location as demand grows
Source: Analytics: The New Path to Value, a jointMIT Sloan Management Review and IBMInstitute of Business Value study. © 2010Massachusetts Institute of Technology.
Nearly two out of three respondents reports realizing acompetitive advantage from information and analytics.
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Total respondents n = 11442010 and 2011 datasets © Massachusetts Institute of Technology
Realizing a competitive advantage
Respondents were asked “To what extent does the use of information (including bigdata) and analytics create a competitive advantage for your organization in yourindustry or market.” Respondent percentages shown are for those who rated theextent a [4 ] or [5 Significant extent]. The same question has been asked each year.
Competitive advantageenabler
A majority of respondentsreported analytics and information(including big data) creates acompetitive advantage within theirmarket or industry
Represents a 70% increasesince 2010
Organizations already active inbig data activities were 15%more likely to report acompetitive advantage
A higher-than-averagepercentage of respondents inLatin America, India/SE Asia andANZ reported realizing acompetitive advantage
63%
58%
37%
2012
2011
2010
70%increase
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Analytics Leadership andGovernance
IBM’s Analytics Transformation Governance Model
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AnalyticsPractices
AnalyticsCommunities
EnterpriseInformation
Management
CIO Office
GlobalIntegratedEnterprise
Shared Services
Big Data &AnalyticsUniversity
BusinessPerformance
Services
IBM Research
Development
Groups
EnterpriseTransformation
Initiatives
Line of BusinessAnalytic Groups
Network of Analytic Communities
DataStrategy
Infrastructure
InitialDeployment
Channel
Education Portal
Apply and solve business problems
Deepmathematicalskills
Product andSolution offerings
ScalingDeployment
Addressing businesschallenges in LOBs
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Business Analytics Transformation
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Mission
Drive the Widespread Use of Analytics across IBM to Elevate BusinessPerformance
Long Term BAT Strategy
Transform for Growth using Analytics via 4 E's:
Evangelize – promote the value gained by using analytics
Educate – provide training in consuming and doing analytics
Enable – assist business leaders in solving their businesschallenges with analytics
Empower – establish business structures to support peopleimplementing analytics
We leverage a rich set of products internally for BusinessAnalytics
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Bring analytics to strategicdecisions
Collaboration Management
DashboardingReporting
VisualizationFinancial Data
Searching
Smart ApplianceStream ComputingParallelizationEntity AnalyticsCloud Computing
Highly scalable & big datacomputing The Customer-centricity
revolutionSocial Network AnalysisSocial Media Analysis
Voice of Customer
IBM AnalyticsEcosystem
Deployment todecision makers
Complex Event Processing, Business Process ManagementBusiness Rules & Events (ILOG)
Multi-channel deployment & management
GBS
Core AnalyticsTechnologies
DataStage
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Analytics Case Studies –Emily Plachy
Human Resources: Tailored analytics-drivenrecommendations reduces attrition of high-valueemployees
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Solution components:
- IBM® SPSS Modeler- IBM® Cognos BI- IBM® ILOG CPLEX
$85M
325% ROI
Estimated net benefit throughreduced attrition in IBM’s growthmarket employee population
For 2012-2013 investment
Business problem: Retaining high-value employees.
Solution: Identify drivers of attrition and risk level for every
employee. Leverage the model to create a customized retention
plan for each high value employee.
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Finance: Identify and mitigate acquisition risk byleveraging data and analytics
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Business problem: Acquisitions ‘synergies’ arechallenging to quantify and realize and can significantlyalter performance & financial expectations.
Solution: Use acquisition data and advanced analyticsmodels to create tailored risk profiles for eachcontemplated deal and address throughout theacquisition lifecycle.
80+ Acquisitionsbenefited from headlights intoexecution risks to affect bothdeal pricing and integration plan
Solution components:
- IBM® SPSS Modeler, Statistics- IBM® Cognos BI- IBM® WebSphere
End-to-End RiskManagement across theacquisition portfolioStreamlined tracking andreporting to identify systemic riskand address challenges
Sales: Boost sales effectiveness by applying advancedanalytics to align resources to the market opportunity.
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Business problem: Deploying sellers for maximum revenue growthby account.
Solution:• Advanced analytical models predicting customer profit contribution
based on historic revenue growth and opportunity for everyaccount.
• Recommendation engine provides Increase / Decrease / Maintainresource shift recommendations at a client level.
$300Mestimated additional revenueduring 2013 due to sales forceproductivity increase
3000% ROIfor 2013, based on a yearlyongoing investment of $10M
Solution components:
- IBM® SPSS Modeler, Statistics- IBM® Cognos BI- IBM® Netezza
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Services: Develop new business using analytics and socialmedia.
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Business problem: Developing new business in an existingmarket, finding new customers, finding new products andservices for existing customers.
Solution: IBM Global Technology Services and IBMResearch developed the Long-Term Signings Platform.
• Analyzed over 30 data sources• Discovered associations between clients and products
Millions of dollars in increasedrevenue and millions of dollars incost savings
$M
Solution components:
- IBM® SPSS Modeler, Text Analytics
Best Practices for GettingStarted
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Developing and deploying an analytics solution consists ofthree elements: data, math/analytics, business process.
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DescriptiveGet in touch with reality, a single
source of the truth, visibility
PredictiveUnderstand the most likely future
scenario, and its business implications
PrescriptiveCollaborate for maximum business
value, informed by advanced analytics
CognitiveHighly automated optimization
solutions that get smarter over time
What happened?
What will happen?
What should we do about it?
Variety (many forms of data)
Big Data = All Data
Veracity (data in doubt)
Volume (data at rest)
Velocity (data in motion)
How do we optimize a dynamic, BigData environment?
Developing an analytics blueprint is the first step toconverting data and analytic insights into results.
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Source: Analytics: A blueprint for value – Converting big data and analytics into results, IBM Institute for Business Value © 2013 IBM
StrategyInstill a sense of purpose
Is there a common agenda for analytics among leaders? Are your investments aligned to value delivery? Does the funding structure support cross-silo initiatives?
TechnologyArchitect for the future
Are data and analytics skills nurtured within your organization? Are data management practices strong enough to instill confidence? Is the analytic infrastructure architected for today’s challenges?
OrganizationEnable the organization to act
Are data and analytics part of your decision making processes? Can your define the impact from analytics investments? Is the level of trust sufficient to rely on others for data and analytics?
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“New path to value” – a five-point approach tooperationalize analytics
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Recommendation 1:Focus on the biggestand highest valueopportunities
Recommendation 4:Keep existing
capabilities whileadding new ones
Recommendation 5:Use an informationagenda to plan for
the future
Recommendation 2:Within eachopportunity, start withquestions, not data
Source: Analytics: The New Path to Value, a joint MIT SloanManagement Review and IBM Institute of Business Value study.Copyright © Massachusetts Institute of Technology 2010.
Recommendation 3:Embed insights to drive
actions and deliver value
Best Practice Approach to Analytics Projects
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• Perfect data is a longjourney and you can’tafford to wait
• Organize & cleansedata incrementally sothat projects can start
Incremental
• To gain business valuestart on analyticprojects right away
• Have businessanalysts work with dataanalysts
Collaborative• Put a stake in the
ground to progress theproject and get results
• Review and makeimprovements
Iterative
Business AcumenData Analysts
Deliver fastThink big Start small
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Several themes have emerged from our analyticssolutions.
Relationships inferred from data today may not be present in datacollected tomorrow.
You don’t have to understand analytics technology to derive value fromit.
Fast, cheap processors and cheap storage make analysis on big datapossible.
Doing things fast is almost always better than doing things perfectly.
Using analytics leads to better auditability and accountability.
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Nine levers: Capabilities that enable and enhance big data& analytics value creation
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Source: “Analytics: A blueprint for value – Converting big data and analytics into results,” IBM Institute for BusinessValue © 2013 IBM
Source of Value Measurement Platform
Enable
Basis for bigdata andanalytics
Culture Data Trust
Drive
Needed torealize value
Actions and decisions thatgenerate value
Evaluating impact onbusiness outcomes
Integrating capabilitiesdelivered by hardware and
software
Availability and use of dataand analytics
Data management practices Organizational confidence
Sponsorship Funding Expertise
Amplify
Boostsvalue
creationExecutive support and
involvementFinancial rigor in analytics
funding process
Development and access toskills and capabilities
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Several dynamics are underway that are shaping thefuture for big data and analytics
• Growth of data – 2.5 billion gigabytes generated everyday
• Unstructured data – 80% of big data growth isunstructured (social media, video, audio, images, datafrom sensors).
• Cognitive computing – Just when we need it, the third eraof computing, cognitive, offers the promise of allowing usto rapidly explore big data and uncover insights.
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‒Think Ahead
‒Tell a Story
‒Understand Your Business
‒Get Better Data
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Conclusions
Conclusions
• The use of analytics correlates to organizational performance.
• Developing and deploying analytics solutions consists of threeelements: Data / Analytics / Business Processes
• You can operationalize analytics using a 5-point approach.
• Successful analytics deployments incrementally cleanse data,have collaborative teams, and deliver iteratively.
• The most competitive companies will move from raw big data toinsight-driven actions with speed; and cognitive computing willhelp do this.
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