Business Analytics

Smarter Business Intelligence:
Advanced Analytics In Practice

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Today’s Presenters

Chris Murphy
Editor, InformationWeek

David Stodder
Analyst, Research and Writer
Perceptive Information Strategies

Agenda

Setting the stage
• Analytics defined
• Momentum
• The business imperative

Advanced Analytics Strategy

Analytics Defined

Analytics: Prediction, statistical analysis,
Optimization

BI: Reporting

Which sounds more exciting?

Analytics Momentum

• Democratize Analytics
Not just PhDs
• Embedded Analytics
Oracle, SAP, IBM, etc.
• Analytics Templates
• Real-Time Data
• Competitive Advantage?

Analytics: Business Imperative

• No. 1 among BI capabilities
• Real-time insight, predictive demand
• Real-Time Data

Poll Question 1

Q: We’re most interested in advanced analytics for:

•Analyzing customer data

•Finding new product opportunities

•Understanding our risk

•Predicting revenue

•Analyzing supply chains

•Other

Smarter Business Intelligence
Advanced Analytics In Practice

David Stodder
Contributing Editor, TechWeb
dstodder@gmail.com

Today’s Webcast Discussion
 Agility: business context for analytics
 Business intelligence and data warehouse key
trends and directions for analytics
 Analytics, information management and data
warehousing
 Making analytics affordable: templates and pre-
configured systems
 Analytics: predictive analytics and data mining
 Best practices and conclusion

12/7/2010 Copyright (c) David Stodder

To Be Agile, Be Aware: The Basis of
Competition Today
•Informed and proactive: Can anticipate and
react in a coordinated fashion
• Competing in real time: The strategic and
tactical edge only lasts so long
• Need to realize value from every customer,
partner
and process
• Optimize to reduce
latency, cut costs and
improve performance


Agility and Awareness: Information
Systems Support – or Thwart?
 Danger that traditional information systems can
thwart agility
 Standard, siloed reports can deliver incomplete
and inaccurate views of a multi-channel world
 Known problems, known solutions: What about
the unknown?
 Smart devices, smarter customers: Keeping pace
with rising intelligence


Business Intelligence and Data
Warehouse: Success Factors
How good is the information?
- Accuracy and quality; need it now, but need it right
- Comprehensiveness; all relevant sources included? In search
of the single view

What can I do with the information?
- Timely data is good, but do users understand it?
- Think dynamically: Continuous people and process
improvement

How can I profit from (or protect myself with) this
information?
- Operational intelligence is about business innovation
- Risk and regulatory compliance are major drivers


Data Scarcity: Not the Problem
 Many organizations already swimming in an
abundance of data
 Focus on gaining higher value from data
 Data is already big: “Big Data” focus on
dynamic behavior and velocity of information
(data at rest, in motion)
 Info management
challenge: integrating
access to internal and
external data sources

Business Analytics: Sharpening Focus
On Desired Business Outcome
• When outcome, and course are unknown: Use
information to iterate toward clarity
• Optimization: Use information to ensure no
steps are wasted
• Monitor and measure progress using BI and
performance
management
• Example: customer
loyalty tracking


Business Intelligence: Can it Take Us
Where We Want to Go?
 BI systems primarily provide
quantitative data and tools to
manipulate it for analysis and
to support decision-making
 Goal is to deliver
comprehensive views of
business states and directions
 Broadening out from
traditional base of analysts
and power users working
with limited data and updating


Business Intelligence Expectations
Most Important BI Features (3) Reason for Utilizing BI (Top 5)
90%
80%

80%
70%
70%
60%
60%

50% 50%

40% 40%

30%
30%

20%
20%
10%
10%
0%

0%
Fast data Ability to collect Ability to predict
exploration, query and analyze customer behavior,
and analysis operational data in risk or business
capabilities real time outcomes

- InformationWeek Analytics BI and Information Management Survey, September 2010

Business Intelligence Technology:
Pushing Past Limitations
 Historical data is vital, but can limit perspective
and “actionability” of data
 Focus on exceptions, not “data dumps”
 User requirements always change: Self-service
necessary to free users – and IT – of long, often
unsatisfying development
 Search can’t be a stranger: How most people
find information
 Collaboration: Decisions made by teams, not
individuals; embed in applications, services

“Smarter” BI: Collaborative,
Current, Easier to Use and Trusted
• Dashboards display BI/perf. mgmt info, quickly and
easily understood; drill down for anomalies
• Performance management starts to “template”
information around KPIs and metrics
• Collaborative potential: Integrating BI and
collaboration (e.g., IBM
Cognos 10 and Lotus
Connections)
• “Real time” – meaning
what’s important now
(could be real-time data)
IBM Cognos 10 dashboard example


But Can BI Do Analytics?
 Getting beyond reporting: Spreadsheets still most
widely used tool for analysis
 Financial analysis #1 reason for utilizing BI: Critical to
expand beyond accounting to support strategic and
operational analysis (including activity-based costing)
 Implementation goals for BI (IWK Survey):
◦ Monitor/share metrics: 72%
◦ Analyze customer data to increase sales: 56%
◦ Analyze customer data to retain customers: 53%


Polling Question #2
Q. What is your top priority with business
intelligence?
- Giving users self-service capabilities for
visualization and drill-down analysis
- Accessing real-time data
- Using BI to improve data quality and
consistency
- Enabling performance management KPIs and
metrics
- Financial reporting and analysis


Smarter BI: Nowhere Without Info
Management Infrastructure
 The “Hercules” of Business
Intelligence
 Taking advantage of hardware
advances (virtualization, very
large memory, new chip
designs)
 Information integration
 Enterprise data warehouse to
support BI; rules to facilitate
information governance
(security, HIPAA, etc.)
 Single view of the truth: Data
quality, profiling, discovery


Relevant, Timely and Reliable Data:
Challenges Remain


From Traditional to Next-
Generation Data Warehousing
• Serving small, internal user  Real-time analytics to improve
communities customer, partner service;
• Built around extraction, real-time event alerting
transformation and loading  ETL, MDM and federated
(ETL) information integration
• Historical analysis and  Support for performance
reporting management KPIs and
• Batch loading at off hours metrics, dashboards and
• Different systems for simple scorecards
& complex queries (e.g., ODS  Continuous updating
and DW)‫‏‬  Deployment of appliances in-
• Info delivery not memory analytic apps and
synchronized with processes pre-configured systems
 Little external data  “Cloud” data services


Critical Trends in Information
Management: Templates
 Rapid development: Using pre-built data
warehouse models, often specific to industry or
application
 Models and templates to improve consistency
of implementation
 Example: IBM Delivery Accelerators: e.g., retail-
specific template, dashboard and workbench
accelerators, data models, development tools,
processes, predictive modeling


Critical IM Trend: “Semantic”
Integration Managed Centrally
 Relieve BI/analytics tools and users of having to
define data types; reduce “what is a customer?”
chaos
 Develop coordinated, accurate and stable
business definitions and semantic meaning:
Master data management
 Managing ETL processes more effectively to
reduce cost and delay
 Improving data quality: BI fails without it!


Critical IM Trend: Pre-Configured
Systems and Appliances
 “Complex queries” – analytics – the most
frequent reason organizations purchasing
appliances and specialized databases (e.g.,
column-oriented)
 Pre-configured to speed deployment
 Tight integration
 Specialized for analytics
 Scalability

IBM Power7 Systems


In-Memory, In-Database Analytics:
Feeding the Need for Speed
 In-memory analytics: Bringing more power and
flexibility to the user’s workstation
 In-database analytics: Using the database
system to power analytics; e.g., SAS relationship
with IBM
 Real-time “trickle” data feeds and analytics;
“ELT” processing
 Embedding BI/analytics with processes


Polling Question #3
What is your top priority for information
management to support BI/analytics?
- Deploying BI/DW appliances and pre-configured
systems
- Enabling information integration layer (including
ETL, ELT, MDM) to support BI/analytics
- Taking advantage of better hardware (e.g.,
virtualization, blades, faster chips)
- Moving data warehouse systems to the cloud
(public or private infrastructure as a service)


Analytics: Improving Outcomes
“Simple” Analytics “Advanced” Analytics
- BI “what-if” queries - Optimization
- Accounting-oriented - Activity-based costing
financial analysis modeling and analysis
- Performance - Time series analysis
management metrics and forecasting
- Online analytical - Predictive analytics
processing (OLAP) - Whatever those
- Stuff nontechnical Ph.D.’s are doing
users can do


Advanced Analytics: Topping
InformationWeek BI “Wish List”
 3.8 on scale of 5 (“extremely interested”); one
third rated it 5
 Proactive objectives: anticipate demand to
adjust pricing, manufacturing forecasts and
supply chain planning
 Know what customers want before they ask for
it – or go to a competitor
 Fraud example: Isolate the bad so that good
claims are processed faster


Analytics: Case Example
 Infinity Property and Casualty: auto insurance
for drivers who represent higher than normal
risks and pay higher rates for comparable
coverage
 Objective of speeding claims process and
improve efficiency, while cutting fraud and
improving customer satisfaction
 “Right-tracking”: claims profiled up front and
sent to appropriate specialists based on claim
characteristics


Infinity Property & Casualty
Example, Continued
 Predictive traits in claims modeled using IBM SPSS
 Able to address concerns beyond just fraud
 Six months to develop models and rules to
integrate predictions into Infinity’s claim system
 Benefits: Used to take 40 days for claims to reach
specialists; now takes 48 hours
 Success rate in proving fraud now 87%; company
able to discontinue using third-party firm to
handle collections ($12 mill/yr)


Predictive Analytics Objectives
 Data mining: discovery of previously undetected
patterns and relationships in data
 Predictive analytics: applying historical patterns
to predict future outcomes
 Statistics (e.g., regression); AI (e.g., neural nets);
hybrid (e.g., decision trees); optimization (e.g.,
Monte Carlo simulation)

Acknowledgements to Eric Siegel, Prediction Impact


Data Mining: CRISP-DM Cycle

Source: www.crisp-dm.org


Customer Analytics: Priority Use of
Predictive Analytics
 How to increase margin, not just sheer number
of customers?
 What are the most effective metrics and
indicators of customer attrition and acquisition?
 Predictors are linked directly to business
strategy – to desired business outcomes
 Development of incentives programs aimed at
the right customers


Analytics: Challenges
 People and politics: Will they trust the results,
or go with the gut?
 Model development – combining predictors –
can be slow, trial-and-error process; models
must be kept up to date
 Structured data only half the story: Adding text
analytics and mining to apply quantitative and
linguistic analysis to words and sentiment


Proactive and Focused on Business
Outcomes: BI and Analytics Together
 Anticipate the future, plan how to act with
consistency rather than case-by-case
 BI: visualization, alerting and “simple” analytics –
backed by more advanced analytics – to make
information actionable
 Linking performance management to analytics
 Spreadsheets: Either making them more useful,
or replacing them with better tools


Making Analytics Affordable: Key
Trends to Watch
 Labor and expertise are huge costs: Using
templates for analytics and information
management
 Industry models for rapid development
 Pre-configured appliances and scalable systems
enable organizations to reduce time and cost
 Data services: Analytics in the cloud
 Embedding analytics in applications and
processes


Best Practices for Analytics
 Start with your BI platform: Where are users
bumping up against limits for understanding
“unknowns”?
 The information management layer is critical:
Analytics thrives on lots of data from multiple
sources for correlation and pattern analysis
 Don’t delay: Predictive analytics becoming more
mainstream; key to competitiveness


Best Practices: Going Forward
 Focus modeling on desired business outcomes
 Be patient with model development, and be
prepared to update models continuously;
evaluate industry models
 Customer analytics and fraud detection: where
depth of experience is greatest
 Social network analysis and “Big Data”: valuable
external sources, though in the realm of the
“experts” for analytics use


Questions and Answers

Chris Murphy
Editor, InformationWeek

David Stodder
Analyst, Research and Writer

Business Analytics

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