This document discusses analytics and data mining techniques. It begins by outlining common measures used in analytics like time, proportions, size, and financials. It then discusses the universal language of cause and effect in analytics and how analytics finds relationships between causes and effects. The document outlines various sources of data, fundamental concepts in analytics like increased computing power and data volume, and common tools. It provides examples of universal applications of analytics across marketing, customer service, and other functions. The document charts the evolution of analytics from basic surveys to more advanced techniques leveraging social media, text, and sensors. It outlines the CRISP-DM process for analytics projects and career paths in analytics. Finally, it discusses various data mining techniques categorized as

1. Analytics : Understanding Patterns
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2. The Universal Language of Measures
• Time
• Proportions
• Size
• Financials
• Productivity
• Loyalty
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3. The Universal Language of Cause & Effect
• Process & Scale
• Habits & Health
• Technology & Efficiency
• Consumer Understanding &
Pricing
• Risk & Return
• Action & Outcome
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4. Possibilities of no pattern unlikely ........
Cause
Effect
Analytics is finding the relationship/ path of Cause to Effect
Effect = fn ( Data , Math , Common Sense)
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5. Sources of Data
• Survey’s
• Transaction Systems
• Free Text
• Digital Images
• Sensors
• Voice
• GPS
• ..... Upto the Imagination
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6. Fundamental Concepts
• Exponential Increase in
Computing Power
• Explosion of Digitized Data
• Open Source Data Mining &
Statistical Software
• Democratization of
Multivariate Analytics ( N-
Dimensional Plane )
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7. Tools For Data Mining & Predictive Modeling
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8. Universal Applications
• Direct Marketing
• Scoring Applications
• Forecasting
• Identifying critical influencing drivers
• Marketing
• Customer Service
• HR
• Across all functions....
Regression - Deriving Drivers Cluster - Classifying & Grouping
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9. Evolution of Analytics - The Answers
Survey Analytics - Can I ask you?
Transaction Data Analytics -You buy so you are
Social Media Analytics - You are the company you keep
Sentiment Analytics - You are what you feel
Thought Analytics - You are how you think
Pre 80’s
2005
2008
2010
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10. Evolution of Analytics - The Data & Techniques
Questionnaire / Cross Tabs /Univariate /Bivariate
Transaction Databases /Multivariate
Web Logs / Text Mining/Multivariate
Text /Voice/Imaging / Artificial Intelligence
Sensors / Artificial Intelligence
Pre 80’s
2005
2008
2010
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11. Executing Analytics Projects
CRoss Industry Standard Process for Data Mining (CRISP-DM) for developing and deploying analytics
solutions
Problem
Objectives
Data
Study
Data
Preparation
Analysis &
Modeling Evaluation
Reporting &
Deployment
Determine
Problem
objectives
Assess
situation
Determine
data mining
goals
Produce
project plan
Collect initial
data
Describe data
Explore data
Verify data
quality
Select data
Clean data
Construct data
Integrate data
Format data
Select analysis /
modeling
technique
Generate test
design
Build model
Assess model
Evaluate results
Review process
Determine next
steps
Plan deployment
Plan monitoring
and maintenance
Produce final
report
Review project
Domain expert
finalizes
objectives with
client
Analysts use data
mining software to
integrate and
understand
relevant data
Complex data
cleansing
algorithms used to
collate all relevant
data into an
analytical data
mart.
Statisticians select
techniques) based on
hypothesis. Business
consultants and
analysts collaborate to
unearth key drivers and
forecast key business
indicators.
The solutions are
evaluated and
validated by the
business users and
practice head.
The solutions are
integrated with the
relevant business
processes.
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12. Career Options
Captives Core
3rd Party ITES Boutique
Offshoring Geo Independent
Internal Client
External Client
Products
Analytics Division of Leading
Companies
Small Companies Focused on Niche
Vertical & Function
BI / AnalyticsVerticals of most ITES
firms
BFSI/ Retail Captives
Product Companies Like SAS/IBM- SPSS/ STATISTICA etc
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13. Techniques of Data Mining - 1
Technique Category Description
Summarizing data Data Understanding
Frequency counts of categorical
variables . Central Tendency Measures for
Numeric
Standardizing data Data cleansing / Normalization
Format standardization , missing value
treatments
Merging / Appending Data Preparation
Integrating multiple databases to create
single database (datamart buildup )
Variable Creation / Integration Data Preparation
CreatingVariables which the users
understand and derive meaning
Cross Tabulation Reporting
High level reporting of 2*2 or more
variables
Cubes Reporting
Multi level and real time drill downs of all
relevant variables
Macro’s Automation
Automatic generations of all standard
reports / cubes.
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14. Techniques of Data Mining - 2
Technique Category Description
Measures of Central
Tendency
Data Understanding Enables identifying the outliers and the central values
Hypothesis Testing /
Correlations
Analysis
Identification of whether basic assumptions related to
the data are valid or not . Used for simple analysis
Regressions/ Factor Analysis /
ARIMA
Predictive Modeling
Identifying the factors on which the key situation at
hand is dependent on. Forecasting Key Indicators
Clustering Models Grouping / Segmentation
Bucketing records into mutually homogenous &
collectively heterogenous groups
Text Algorithms Grouping
Preparing unstructured data to be in a form for
advanced statistical modeling
Artificial Intelligence/Neural
Networks
Inference and Judgement
Analytics
Building automated engines which analyze information
in a ‘human’ simulated manner
Decision Trees/Chaid /SEM Grouping / Segmentation Root Cause Analysis , Path / Dependency Analysis
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15. Thank You
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