building a bi solution leveraging analytical reporting
DESCRIPTION
Building a BI Solution Leveraging Analytical Reporting. Arunachalam T, IM Group, SETLabs, Infosys. Speaker Profile – Arunachalam. Certifications: Dr. Bill Inmon certified CIF Architect TDWI Certified Business Intelligence Professional (CBIP) DAMA Certified Data Management Professional (CDMP) - PowerPoint PPT PresentationTRANSCRIPT
Building a BI Solution Leveraging Analytical ReportingArunachalam T, IM Group, SETLabs, Infosys.
2
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
3
Part I :-Analytics Demystified.
4
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
5
Analytics – An Enabler
Analytics is the science of analysis. -
Wikipedia.
Why?
How?
Where?
What?
6
Analytics vs. Analysis
Analytics
Analysis
Analytics
Analysis
Analytics
Analysis ?
7
Analytics vs. Analysis
Analysis
Tools Knowledge
Experience
InteractiveFeatures
Analytics
8
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.
9
Levels of Analytical Reporting
Strategic
Tactical
Operational
Modelers
Analysts
Knowledge Workers
Models/Workbenches
Dashboards/Visual Discovery
Spreadsheets/BI Reports
RolesTools
10
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
11
Part II :-Development Techniques.
12
Components of Analytics Application
AnalyticsApplication
Visual Discovery Interface
Analytic Functions
Application Server
Internal & External Sources
Source Mappings & Integration
Data Model & Analytic
Data Store
13
Characteristics of Analytics Application
•Logically•Physically•Technically
Integrated
•Appropriate•Meaningful•Self Explanatory
Intuitive
•Domain Specific•LOB Specific•Context Specific
Institutionalized
•Hierarchical•Navigational
Interactive
14
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
15
Packaged Analytics
Courtesy: Wayne W. Eckerson, Beyond Reporting, TDWI Best Practices Report, Third Quarter 2009, p 17.
16
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
17
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.
18
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.
19
Q & A
Questions?