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Analytics : Understanding Patterns Tuesday 10 July 2012
Analytics : Understanding Patterns
Tuesday 10 July 2012
The Universal Language of Measures
Time
Proportions
Size
Financials
Productivity
Loyalty
Tuesday 10 July 2012
The Universal Language of Cause & Effect
Process & Scale
Habits & Health
Technology & Efficiency
Consumer Understanding & Pricing
Risk & Return
Action & Outcome
Tuesday 10 July 2012
Possibilities of no pattern unlikely ........
Cause
Effect
Analytics is finding the relationship/ path of Cause to Effect
Effect = fn ( Data , Math , Common Sense)
Tuesday 10 July 2012
Sources of Data
Surveys
Transaction Systems
Free Text
Digital Images
Sensors
Voice
GPS
..... Upto the Imagination
Tuesday 10 July 2012
Fundamental Concepts
Exponential Increase in Computing Power
Explosion of Digitized Data Open Source Data Mining &
Statistical Software
Democratization of Multivariate Analytics ( N- Dimensional Plane )
Tuesday 10 July 2012
Tools For Data Mining & Predictive Modeling
Tuesday 10 July 2012
Universal Applications
Direct Marketing
Scoring Applications
Forecasting
Identifying critical influencing drivers
Marketing
Customer Service
HR
Across all functions....
Regression - Deriving Drivers Cluster - Classifying & Grouping
Tuesday 10 July 2012
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 80s
2005
2008
2010
Tuesday 10 July 2012
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 80s
2005
2008
2010
Tuesday 10 July 2012
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.
Tuesday 10 July 2012
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 / Analytics Verticals of most ITES firms
BFSI/ Retail Captives
Product Companies Like SAS/IBM- SPSS/ STATISTICA etc
Tuesday 10 July 2012
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 Creating Variables which the users understand and derive meaning
Cross Tabulation ReportingHigh level reporting of 2*2 or more variables
Cubes ReportingMulti level and real time drill downs of all relevant variables
Macros Automation Automatic generations of all standard reports / cubes.
Tuesday 10 July 2012
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 / SegmentationBucketing 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
Tuesday 10 July 2012
Thank You
Tuesday 10 July 2012
