building a bi solution leveraging analytical reporting

Building a BI Solution Leveraging Analytical ReportingArunachalam T, IM Group, SETLabs, Infosys.

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Speaker Profile – Arunachalam

Certifications:

• Dr. Bill Inmon certified CIF Architect• TDWI Certified Business Intelligence Professional (CBIP)• DAMA Certified Data Management Professional (CDMP)• TOGAF 8 certified Enterprise Architect

Thought Leadership:

• BI Consulting Methodology, B-eye-Network• IM – Key for creating Business Value, TDAN• Convergence of BI and Search, Information-Management.com

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Part I :-Analytics Demystified.

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Analytics – The Buzz Word

report ajax ad-hoc OLAP metadataETL performance management MDM

real-time CEP dashboards metrics

KPIs intelligence prediction portlets

quality strategy analytics process charts visualization scorecard graphs

collaboration mashup frameworks

mining cloud integration architecture warehousing pervasive open-source

SaaS BI 2.0 analysis governance

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Analytics – An Enabler

Analytics is the science of analysis. -

Wikipedia.

Why?

How?

Where?

What?

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Analytics vs. Analysis

Analytics

Analysis

Analytics

Analysis

Analytics

Analysis ?

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Analytics vs. Analysis

Analysis

Tools Knowledge

Experience

InteractiveFeatures

Analytics

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Evolution of Analytics Capability

Descriptive

Perceptive

Predictive

Time

Valu

e

- Tools that provide rear-view analysis.

- Tools that provide hidden insights.

- Tools that provide futuristic view.

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Levels of Analytical Reporting

Strategic

Tactical

Operational

Modelers

Analysts

Knowledge Workers

Models/Workbenches

Dashboards/Visual Discovery

Spreadsheets/BI Reports

RolesTools

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Analytics – Maturity Model

•Drill Up and Down•Slice and Dice•Dashboards•Scorecards

•Mining•Hidden Patterns•Relationship Discovery

•Filtering•Sorting•Ranking

•Predictive Modeling•Forecasting•Scoring

Sophisticated Basic

TransformationalAdvanced

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Part II :-Development Techniques.

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Components of Analytics Application

AnalyticsApplication

Visual Discovery Interface

Analytic Functions

Application Server

Internal & External Sources

Source Mappings & Integration

Data Model & Analytic

Data Store

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Characteristics of Analytics Application

•Logically•Physically•Technically

Integrated

•Appropriate•Meaningful•Self Explanatory

Intuitive

•Domain Specific•LOB Specific•Context Specific

Institutionalized

•Hierarchical•Navigational

Interactive

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Development Approaches

Build •Custom•ADEs/ IDEs

Extend•Packaged Application Analytics

•E.g., Siebel Analytics

Hybrid•Packaged Analytic Applications

•E.g., SAS Platform

Analytics Application

Development Approaches

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Packaged Analytics

Courtesy: Wayne W. Eckerson, Beyond Reporting, TDWI Best Practices Report, Third Quarter 2009, p 17.

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Analytics is part of Larger System

Operational Systems

Legacy

Internal Systems

External Systems

Fir

ew

all

DataWarehouse

Metadata Management

Predictive Modeling

Extraction

Business Rules

Data Quality

Load

SAP

HR

Finance

Suppliers

Vendors

Projects

ETL Data Storage

Forecasting

Mining &Visual Discovery

Dashboards &Scorecards

Self-Service

Aggre

gati

on

Real-time

Data

base

Adapte

rs

Archival

OfflineStorage

Personalization

Reporting &Analysis

Administration

Data Re-Purport Information Delivery

Customer Analytics

MarketingAnalytics

Performance Analytics

Applica

tion

Serv

ers

Security

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Challenges

Following are some of the challenges faced by the businesses today :

• Creates a stove-piped system/ information silo• Requires rare skill sets – statistical analysts, miners and predictive

modelers• Lack of standard analytical tools/ approaches across organization• Data requirements of Operational, Tactical & Strategic Analytics are

complex• Resource Intensive – CPU, RAM, I/O• Data movement from operational & DW systems to ADM can hog

bandwidth.

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Best Practices

To overcome the challenges, enterprise can follow best practices including :

• Perform user defined analytical functions (UDFs) at DB level/ In DB Analytics

• Migrate from RDBMS to ADBMS – columnar database• Embrace 64 bit OS and MPP architecture• Adopt in-memory storage/ query processing• Leverage technological advancements such as clustering, grid

computing• Familiarize yourself with analytics frameworks such as MapReduce,

Hadoop• Plan for soft data/ text analytics.

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Q & A

Questions?