Bi presentation Designing and Implementing Business Intelligence Systems

1Proprietary and Confidential © ERP India – Vispi Munshi – erp-india.org
Designing and Implementing Business Intelligence Systems
Vispi Munshi
Founder - ERP India
vmunshi@erp-india.com
http://www.erp-india.com

Today’s Agenda
- Introduction
- What is Business Intelligence
- BI System Design
- Data Integration
- Data Analytics, Dashboards, Alerts : Demo
- Business Metrics and Scorecards
- Data Mining, Predictive Analytics : Examples
- Big Data, Data Visualization
- Data Modeling
- Data Warehousing (Corporate Information Factory)
- Conclusion, Q&A

Ground Rules
- Please turn mobile ringer to SILENT MODE
- If you have to take a call, please leave the room and then start talking
- Questions can be asked any time
- Before asking question, raise your hand
- No talking with each other
- Disagreements allowed, but no disrespect
- Timeframe awareness
- Lets learn from each other and increase our knowledge

Introductions
- Full Name
- Where do you work (Company/Consultant/Student etc)
- Your job designation and responsibilities
- Any BI tool you have already used?
- Expectation from this workshop

What is business intelligence?
Business Intelligence is a term generally
used to identify a class of Information
System applications useful for supporting
operational, tactical and strategic decision
making of a organization.
BI deals with producing (and presenting)
Accurate, Relevant and Timely (ART)
INFORMATION from integrated data.
Not Accurate: Users loose faith in system
Not Relevant: Users ignore the system
Not Timely: Users find alternatives to the
system

MIS (Management Information Systems)
DSS (Decision Support Systems)
EIS (Executive Information Systems)
- Limited presentation (UI) capabilities
- Not well integrated (separate systems for each type)
- New BI systems combine MIS, DSS and EIS in one system
- Were used more at department level than at company/enterprise level
- Required more programming
Earlier BI Avatars – MIS, DSS, EIS

Benefits
- Sales Optimization
- Cost Reduction, Planning and Budgeting
- Inventory Optimization
- Purchase Optimization
- HR Optimization
- Production / Manufacturing Optimization
- Demand Forecasting
- Market Competitive Analysis and Customer Relationship Optimization
- Supply Chain (Sales and Distribution) Optimization
- Most Important is ART Information to support all levels of Decision Making
- Aim is to replace/reduce Excel based Analysis

BI System Design
BACK-END
Data Collection and
Integration Tools
ERP
CRM
Web
Files
Data Mart /
Warehouse
Forecasting,
Mining
PRESENTATION
FRONT-END
Analytics, Visualization,
Dashboards, …
EXTRANET ACCESS
To
PARTNERS
ALERTS
byemail,mobile,intranet

- Wide variety of source systems
- Technologies of source systems may be different
- Unified view of data dimensions and facts
- Performance of BI front-ends also improve
- Data cleaning is also sometimes required
Why is Data Integration Important?

- Based on multi-dimensional view of data (cubes)
- Pivot Table demo
- Features for drilling in and across data
- Slicing and dicing of data
- Graphics capabilities
- What-if Analysis
- Similar to Excel
- Exception reporting
- Data Analyst would use such tools and create Reports/Analysis/Graphs (objects)
- These objects would be supplied to Executives using Dashboards, Alerts etc.
Data Analytics (New Age DSS)

Multi-dimensional Data

High-end: Business Objects, OBIEE, Microstrategy, …
Open Source: http://en.wikipedia.org/wiki/Business_intelligence_tools
Data Analytics Tools in Market

Dashboards and Alerts (New age EIS)
- Dashboards are collection of reports/analysis/graphs
- Alerts are specific events that the system finds and sends a email/sms etc to
the executive
- Key Performance Indicators (KPI’s) can be set and linked to alerts
- Linking across objects provides executive with ability to go through the
generated information in a very intuitive manner

Business Metrics (KPI’s)
- Cost per lead generated (Marketing)
- Sales Lead closure rate (Sales)
- Average Employee CTC per function (HR)
- Service Call Response Time (Customer Service)
- Transport Cost per Unit (Supply Chain/Logistics)
- Handling Damage % (Inventory)
- Stress Test Failure % (Manufacturing, Quality)
- Key factors: Speed, Quality and Cost

Measuring Performance: Scorecard
Metric1 : 5678, 7%
Metric2 : 2500/-
Metric3 : 34%
Metric4 : 77%
Metric5 : 345, 1.3M
Metric6 : 45

Supply Chain KPI Scorecard
Speed Quality Cost
Suppliers
Inbound Transport
Warehouse
Outbound Transport
Distributors
Total

Data Mining Tools
- Also known as KDD (Knowledge Discovery in Databases)
- Involves use of large data sets
- Involves uses of Statistical Methods, Database Systems and Artificial Intelligence
- The objective is to discover patterns or knowledge from existing data
- Involves four step process: Data Preparation (classification), Hypothesis (user provided
guidance), Discovery (of knowledge) and Validation (of discovered knowledge against
hypothesis)
- High end tools: SPSS, SAS, …
- Open Source: http://en.wikipedia.org/wiki/Data_mining

Data Visualization
- Presents a intuitive graphic representation of the data and generated
knowledge
- The visualization is a embedded feature in most Analytics and Mining tools

Predictive Analytics
- Combines Data Analytics and Statistical techniques to provide a interactive
tool
- encompasses a variety of statistical techniques from modeling, machine
learning, and data mining that analyze current and historical facts to
make predictions about future, or otherwise unknown, events
- Recency, Frequency, Transaction Value, Demographics
- Classification, Clustering, Association Rules, Regression
- http://en.wikipedia.org/wiki/Predictive_analytics

Example 1: Focusing Marketing Campaigns
Past
Prospects/Leads
Conversions and
Failures
Demographic Data
Create Model
(Association)
Model (Decision
Tree)
Shortlisting
Prospect
List Shortlisted
Prospects
High
Medium
Low

Example 2 : Predicting which employees are likely to Perform Well
Past Employees Records
-Demographics
- Education, Location
- Skills, Trainings
- Duration, Grade etc
Past Performance
Records
Create Model
(Association Rules,
Clustering
Performance Model
(Decision Tree,
Neural Network)
Current Employees Records
-Demographics
- Education, Location
- Duration, Grade etc
Current Performance
Records
Prediction
Algorithm
(Classification)
Performance
Predictions
Analytics

Example 3: Market Basket Analysis using Association Rules
Example: When a customer buys a hammer, then 90% of the time they
will buy nails. HIGH SUPPORT
Example: When a customer buys a hanger set, then 3% of the time they
will buy nails. LOW SUPPORT
If SUPPORT between item pair is more than say 30% then the item pairs
can be placed on same shelf.
If SUPPORT between item pair is more than say 50% then they can be
packaged together.

Big Data
- Very large datasets (peta bytes), which cannot be handled by regular
database systems
- Applications such as bio-informatics, space research, large banks, large
ecommerce portals etc. require big data systems
- Data mining requires big data systems to generate meaningful knowledge
- Hadoop is a leader in this area

Data Modeling in BI
- Data in traditional Information Systems are modeled using Entity
Relationship and Normalization techniques
- Normalization helps reduce data redundancy
- BI Systems data are modeled using Dimensional Modeling techniques
- Dimensional Model uses Star Schema format
- Dimensional modeling allows de-normalization (data redundancy) for
performance improvement
- A hierarchy represents multiple levels in a dimension
- Dimensions may have multiple hierarchies
- Surrogate Data Keys are normally used

Data Warehousing
- Difference between Data Warehouse and Data Mart
- Types of Data Marts (functional, regional etc)
- Pros and Cons of Data Warehouse v/s Data Marts
- Updating Frequency
- Data Repository and Meta Data
- ETL (Extract Transform Load) tools
- MDM (Master Data Management) tools
- Data Cleaning/Quality tools
- Datawarehouse Frameworks
- Data Stewardship/Governance

Conclusion
Q&A
vmunshi@erp-india.com
98250 11489

Bi presentation Designing and Implementing Business Intelligence Systems

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