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Business Intelligence &Data Warehousing
Copyright 2005 Satyam Computer Services Limited.
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Agenda
Roadblocks for BI Implementations
Roadblocks for BI Implementations1
1
2
2 Introduction to Packaged Analytics - iDecisions
Introduction to Packaged Analytics - iDecisions
3
3 Examples of Industry Verticals and Subject Areas
Examples of Industry Verticals and Subject Areas
4
4 iDecisions Case Studies
iDecisions Case Studies
5
5 Satyam Business Intelligence Practice Overview
Satyam Business Intelligence Practice Overview
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SourceOLTP
Systems
ReplicatedData Sets
Infinite Versionsof the Truth
Data Extracts
SourceOLTP
Systems
DataWarehouse
One Versionof the Truth
Source
OLTPSystems
HR Reporting System
Sales Reporting System
Mgmnt Reporting System
CRM Reporting System
Replicated Data Sets
Multiple Versions of the Truth
Oracle Financials
i2 AASystem
i2 Seibel CRM 3rd PartyData
OracleDW
Siebel AASystem
3rd Party AASystem
Whose Truth is it Anyway
60-80% of DW projects have failed to deliver on the promise
Business Intelligence - Reality Check
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Better Info
Better Data Qual
End-User Prod
Sprt Org Chg
Imprv Cost Mgmt
Reduce DSS Bac
Better Perf
Facilitate BPR
IT Productivity
Other
0 0.1 0.2 0.3 0.4 0.5 0.6
Better Info
Better Data Qual
End-User Prod
Sprt Org Chg
Imprv Cost Mgmt
Reduce DSS Bac
Better Perf
Facilitate BPR
IT Productivity
Other
Common Reasons for Failure
The Great Divide Between Business Users and IT
Ambitious Planning Data Warehouse is a Journey, not a Destination
Architecture and Technology Compromises
Quality of Data impacts the Quality of Decisions
Gap in Technology and Process Skills
Usage Problems Reports Vs Analytical Capability
Business Intelligence - Reality Check
Though the Business Drivers for Business Intelligence are clear, success hasbeen elusive due to clearly defined requirements
Source:Meta Group
0
2
4
6
8
10
12
14
16
18
1970's 1980's 1990's 2000's
Business Information Needs IT's Ability to Supply
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Multiple Data
Sources
Applications anddata organizedaround products
Heterogeneous
Hardware and
Databases
No consistentterminology /
business meta
data
#2#2
Proprietary
Technology
Standards
Multiple tools inmarket withown technology
stds
No openarchitecture
#4#4
Roadblocks
Time to Market
Modeling forDataWarehouse
Difficulty inwriting ETLprograms
Difficulty in
managingmetadata
#3#3#1#1
Unclear
Business
Requirements
Challenge indefininganalysis needs
(Reports VsAnalyticalNeeds)
Bridgingbusiness to
technology
Availability ofBusiness Users
Business Intelligence - Roadblocks
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Agenda
Roadblocks for BI ImplementationsRoadblocks for BI Implementations11
22 Introduction to Packaged Analytics - iDecisionsIntroduction to Packaged Analytics - iDecisions
33 Examples of Industry Verticals and Subject AreasExamples of Industry Verticals and Subject Areas
44 iDecisions Case StudiesiDecisions Case Studies
55 Satyam Business Intelligence Practice OverviewSatyam Business Intelligence Practice Overview
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Implementing Business Intelligence: Build Vs Buy
Source Build or Buy. Data Warehouse Institute 2003
Current Trends Business need - reduced time-to-market, i.e. faster implementation. Industry trend is move towards pre-packaged analytical applications
(pre-built & customizable Data Warehouse).
Trend similar to adoption of ERP applications and packagedapplications
Build Buy Build Buy
Average Project Cost(US$)
Return on Investment (in%)
2.1 mil. 1.8 mil. 104% 140%
15% 26%
Source: IDC's "The Financial Impact of Business Analytics" study made across 43European and U.S. organizations
A recent study by IDC also asserts that preA recent study by IDC also asserts that pre--built applications are cheaperbuilt applications are cheaper
to build and produce better returns on investmentto build and produce better returns on investment
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Definition of Packaged Analytics
Solution based on industry best practices
Highly Customizable and Flexible
Open Architecture
Analytical Application Templates : Packaged
AnalyticsPackaged Analytics are value-added solutions embedding knowledge ofthe process and expressing specific metrics for a given (set of) businessfunction(s) based on industry best practices.
Source: Gartner Group, 2002
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iDecisions Solution
Pre-Packaged, customization-friendly and Open AnalyticalApplication from Knowledge Dynamics
Developed based on Best-of-Breeds practice across
industries. Salient Features
Industry-standard multi-layer BI Data Model
Pre-built report and analysis templates Reporting and Analysis Layer delivered on Oracle BIEE
Technologies
Includes pre-built Metrics, Perspectives and Reportstemplates for data retrieval, measurement and analysis.
Highly customizable solution (One-Click Customization).
Cost-efficient and user-friendly interface.
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iDecisions - Accelerators for BI Solutions
Solution Accelerators for BI Analytical Solutions Industry Focused Domain Solutions
Pre-Built Data Models incorporating Industry Best Practices
Reduced Time-To-Market
Technology Neutral and Agnostic
Pre-Built Analytical Templates on Oracle BI EEE, Hyperion, Cognos, Business Objectsand MS Reporting Services
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iDecisions Packaged Analytics
iDecisions is a Packaged Analytic Application forBusiness Intelligence from Knowledge Dynamics.
It can be used by organizations to jumpstart their
Business Intelligence (Data Warehouse) initiatives.
iDecisions
Approach
Business
Requirements
Data
ModelETL Design
User AccessDesign
Development Test
BRD GapAnalysis
ImplementChange
Test
Traditional
Appro
ach
Month 1 Month 2 Month 3 Month 4 Month 6
Implementation time
reduced by 50%
Implementation time
reduced by 50%30%
Cost Savings
Illustrative
iDecisions
Approach
Business
Requirements
Data
ModelETL Design
User AccessDesign
Development TestBusiness
Requirements
Data
ModelETL Design
User AccessDesign
Development Test
BRD GapAnalysis
ImplementChange
Test
Traditional
Appro
ach
Month 1 Month 2 Month 3 Month 4 Month 6
Implementation time
reduced by 50%
Implementation time
reduced by 50%30%
Cost Savings
Illustrative
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iDecisions Deliverables and Value Proposition
Deliverable Value Proposition
Definition of staging files entry point for sourcedata (IFS format)
Pre-identified key data elements accelerates data acquisition from sourcesystems
Detailed Data Model Industry specific, flexible data models, decreases the time to build normalizedEDW and dimensional Data Models and map them to the acquisition andpresentation layers
Analytical Templates (standard predefined reportsand analytical scenarios)
Customizable templates, provides for domain specific, best practice analyticsand reporting that is flexible enough to incorporate metrics unique to eachbusiness
Internal Data
External Data
SOURCE DATA STAGING AREA DATABASE USER LAYER
AnalyticalTemplates
Data Entry ASCII, Excel etc.
Staging Area
Files, Either
RDBMS xml
or flat files
Custom
built
routines
Any
databaseData
Aggregation
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iDecisions on Oracle Platform
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Why iDecisions?
RAPID ROI with pre-built reports, templatesand best-practice analytics.
REDUCED TIME to implement.
REDUCED RISK at implementation.
QUICK time-to-market capability.
SCALABILITY included.
FLEXIBLE and customizable analyticapplications that cater to individual
requirements.
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Agenda
Roadblocks for BI ImplementationsRoadblocks for BI Implementations11
22 Introduction to Packaged Analytics - iDecisionsIntroduction to Packaged Analytics - iDecisions
33 Examples of Industry Verticals and Subject AreasExamples of Industry Verticals and Subject Areas
44 iDecisions Case StudiesiDecisions Case Studies
55 Satyam Business Intelligence Practice OverviewSatyam Business Intelligence Practice Overview
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Banking - Credit Risk
CustomerCountry
Account
Perspectives Metrics
- Risk Adjusted Return on Capital (RAROC)
- Expected Loss
- Unexpected Loss
- Change in Risk Profile
- Account Outstanding Amount
- Account Credit Repayment Status
- Account Write-off, Recovery & Write-BackDetails
- Relationship Officer Details
- Customer Credit Rating
Type, BU,Currency, Start-
End Dates, Credit
Limit, Rating,Charges,Repayment Grade
Customer Type
Customer Segment
Customer Demographics
Product
Prod.Desc & GrpStart-End DatesInterest, Charges
RiskGrade,L
imits,
Currency
Analytical Templates
Exposure Analysis
Current Relative Exposure byProduct
Current Exposure by DPDClassification
Exposure Trend by CollateralType
Delinquency Analysis
Accounts with current
DPD>180 days & DPDTrend
Old & Current AccountDelinquency
Write-off Amount by Loan
Classification Trend
Collateral Analysis
Collateral Mix by Country
Obligor Type by Outstanding
AmountCollateral Mix by Risk Grade
Collateral Type by FSV
Amount
Credit P rocess
Application Score Report
Credit Aging Report
Yield W ise Credit ReportRisk Grade vs RORC Report
IndustryInternal ClassificationRegulatory ClassificationLimits
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Banking - Customer Intelligence
Perspectives Metrics
-Customer Net Worth
-Customer Type (Salaried/Self Employed)
-Customer Residence Type (Rented/Own)
-Account Details
- Cost Details per Channel/Transaction
Type
- Cost of Capital, Operating Expenses,Other Cost Details
-Customer Contact Details
- Customer Acquisition Cost
- Customer Retention Cost
CustomerPro
duct
Account
Account Details
Type,Currency,Status, Balance,Brance
Customer Type
Customer Segment
Customer Demographics
ChannelChannel Start/End Date
Transaction Type
ProductStartD
ate
ProductEndDate
EffectiveInterestRa
te
Analytical Templates
Segmentation & P rofile Analysis
Segment Migration Trend Top Gainers by Segment
Segment by Similar Product AffinityNo. of Customer by P roducts Product by Length of Relationship
Behavioral Analysis
Same Time Joiners Trend
Segment Target vs. Actual
By Revenue
Customer Segments w ith
Different Product Affinity
Customer Profitability
High Profit Customer
Top Customers by Revenue
Credit Score AnalysisLowest Product Profits
Product Contribution
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Enterprise - Sales
CustomerT
ime
LegalEntity
Perspectives Metrics
- Billed Volume
- Billed Quantity
- Book to Bill Ratio
- Backlogs
- Order Entry Quantity
- Order Entry Amount
- Order Entry Costs
- Scheduled Deliver Quantity
- Scheduled Time Frame
- Order Booked Quantity
- Order Booked Revenue
Customer Type
Customer Segment
Customer Demographics
ProductProd.Desc & GrpStart-End DatesInterest, Charges
Month,quarter,
year
Analytical Templates
Comparative Analysis
Top Nth Products,
Bottom Nth Products,
Top X Customers,
Bottom Nth Customers,
Top Nth Salespersons, Bottom Nth Salespersons
Sales Variance Analysis
Actual vs. Budget,
Forecast,
Re-Forecast
Sales Trends
Variation over Time Periods Variation over P roducts
Sales Key Performance
Indicators
Sales by Customers,
Sales by P roducts,
Sales by Time P eriods
Market Competition
By Market Segment,
By Product Groups,
By Customer Locations
Entity Type
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Enterprise - Manufacturing
Lots
Tim
e
WorkArea
Perspectives
Lot Type, Desc
Process
Process Desc
Start-End Date
Month,quarter,
year
Analytical Templates
Key Indices Report
Work In P rogress
Turn Ratio
Cycle Time Moves
Yield
Efficiency
Fab Performance
Line Yield
Fab Yield
Wafer Acceptance TestYield
Fab CVP
Efficiency Report
Equipment Availability
Scrap
Re-work
Production Capacity
Wafer Start
Wafer Out
Fab Out
Area Desc
Area Size
Metrics
- Wafer Per Hour
- Equipment
Efficiency
- Utilisation
- Fab Yield
- Fab CLIP
- Run Time
- Lost Time
- Test Time
-Wafer Moves
-STEP Moves
-Location Moves
-Work-In-Progress
(WIP)
-End On Hand
-Begin On Hand
-Turn Ratio PerDay
-
Back-up Time
Shift Desc, Time
Shift
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Agenda
Roadblocks for BI ImplementationsRoadblocks for BI Implementations11
22 Introduction to Packaged Analytics - iDecisionsIntroduction to Packaged Analytics - iDecisions
33 Examples of Industry Verticals and Subject AreasExamples of Industry Verticals and Subject Areas
44 iDecisions Case StudiesiDecisions Case Studies
55 Satyam Business Intelligence Practice OverviewSatyam Business Intelligence Practice Overview
Case Study 1 Financial Intelligence System for a PortCase Study 1 - Financial Intelligence System for a Port
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CLIENT OVERVIEWOur client is the technological and market leader in port management and logistics. Theclients network of ports in Asia, the Middle East and Europe handles close to one-tenth ofglobal container throughput and 30% of world-wide transshipment volume. Our client isembarking on initiatives in e-commerce, logistics, cruise center development and eventmanagement with a view to extend their core competency across related business.
OBJECTIVEEnabling faster reporting and updatesProviding better transparency of informationEnabling sharing of resources and competencies to allentities
SOURCE
Non-Oracle
system
s
Oracle 11i Apps
Old COA
Oracle 11I Apps
New COA
External
Source
AR, AP, GL, PS
AR, AP, GL,
PS
Hyperion Essbase
App Manager / EIS Admin
Oracle 9i Database Server
STAGING WAREHOUSE / DATAMART END USER ACCESS
iLoad
Environmen
t
Essbase Integration Services
OWB
Repositor
y
Essbase Server
Essbase Cubes
FSDW
EIS
Repository
Analyzer
Repository
OWB Client
Financial System Data Warehouse Environment
Hyperion
Analyzer
Essbase Excel
Add-in Client
The solution implemented was a Financial System DataWarehouse dealing with Management Reporting, Revenue
Analysis, Spend Analysis, Debt Analysis and Staff Cost AnalysisBusiness Subject Areas
BENEFITSEase in development and consolidation of management
reportsEmpower users to perform on-line analysisMaintain historical data for meaningful correlation and trendanalysisProvide an integrated and consistent data repository tofacilitate information distribution for operational and planningsupportProvide users with pre-defined monthly, weekly, etc. reports
on timeReduce effort in preparing both regular and ad-hoc reportsand statistical analysis by empowering users to find theirown information through the use of powerful data accesstools.
SOLUTIONPopulation of Singapore financial/ historical data for bothold and new Chart of Accounts to the Financial System DataWarehouseFinancial Figures comes from the Oracle Financials,namely Oracle General Ledger (OGL), Oracle AccountsReceivables (OAR), Oracle Accounts Payables (OAP),Oracle Purchasing (OPS) and the different non-Oracle
Financials platform systems.Reporting and Analysis functionality for companies locatedin Singapore
Case Study 1 - Financial Intelligence System for a Portand Logistics Services Provider
Case Study 1 - Financial Intelligence System for a Portand Logistics Services Provider
C S d 2 A P fi bili M S
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TRADITIONAL
iDE
CISIONS
BusinessRequirements
Discovery
DataModel
ETL Design
User AccessDesign
Development Test
BRD GapAnalysis
ImplementChange
TestTIME & COST SAVINGS
Reduced Implementation
Time & Reduced Risk
The CustomerThe Customer
A UK based Pharma major with a presence in 116 countries with FY05 revenue of USD 39 Billion
Typical Implementation Duration 5 Months
Implementation Duration 3 Months
To meet the project objectivesthe following businessmodels were delivered.
The key business drivers for the implementation of the BI solution whereIdentify and effectively manage cost drivers to maximize group profitability.Interrogate the Sales and Cost of Goods Sold information, to derive Gross Profit at market and productlevel
Gather business insights to achieve multi-million dollars savings to replace the existing manual reportingand expense allocation processes.
Profit General
Ledger
Sales Expenses
Reduction of time 2 MonthsExpected ROI of 5 Million USD
Case Study 2- A Profitability Management SystemCase Study 2- A Profitability Management System
Th S l i
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The SolutionThe Solution
Customer Challenge
Timely management of tax liability arising out of profits accrued through intra-company sales was difficultdue to the manual processes involved in generating the relevant business insights
An IT enabled solution needed to be implemented within 3 calendar months to ensure appropriate profitmanagement for the current and future fiscal. Multi-million dollars of savings were at risk if the profitabilityrelated business insights could not be had within the required timeframe.
Business Benefits Operational Efficiency: Elimination of manual processes reducedthe time required for conducting profit and tax analysis Reduced Implementation Timeline: Shorter time to market for thesolution by leveraging iDecisions framework Best Practice Inculcation: Cross pollination of industry bestpractices due to utilization of iDecisions framework whose data
model is a collective essence of best practices across industries Leverage existing IT investments: iDecisions based Satyamsolution leveraged existing IT investments in JD Edwards andCognos thereby reducing additional investments in new products Futureproof Solution: Loose coupling of iDecisions with sourcesystems and reporting tools ensured that the business analyticsframework did not lock the customer into a particular product
Satyam Solution
A business insightssolution that provided therelevant profitability reportsfor intra-company sales after
collating the data frommultiple systems
Solution leveragesiDecisions a businessanalytics framework ofSatyam
Case Study 3 : APAC Financial Data Warehouse &Case Study 3 : APAC Financial Data Warehouse &
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The client is organized in the region with offices and manufacturing locations in variouscountries in the region. Singapore is the regional headquarters. There are five separateinstances of JDE in Asia. Each instance is customized to meet the local requirements andhas 7 years of data. Main Subject areas covered are P&L, Balance Sheet, Cash Flow,Product Code Analysis, Expense, Inventory, Liability, Capitol Expense, Accountreceivables, Sales & Gross Profit.
Case Study 3 : APAC Financial Data Warehouse &Budget Planning System
Case Study 3 : APAC Financial Data Warehouse &Budget Planning System
Excel based planning andbudgeting system that takes along time and lot of effort tocomplete.
Lack of centralRepository of financial &operational data cost
and profit analysis.
Business Pain Points
Lack of common views of financialinformation for analysis andbudgeting across variousdepartment and regions.
Integrated Financial Intelligence
System for FinancialPerformance Management
Focusing on Cost Allocation,Consolidation, Budget Planning
and Analytics
Solut
ion Effective management reporting and control. Manage multiple versions of budget acrros variousregions.
Able to perform manual adjustment to the costallocation With the consolidated financial data, finance usersare now able to perform budget planning and forecastexercise more efficiently and accurately Able to perform consolidation that allows users to
consolidate data from subsidiaries to generate thegroup balance sheet and profit & loss statements
Benefits
C St d 4 R l ti hi M k ti C St dCase Study 4 Relationship Marketing Case Study
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Case Study 4 - Relationship Marketing Case StudyCase Study 4 - Relationship Marketing Case Study
Objectives of the Campaign Automation Project Reduce customer acquisition costs by 50%
Boost customer retention by 5%
Support growing communication volume with same
resources Leverage customer knowledge to enhance targeting of right-
time communications
OverviewThe client is one of Indias leading private sector banks.
Provides a complete range of accounts and services.
Needed an effective way to Market its growing product and service lines
Leverage its customer base to optimize returns and
bottom-line revenue
Increase customer profitability
Build deeper, stronger customer relationships
Perform customer lifecycle marketing to send targeted
communications Build relationships and increase profit
Objectives of the Customer Activity Record(CAR) Project
Create a one-stop-shop for all Analytical Marketing needs
Enhance Customer Analysis capabilities with built ininformation such as scores and segments
Identify and implement a solution that is scalable bothfunctionally and technically
Benefits The key benefits derived by the client are:
Significant reduction in data preparation time for the campaignmanagement and marketing activities
Provided for increased latency of data for analytical marketingneeds to support real-time and event triggered marketingcampaigns.
Provided unrestrained slice and dice capabilities to end users tosupport data driven decision making by the users to design moreeffective campaigns.
Provides external data like competitor information to be
leveraged upon for analytical marketing purposes to increase theeffectiveness of the campaigns.
The Customer Activity Record is:
Used to store all analytical marketing data elements
inclusive of rollup dataAs a virtual sandbox of marketing data elements
(CAR)
A collection of all data elements that are owned by
other user groups but required for analytical
marketing (e.g. Credit Score)
Bank Enhances Bottom-Line Revenue Through Customer Lifecycle Marketing
A hit tArchitecture
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ArchitectureArchitecture
Analysis
Unica
StagingArea
Finware
FinOne
Vision Plus
Flex@
TPP
Base24
Debos
Others
Sources Acquisition Management User Access
OLAP Tool
ETL Tool
Reports
EDW
OCRM
Contact
MIS
Marketing
Reports
Weekly Load
Data Mining
De-D
uping
Cleansing
Ho
useholding
Mktg
ODS
Analysis
Unica
StagingArea
Finware
FinOne
Vision Plus
Flex@
TPP
Base24
Debos
Others
Finware
FinOne
Vision Plus
Flex@
TPP
Base24
Debos
Others
Sources Acquisition Management User Access
OLAP Tool
ETL Tool
Reports
EDW
OCRM
Contact
MIS
Marketing
Reports
Weekly Load
Data Mining
De-D
uping
Cleansing
Ho
useholding
Mktg
ODS
Unica Affinium
Agenda
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Agenda
Roadblocks for BI ImplementationsRoadblocks for BI Implementations11
22 Introduction to Analytical Application TemplatesIntroduction to Analytical Application Templates
33 Examples of Industry Verticals and Subject AreasExamples of Industry Verticals and Subject Areas
44 iDecisions Case StudiesiDecisions Case Studies
55 Satyam Business Intelligence Practice OverviewSatyam Business Intelligence Practice Overview
B siness Intelligence & Data Wareho sing Snapshot
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Business Intelligence & Data Warehousing - Snapshot
Part of 7500+ strong EBS group
3500+ member strong BIDW practice
Total Number of Engagements Completed - 300+
Active customers - 70+
Major successes in BIDW Solution Center model - Six BIDW Solution Centers
30+ Technology Competencies
15+ Alliances with leading technology vendors in BIDW
Certified at CMMI Level 5 Global
Global Delivery locations in Hungary, Canada, Malaysia, China and Australia apart from India and US
Niche Singapore based BI Consulting firm
Vertical BI expertise in Banking and Basel II Experience in implementing over 100 Business Intelligence projects in Asia-Pacific
On Target TM methodology for BI Consulting
iDecisions TM for Analytical Applications
Gartner Magic Quadrant for Business Intelligence Implementation Services, North America, 2006 Satyamin theVisionariesquadrant
Testimonials by Thought Leader Bil l Inmon and Industry Analysts
Won the TDWI Best Practices Award in 2002 and 2006
First organization from India to join the XBR L Consortium
Experiences in Large Warehouses and Off shoring
PR
ACTICE
KD
HIGHS
Competency Spectrum
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Competency Spectrum
Capability
Engagement
Proof-Points
Low
High
CognosIBM Data stage
Microsoft
Low High
InformaticaBusiness Objects
SAPBWTeradataOracle
MicrostrategyActuate
SAS
EPM Tools
Hyperion
TechnologyCapabilities
Kalido
Siebel AnalyticsEpiphany
3,500+ Strong practice Global Delivery locations
Certified at CMMI Level 5
Global
30+ Technology Competencies
15+ Strategic Alliances with
Tech vendors
Focused Competency Centers
for leading Technologies
400
260
200
60
140
220240
100
20 60 50 50
100 100
200
50
150
250
350
450
Oracle SAP NCR SAS COGNOS
BUSINESS OBJECTS INFORMATICA DATASTAGE ACTUATE HYPERION
EPIPHANY SEIBEL EAI/EII MICROSOFT OTHERS
Panorama
Information Builders
Proclarity
BAMCelequest
The Challenges faced by our Clients can be broadly
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SomeCommonRoad-blocks
SomePossibleInitiatives
1. Lack of analysisready data withintegration issues
1. Lack of analysisready data withintegration issues
Build a Datawarehouse / mart
Implement a data
integration platform
Build a Datawarehouse / mart
Implement a data
integration platform
Data WarehouseImplementationServices
Design, Implementand OperateIntegrationCompetency Center
Data WarehouseImplementationServices
Design, Implementand OperateIntegrationCompetency Center
2. Lack of easy-to-useanalysis tools andapplications
2. Lack of easy-to-useanalysis tools andapplications
Deploy user friendlyanalysis tools
Implement subject
oriented analyticalapplications
Deploy user friendlyanalysis tools
Implement subject
oriented analyticalapplications
Design andImplementation of BIApplications
Implementation ofiDecisions basedSolutions
Design andImplementation of BIApplications
Implementation ofiDecisions basedSolutions
SampleOfferings
3. Lack of analysis cultureand processes
3. Lack of analysis cultureand processes
Implement data drivenprograms
Implement aReporting/BusinessIntelligence Unit
Implement data drivenprograms
Implement aReporting/Business
Intelligence Unit
Technical andAnalytical Consultingto accelerate adoption
Design, Implement andOperate BusinessIntelligenceCompetency Center
Technical and
Analytical Consultingto accelerate adoption
Design, Implement andOperate BusinessIntelligenceCompetency Center
classified into Integration, Intelligence, Insight
BIDW Practice Overview
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Satyam BI/DW SolutionOffering Framework
BIDW Practice Overview
Customers BI ValueFramework
StrategyMittal Steel, EGL, Walgreen, BN,
DHL
Organization &Process
Telstra, Cisco, GECF, GEE
Applications andFunctionalityHLI, J&J, Starhub, CAT, DuPont
BI InfrastructureGECF, Telstra, Cisco, Barclays
ERP CRM SCM Legacy
Intelligence
Integration
Insight
EBS
Depicts Gartners BI Value Framework, SourceGartner Research (April 2004)
BI Solution Centers,PROBIS Program Mgmt.,Six Sigma
Dedicated Business Solutions Group
for Strategy Consulting based in US
Acquisition of Knowledge Dynamics -Consulting Services and Citisoft Investment Banking
Solution Centers large one stopshop for BI & DW. PROBIS uniqueProgram management methodology forBI and Six Sigma
Intelligence includes BI solutions like
Churn, Campaign Mgmt, BAM
Satyams Enterprise Business
Solutions practice (EBS) has strongexpertise in ERP, CRM, SCM a, EAIand BPM solutions.
Dedicated Process Consulting group
for SOX Solutions, Enterprise RiskManagement and BPR
Business Solutions Group,Knowledge Dynamics,
Citisoft &Process Consulting
Insight include Business Analyticsservices like Customer Intelligence,Procurement intelligence.
Integration services are focused ondata modeling, ETL, DB design andother such core DW processes andarchitectures.
Satyam Oracle BI Experience Profile
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Satyam Oracle BI Experience Profile
Influencer Partner of Oracle 125 Skilled Personnel in Oracle BI tools
70 Advanced Experts
35 Experts 20 Intermediate
Expertise in Various tools under the Oracle BI portfolio Oracle Warehouse Builder Oracle Discoverer Desktop Oracle Discoverer Plus
Oracle Portal Oracle EDW Oracle BI Beans Oracle OLAP Server Oracle Financial Analyzer Large Project base worldwide
Worked with various technologies, and businessdomains
Detailed reporting to digital dashboards Implemented defined benchmarks and created the
best practices
OracleBIDiscoverer Plus OracleBI Discoverer
Viewer
OracleBI DiscovererPortlets
OracleBISpreadsheetAdd-in
OracleBI Discoverer Administrator
OracleBI Warehouse Builder
AS10gR2 PortalAS10gR2 BI
OOracleracle BBII PProductroduct AArchitecturerchitecture
Satyam Oracle BI Competency
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Our Alliance with OracleOur Alliance with OracleStrategic Alliance with Oracle and Preferred System Integrator PartnerAccess to Oracle technical support portal to support our Centre of ExcellenceRights to use all Oracle software for competency development and prototypedevelopment
Regular training on all upgrades and fresh releasesLatest software of all the Oracle BI products
Oracle Center Of ExcellenceOracle Center Of Excellence
Our Investments
Infrastructure ( Exclusive servers for multipleinstances)
Dedicated resources at architect level
Rigorous quality checks on project deliverables
Knowledge mgt portal & discussion forums
Conducting Best practice sessions
Tool Benchmarking & feature comparisons
Training and certifications
Benefits to Customers
Improved Productivity and hence lower costs
Reduced cycle times for deployment of solutions
Ensured quality
Proven technical capabilities to demonstrateinnovative solutions and proof of concepts
Reduced risks in technology deployment
Benchmarking of tool capabilities
Satyam Oracle BI Competency
Sample Oracle BI & DW Projects
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Sample Oracle BI & DW Projects
Customer Project description Tools usedA finance major in the US Data Warehouse for consolidating the global
financial dataOFA and Hyperion Essbase on a Sun Solaris
and Unix platform
Insurance major in the UK Planning, projection and modeling system OFA on a Windows NT and Unix platform
Retail major in India Data marts development for all the divisions along
with the deployment of a Managementdashboard
OFA, Oracle Express Objects and Oracle
Express Analyser on Unix platform
3 projects in the Retail sector major in theMiddle East
Data Warehouse implementation OFA and OSA
Telecom major in the Middle East Web-enabled DSS which involves building a DataWarehouse
OFA and OSA
Insurance Major in Japan Finance Data Mart development Discoverer
ISP major in India Data Warehouse implementation Discoverer
Banking Major in India Data Marts for tracking branch performance and foranalysing loan repayments
Oracle Express Server, Oracle Express Objects,Discoverer
Multinational Banking Major in Singapore Data Marts for Sales, Market risk and AssetLiability Management
Discoverer
Healthcare Major in India Enterprise Data Warehouse implementation Oracle Express Server, Express Objects,Express Analyser
Multination Beverages Major in Dubai Sales Data Mart implementation Oracle Express Server, OSA
Telecom Major in India Data Warehouse implementation Oracle Designer, Oracle Enterprise Manager,Discoverer
Logistics Major in Australia Data Marts development for Sales & Marketing,
Finance, Yield Management, Operations andthe final development of a Data Warehouse
OSA, Discoverer, Oracle Warehouse Builder
To know more.
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To know more.
Visit Satyams at Booth No 3548 OracleOpenWorld 2007 in San Francisco.
November 11-15, 2007 @ MosconeConvention Center
www.iDecisions.com
or
http://www.idecisions.com/http://www.idecisions.com/ -
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www.satyam.com
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