big data - 6 steps to success
TRANSCRIPT
© 2014 IBM Corporation
Big Data: 6 Steps to Success
© 2014 IBM Corporation@IBMExpOne | #IBMExpOne
Agenda
• The current landscape
• Big data success steps
• Case studies
• Questions
Sameer KhanSenior Product Marketing Manager& Digital LeaderIBM Customer Analytics@sameerkhan
© 2014 IBM Corporation@IBMExpOne | #IBMExpOne
Disruptive forces impact long standing business models across industries
““Data is the new oil.Data is the new oil.Data is just like crude.Data is just like crude.ItIt’s valuable, but if unrefined’s valuable, but if unrefinedit cannot really be used.” it cannot really be used.” – – Clive HumbyClive Humby
““We have an economy based on a We have an economy based on a resource that is not only renewable, resource that is not only renewable, but self-generating. Running out is but self-generating. Running out is not a problem, drowning in it is.”not a problem, drowning in it is.”–– John NaisbittJohn Naisbitt
Shift of power to the consumer
Pressure to do more with less
Proliferation of big data
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Lots of data, not enough answers
Only 26% of companies
have a well-developed strategy in place for improving customer experience.*
58% of companies
have limited or no understanding of which usability issues affect conversion*
91% of companies
have limited or no understanding of why people leave their site without converting*
* Econsultancy - 2013
The departmental analytic approaches of the past are insufficient for the needs of today
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The demand for big data solutions is real
The healthcare industry spends roughly $250 billion on fraud, per year. By 2016, this could grow to more than $400 billion a year.1
One rogue trader at a leading global financial services firm created $2 billion worth of losses, almost bankrupting the company.
6 billion global subscribers in the telco industry are demanding unique and personalized offerings that match their individual lifestyles.2
$93 billion in total sales is missed each year because retailers don’t have the right products in stock to meet customer demand.
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Shift In Algorithms: Generalized to Personalized
http://en.wikipedia.org/wiki/Medical_algorithm
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We all have…
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Big Data Steps to Success
Step 1: Identify the business problems
Step 2: Seek on the needed data
Step 3: Standardize your data
Step 4: Pick the right model
Step 5: Validate and test
Step 6: Execute and measure results
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Step 1: Identify the Business Problem
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Understanding the Business Challenges before Developing Big Data Solutions
External Content (Twitter, News Feeds...)
Internal Content (CRM, Warehouses,
ERP, ECM...)
“I am monitoring all angles – yet I can’t connect the dots.”
“I don’t know what I don’t know – where is my business exposed?”
“I can’t unlock the value in my data to drive economic value to my business.”
“Innovation is falling short as I am unable to see the full research picture.”
“I can’t find the right answers fast enough to support my customers.”
?
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Step 2: Seek only needed data
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Identify the Types of Data Sources and its Value
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Step 3: Standardize Your Data
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Leveraging data still faces many obstacles
Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute for Business Value study. Copyright © Massachusetts Institute of Technology
have a limited understanding of how to use analytics
38%
Self-service analytics and expectations to drive better data-driven decisions are rising
of the time is spend on data preparation80%
Making decisions rapidly is no longer a goal; it’s an imperative
find it difficult to get data24%
Access to requireddata sources is critical while maintaining governance standards
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Even a simple analytics project has multiple steps and people
Data Access
Data Preparation
Analysis
Validation
Collaboration
Reporting
Data Scientists and Statisticians
Business Users
ITBusiness Analysts
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Step 4: Pick the Right Model
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Improving the customer experience by better understanding behaviors drives almost half of all active big data efforts
Total respondents n = 1061
Big data objectives
Top functional objectives identified by organizations with active big data pilots or implementations. Responses have been weighted and aggregated.
Customer-centric outcomes
Operational optimization
Risk / financial management
New business model
Employee collaborationSource: Analytics: The real-world use of big data, a collaborative research study by the IBM Institute for Business Value and the Saïd Business School at the University of Oxford. © IBM
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Customer Centric Model Scenarios
Scenarios
1. Optimize By Channel Approach
2. Integrated Multi-Channel Approach
3. Master Data Management (MDM) Approach
4. Hybrid Approach
Source: CRM Ecosystem, Forrester
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Integrated Multichannel Approach Use Cases
General /Descriptive
Analytics
Predictive Analytics
Prescriptive Analytics
Similar BehaviorsSimilar Needs
Similar Web Navigation Challenges
Predicting Sales Volume
EverydayPrice
Market Trend
Inventory
Market Trend
Competitors
Seasonality Gift Cards
In-store Displays
Cross channel Products/Price
Recommendations
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MDM provides the enterprise backbone for an extended 360 degree view
Customer
Person
Prospect
Citizen
Member
Provider
Employee
CompanyTrading Partner
Organization Supplier
Distributor
Vendor
Office
Product
Service Financial Account
Agreement
Contract
Assets
Locations
Manufactured Goods
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Customer search:MDM draws in all related records: J Robertson, Janet Robertson, Jan Baker
Customer search:MDM draws in all related records: J Robertson, Janet Robertson, Jan Baker
Janet Robertson
MDM enables a complete purchase history, including Jan Baker’s records from before 2011
MDM enables a complete purchase history, including Jan Baker’s records from before 2011
Customer’s Products from MDM
Customer’s Products from MDM
Customer info from MDM
Customer info from MDM
Indexed 3rd party information related to customer
Indexed 3rd party information related to customer
Unstructured internal information related to customer
Unstructured internal information related to customer
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Step 5: Validate and Test
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Putting the Selected Model to Test
DiscoverDiscoverConnect securely to all data sources Provide unified search and navigationSurface relationships & themes
AssessAssessIdentify the value of the dataRecognize users of the data Establish context of data usage
CollaborateCollaborateAugment the data with user knowledgeCreate personalized views of the dataIdentify ongoing integration points
LeverageLeverage Build compelling applications using all of your data
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Integrated Big Data Infrastructure
Secure access to a broad range of enterprise systems
Integration leverages core components of the platform
Compelling applications incorporating all data types and sources
CM, RM, DM RDBMS Feeds Web 2.0 Email Web CRM, ERPFile Systems
ConnectorFramework
Data Explorer App Builder
BigInsights
Integ
ration
& G
overn
ance
UI / User
Streams WarehouseData Explorer
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IBM Watson Analytics
Unified analytics experience
Visual storytelling
Intelligent automation
Data access and
refinement
Report and dashboard
creation
Integrated social business
Guided analytic
discovery
25
Free Trial At
WatsonAnalytics.com
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Architecture Overview – Social Analytics Use Case
Visualization
Geospatial Analysis
Big Data Analytical services
Social Data Analytics
feed
(latitude, longitude); keywords
Trending Analytics (time series)
Notify of event analysis
Existing social media firehose
DiscoverDiscover AssessAssess CollaborateCollaborate LeverageLeverageApps
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Step 6: Execute and Measure Results
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Four value pillars represent ROI potential for big data exploration
Improve Productivity
Reduce Risk & Improve Compliance
Leverage Existing Assets Increase
RevenueEliminate data silos
Leverage existing research and knowledge
Eliminate/retire unused systems
Extract value from existing assets
Reduce training costs
Improve staff retention
Improve collaboration
Capture tribal knowledge
Eliminate redundant projects
Equip sales and service staff with current, accurate
info
Increase upsell and cross-sell
Reduce sales cycle
Increase customer lifetime
value
Recommendations
Reduce time to monitor and
comply
Push relevant regulatory
updates/alerts
Honor pricing, NDAs, etc.
Single version of the truth
Avoid penalties
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Big Data Success Stories
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Duke University Health System gets smarter for its patients Need
• Transform healthcare to provide patients with the capability to proactively manage their health; adapt best practices to rapidly determine which treatments and approaches work best – and which don’t
Benefits
• Increased patient engagement from zero to 30,000 users in less than three months
• HealthView is now used by more than 150,000 patients, roughly one third of Duke’s overall patient base
• Collected US $16 million in co-pays through the new portal
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Will you buy a car today? IBM SPSS Statistics helps Fiat identify the most likely customers and prospects.Need
• Determine the likelihood that future and returning customers would buy specific brands/models of Fiat cars, so dealers could optimize available marketing funds. Also, needed to better understand customer experience w/dealerships and repair facilities.
Benefits
• Improved customer response rate to marketing initiatives by 15-20 percent.
• Improved customer loyalty by 7 percent.• Supports continuous improvement of
dealerships and repair facilities. • Centralized analytical reporting and modeling
system enhances productivity and lowers costs.
• Efficiently works with large Oracle database containing history on 64 million customers.
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First Tennessee Bank: Analytics drives higher ROI from marketing programs Need
• An accountability framework that looked at overall marketing spending and the results that spending generated for the bank.
Benefits
• 600% overall return on its investment through more efficiently allocated marketing resources
• 3.1% increase in marketing response rate through more accurate targeting of offers to high-value customer segments
• 20% reduction in mailing costs and 17% reduction in printing costs due to the ability to target the most attractive segment for specific offers
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Next Steps and Resources
Resources:IBM Big Data Hubhttp://www.ibmbigdatahub.comWatsonAnalytics.comBigDataUniversity.com
Books / analyst papers
Schedule an IBM Big Data WorkshopFree of charge
Best practices
Industry use cases
Business uses
Business value assessment Thank You