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 MunshiFounder - ERP [email protected]://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-ENDData Collection and

Integration Tools

ERP

CRM

Web

Files

Data Mart / Warehouse

Forecasting, Mining

PRESENTATION

FRONT-END

Analytics, Visualization, Dashboards, …

EXTRANET ACCESSTo

PARTNERS

ALERTS by email, 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



ConclusionQ&A

[email protected]

98250 11489

bi presentation designing and implementing business intelligence systems

Data & Analytics