Download - Big Data Analytics Key Note
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2012 IBM Corporation
Big Data AnalyticsImproving the way we live and work
Deepak AdvaniVP, Business Analytics Products & Solutions
August 16, 2012
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Imagine if you could
track disease outbreaks
across country borders in real
time?
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Imagine if you could
catch money laundering before it
happens?
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Imagine if you could
apply social relationships of
customers to prevent churn?
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Imagine if you could
identify at-risk students
before they drop out of
school?
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Respondents who say analyticscreates a competitive advantage
57%increase
Organizations achieving
a competitive advantage withanalytics are
2.2xmore likely tosubstantially outperform theirindustry peers
Ratio of respondents who indicated analytics creates a competitiveadvantage to those who indicated it did not and the likelihood theyalso indicated their organizations was substantially outperformingtheir competitive peers. The ratio was 2.0 to 1 in 2010.
Analytically sophisticated companies outperform their competition
2010
58%2011
37%
Analytics has evolved from business initiativeto business imperative
Source: The New Intelligent Enterprise, a joint MIT Sloan Management Reviewand IBM Institute of Business Value analytics researchpartnership. Copyright Massachusetts Institute of Technology 2011
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Why Business Analytics MatterThe Need for Analytics is Pervasive Across Business and Industry
The healthcare industryspends $250 - $300 billion on healthcarefraud, per year. In the US alone this is a $650 million per dayproblem.1
One rogue trader at a leading global financial servicesfirm created
$2 billion worth of losses, almost bankrupting the company.
5 billion global subscribers in the telco industryare demandingunique and personalized offerings that match their individuallifestyles.2
$93 billion in total sales is missed each year because retailersdont have the right products in stock to meet customer demand.
Source: 1.Harvard, Harvard Business Review, April 2010.
2,IBM Institute for Business Value, The Global CFO Study, 2010.
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The need for progress is clear
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projected growth inworldwide energyconsumption between 2008
and 2035.1
53% proportion of worldwide CO2emissions created by powergeneration, the largest human-made
source.3
170 billion kilowatt-hours wasted each yearby consumers due to insufficientpower usage information.2
1 U.S. Energy Information Administration, International Energy Outlook 2011, September 2011; http://205.254.135.24/forecasts/ieo/pdf/0484(2011).pdf
2 Ontario Energy Board, Ontario Energy Board Smart Price Pilot Report, (Prepared by IBM Global Business Services and eMeter Strategic Consulting for the Ontario Energy Board), July 2007.
3 The Climate Group, SMART 2020: Enabling the low carbon economy in the information age, 2008 ;http://www.smart2020.org/_assets/files/02_Smart2020Report.pdf
http://www.smart2020.org/_assets/files/02_Smart2020Report.pdfhttp://www.smart2020.org/_assets/files/02_Smart2020Report.pdf -
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Three key trends are driving this movement:
The emergence ofBig Data
The shift of power tothe consumer
Pressure to do morewith less
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Meter readings per day in atypical smart meter project
Volume Velocity Variety
500million
Big Data presents a huge new opportunity for Energy & Utilitycompanies if they can harness it
Ingest 3,000 times more
meter readings to betterunderstand and managethe electric distribution
grid
Analyze weather data to
place a wind turbine toimprove its performancewhile extending its useful
life
Analyze all types of asset
performance information tooptimize maintenance
activities and extent usefullife of the assets
From instrumented smart grid,weather forecasts, documents80%
datagrowth
Weather modeling data foroptimizing siting of wind turbines4
petabytes
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Applications for Big Data Analytics
Homeland Security
FinanceSmarter Healthcare Multi-channel sales
Telecom
Manufacturing
Traffic Control
Trading Analytics Fraud and Risk
Log Analysis
Search Quality
Retail: Churn, NBO
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Predictive AnalyticsPolice Use Analytics to Reduce CrimeVideo
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Challenges facing utilities and energy providers
Consumers demanding adifferent model
Generation of vastquantities of data
Increasingly high power costs
Environmental concerns
Fluctuating, volatiledemand
Inadequate infrastructure
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Energy and utilities organizations are working toward a smarterenergy value chain to promote responsibility and efficiency
Transformation of
the utility network
Transform customeroperations
Improve generation
performance
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Improve GenerationPerformance
- Align organization and processes to deliver theright products and solutions to each customer
- Enable more efficient customer sales andservice interactions
- Minimize fraud
Transform the UtilityNetwork
- Improve generation efficiency and reduceoperating expenses
- Maximize power generation uptime throughpredictive maintenance
- Reduce outages and downtime- Optimize maintenance and operational activities- Time of use pricing flexibility- Comply with information privacy and retention
regulations
Smarter Analytics for Energy and UtilitiesIndustry Imperative Smarter Analytics Outcome Where Weve Done It
Transform CustomerOperations
Reduced energyconsumption by ananticipated 20%; controlcosts using real timemonitoring
Decreased productioncosts by 1-2% resultingin a savings of 50,000- 100,000 per day
Reduced frequencyand duration of poweroutages
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Word spread virally of the victory with Twitter reaching
11.7M, 30,121 blog mentions, and 15,025 forum posts
On February 14, 2011, IBM Watson changed history
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Putting the pieces together at point of impactcan be life changing
Symptoms
UTI
Diabetes
Influenza
Hypokalemia
Renal Failure
no abdominal painno back painno coughno diarrhea
(Thyroid Autoimmune)
Esophagitis
pravastatinAlendronate
levothyroxinehydroxychloroquine
Diagnosis Models
frequent UTI
cutaneous lupus
hyperlipidemiaosteoporosis
hypothyroidism
Confidencedifficulty swallowing
dizziness
anorexia
feverdry mouth
thirst
frequent urination
Family
History
Graves Disease
Oral cancerBladder cancerHemochromatosisPurpura
Patient
History
Medication
s
Findings
supine 120/80 mm HG
urine dipstick:leukocyte esterase
urine culture: E. Coli
heart rate: 88 bpm
SymptomsFamily
History
Patient HistoryMedicationsFindings
Extract Symptoms from record Use paraphrasings mined from text to handlealternate phrasings and variants
Perform broad search for possible diagnoses Score Confidencein each diagnosis based on
evidence so far
Identify negative Symptoms Reason with mined relations to explain away
symptoms (thirst is consistent w/ UTI)
Extract Family History Use Medical Taxonomies to generalize medical
conditions to the granularity used by the models
Extract Patient History Extract Medications Use database of drug side-effects Together, multiple diagnoses may best explain
symptoms Extract Findings: Confirms that UTI was present
Most Confident Diagnosis: DiabetesMost Confident Diagnosis: UTIMost Confident Diagnosis: EsophagitisMost Confident Diagnosis: Influenza
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Real-time projectionsof hurricane path
Dynamically updatedrisk assessment
for assets inprojected path
Correlate combined risk andimpending weather threats to
optimize inventory anddetermine supply chain
recommendations
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Top Solution Areas
Retain Customers
Understand what makesyour customers leave,and what makes themstay
Keep your bestcustomers happy
Take action to preventthem from leaving
Acquire Customers
Understand who yourbest customers are
Connect with them in theright ways
Take the best actionmaximize what you sellto them
Grow Customers
Understand the best mixof things needed by yourcustomers & channels
Maximize the revenuereceived from yourcustomers & channels
Take the best action
every time to interact
PredictiveCustomerAnalytics Manage Operations
Maximize the usage ofyour assets
Make sure your assetsare in the right place atthe right time
Identify the impact ofinvestment in various
areas of assets
MaintainInfrastructure
Understand whatcauses failure in yourassets
Maximize uptime ofassets
Reduce costs of upkeep
Secure Operations
Improve the security ofyour assets
Identify unanticipatedattack patterns onassets
Quickly respond with the
best action whensecurity is compromised
PredictiveOperationalAnalytics Detect Suspicious
Behavior
Identify fraudulentpatterns
Reduce false positives
Identity collusive andfraudulent merchants andemployees
Identify unanticipatedtransaction patterns
Mitigate Risk
Identify leaks
Increase compliance
Leverage insights in criticalbusiness functions
Prevent Fraud
Take action in real time toprevent abuse
Reduce Claims HandlingTime
Alert clients of transactionfraud
PredictiveThreat &RiskAnalytics
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Top Solution Areas
Retain Customers
Understand what makesyour customers leave,and what makes themstay
Keep your bestcustomers happy
Take action to preventthem from leaving
Acquire Customers
Understand who yourbest customers are
Connect with them in theright ways
Take the best actionmaximize what you sellto them
Grow Customers
Understand the best mixof things needed by yourcustomers & channels
Maximize the revenuereceived from yourcustomers & channels
Take the best action
every time to interact
PredictiveCustomerAnalytics Manage Operations
Maximize the usage ofyour assets
Make sure your assetsare in the right place atthe right time
Identify the impact ofinvestment in various
areas of assets
MaintainInfrastructure
Understand whatcauses failure in yourassets
Maximize uptime ofassets
Reduce costs of upkeep
Secure Operations
Improve the security ofyour assets
Identify unanticipatedattack patterns onassets
Quickly respond with the
best action whensecurity is compromised
PredictiveOperationalAnalytics Detect Suspicious
Behavior
Identify fraudulentpatterns
Reduce false positives
Identity collusive andfraudulent merchants andemployees
Identify unanticipatedtransaction patterns
Mitigate Risk
Identify leaks
Increase compliance
Leverage insights incritical business functions
Prevent Fraud
Take action in real time toprevent abuse
Reduce Claims HandlingTime
Alert clients of transactionfraud
PredictiveThreat &RiskAnalytics
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Grow Customers
Understand the best mix ofthings needed by yourcustomers & channels
Maximize the revenue receivedfrom your customers &channels
Take the best action every timeto interact
Improved 1:1 Marketing
Individual customer profiles using over 30data points from ATM, phone, Web, andbranch interactions
Decreased direct marketing costs by 18% Increase in overall ROI: 600%
Maintain InfrastructureUnderstand what causes failurein your assets
Maximize uptime of assets
Reduce costs of upkeep
Predictive Maintenance
Observation of the entire car fleets repairperformance in real time
High data complexity: analyzing 20Ksignals via 10K DTCs
Reduction of 25% Repeat Repair
Prevent FraudTake action in real timeto prevent abuse
Reduce ClaimsHandling Time
Alert clients oftransaction fraud
U.S. Border Patrol Resource Optimization
Deploying mobile analytics in the hands ofborder officers
Indentify high-risk cars crossing the borderprior to search
Optimize the deployment of border officers
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Making the planet smarter
Smarter Healthcare
Yorkshire Water moved from reactive to proactivemaintenance
supplying around 1.24 billion liters of drinking water each dayUplift in predictability in identifying areas at risk from floodingReducing incidents of other causes f looding and improving
customer service and satisfaction
Improved proactive blockage detection rates by 25-30%
Smarter Education
Marwell Wildlife, a conservation charity, helped secure afuture for an endangered species
Determining the main threats facing the Grevys zebra in the wildUnderstanding critical ecosystems interactionsInvestigating the relationship between nomadic herdsmen andGrevys zebra
Working with communities to implement conservation measuresthat address threats and protect key resources
Smarter Water
Baruch College focused on student successes
Increased applications to its business school by 7.1 %, whenother schools were seeing significant decreases
Achieved a 21 % annual increase in transfer students
Decreased dropouts significantly by using predictive analytics toimprove the placement of freshmen in introductory classes
Sequoia Hospital reached record survival rates
Reduced mortality rate for cardiac surgery to 1.7% in 2008 from3.8% in 2003
Best record nationally for survival from valve replacement overthe past six consecutive years
Reduced doctors diagnostics requests from several weeks to
near real-time speed
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83%
Others
95%
Standouts
Getting closer to customers
14%moreGetting closer to customer
People skills
Insight and intelligence
Enterprise model changes
Risk management
Industry model changes
Revenue model changes
88%
81%
76%
57%
55%
54%
51%
Dimensions to focus on over the next 5 years
Our customers wantpersonalization of services andproducts. It is all about the marketof one.
Tony TylerCEO, Cathay Pacific Airways, Hong Kong
To surprise customers requiresunexpected ideas throughinteractions of people with diverseperspectives.
Shukuo IshikawaPresident and CEO, Representative Director,
NAMCO BANDAI Holdings, Inc. Japan
Source: IBMs 2010 Global CEO Study Capitalizing on Complexity (1,541 CEOs, 60 nations, 33 industries)
Getting closer to the customer is the TOP priority
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ResearchProduct
Purchase Product
Get CustomerService
AdvocateProduct
Marketing
Sales
Support/Services
Feedback Management
Social Intelligence
The customer experience has changed dramatically
UseProduct
Up/Cross Sold
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The Baseline of Customer Analytics Applied Mathematics
Statistics: Ask a Question of a Sample to Generalize to the Universe
A Sample of Data A Universe of Things That Generate Data
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Taking Customer Analytics to the Next Level with Predictive Analytics
Predict the Behavior of the Next Case with a Model
A Universe of Data
Attributes: Married, 2 kids Lives in Suburbs of Chicago Owns two Cars 47 years old
Drinks Scotch
A Predictive Model
Predicted Attributes: Upper Middle Income Owns a minivan Likes Van Halen Likes Johnnie Walker Black
Works long hours Commutes
Predicted Behavior Wants to Buy a Sports car! Buys Car Washes! Buys Chardonnay
Vacations where its warm
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Bringing these pieces together Analytics is a Lifecycle
Capture
Data
Collection
Act
DeploymentTechnologies
Predict
Platform
Pre-built Content
StatisticsTextMining DataMining
BusinessRules
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Getting a More Accurate Picture: All the Data Matters
Behavioral data
- Orders- Transactions- Payment history- Usage history
Descriptive data
- Attributes- Characteristics- Self-declared info- (Geo)demographics
Attitudinal data- Opinions- Preferences- Needs & Desires
- Survey results- Social Network Data
Interaction data- E-Mail / chat transcripts- Call center notes- Web Click-streams
- In person dialogues
Traditional
High-value, dynamic
- source of competitive differentiation
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Data at the Heart of Predictive Analytics
Behavioral data
- Orders- Transactions- Payment history- Usage history
Descriptive data
- Attributes- Characteristics- Self-declared info- (Geo)demographics
Attitudinal data- Opinions- Preferences- Needs & Desires
Interaction data- E-Mail / chat transcripts- Call center notes- Web Click-streams
- In person dialogues
Traditional
High-value, dynamic
- source of competitive differentiation
Who? What?
Why?How?
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Micro-targeting: the move beyond 1 on 1 is accelerating. However, consumersare moving from opt-out to opt-in, regaining control over their personal data
Consumers instrumentation and mobility create additional opportunities (time &spatial data dimensions) for more accurate targeting (context-aware decisions right place & right time) through a plethora of touch points through digitalmedia
Social media has dramatically changed the purchase influence cycle exponentially replicating word of mouth, to the power of 10,000 Customeropinions accessible and free to millions and in a matter of seconds
Integrated analytics: promoting holistic contextual decisions integrating supply-
chain data, personal demand data and risk management Brand equity is struggling to remain a guiding light through the global and
multiplicity of access; differentiation is realized through a customer experiencedriving loyalty
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Future Trends