SlideShare uma empresa Scribd logo
1 de 24
Data Provisioning An Enterprise and  Business Perspective
Problem Statement…. “ There's too much data and it's duplicated hundreds of times. The mistake companies make is that they start from the data they have. They need to ask what data do their users need and what are the questions they are asking. Understand the questions, how they can be answered and what kind of data is needed.” Quote by CIO of Major Corporation
The main dimensions of BI : Reporting (Operational) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The main dimensions of BI : Analysis (tactical) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The main dimensions of BI : Dashboards & Scorecards (strategic) ,[object Object],[object Object],[object Object]
What do end users really care about? ,[object Object],[object Object]
Report developers worst Nightmare……
Relational Rules ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Relational Rules,  slightly bent ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Dimensional Modeling: Star Schema
Recognizing ‘Facts’ and ‘Dimensions’ ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Extracting requirements to build the data model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Provisioning Life-Cycle
Different approaches to Data Warehouse ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],CONS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],PROS Centralized, Integrated Data with Direct access EDW Dependant Data Marts Leave Data where it is Independent Data Marts
Different approaches to Data Warehouse ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],CONS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],PROS Centralized, Integrated Data with Direct access EDW Dependant Data Marts Leave Data where it is Independent Data Marts
Different approaches to Data Warehouse ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],CONS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],PROS Centralized, Integrated Data with Direct access EDW Dependant Data Marts Leave Data where it is Independent Data Marts
Different approaches to Data Warehouse ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],CONS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],PROS Centralized, Integrated Data with Direct access EDW Dependant Data Marts Leave Data where it is Independent Data Marts
Actuate Information Objects as Middleware ,[object Object],[object Object],[object Object]
Information Pyramid: What, When & Where? 0 1 2 3 4 5
How companies end up with 1000’s of reports ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
How companies end up with 1000’s of reports…contd. ,[object Object],[object Object],[object Object],[object Object],[object Object]
How companies end up with 1000’s of reports…contd. ,[object Object],[object Object],[object Object],[object Object],[object Object]
How companies end up with 1000’s of reports…contd. ,[object Object],[object Object],[object Object]
What would you have done differently?

Mais conteúdo relacionado

Mais procurados

Impuls-Vortrag Data Strategy
Impuls-Vortrag Data StrategyImpuls-Vortrag Data Strategy
Impuls-Vortrag Data StrategyMarco Geuer
 
Business Intelligence Presentation (1/2)
Business Intelligence Presentation (1/2)Business Intelligence Presentation (1/2)
Business Intelligence Presentation (1/2)Bernardo Najlis
 
Modern Business Intelligence - Design and Implementations
Modern Business Intelligence - Design and ImplementationsModern Business Intelligence - Design and Implementations
Modern Business Intelligence - Design and ImplementationsDavid J Rosenthal
 
Data Quality
Data QualityData Quality
Data Qualityjerdeb
 
Components of a Data-Warehouse
Components of a Data-WarehouseComponents of a Data-Warehouse
Components of a Data-WarehouseAbdul Aslam
 
Business intelligence vs business analytics
Business intelligence  vs business analyticsBusiness intelligence  vs business analytics
Business intelligence vs business analyticsSuvradeep Rudra
 
Big Data Architecture
Big Data ArchitectureBig Data Architecture
Big Data ArchitectureGuido Schmutz
 
Data Audit Approach To Developing An Enterprise Data Strategy
Data Audit Approach To Developing An Enterprise Data StrategyData Audit Approach To Developing An Enterprise Data Strategy
Data Audit Approach To Developing An Enterprise Data StrategyAlan McSweeney
 
Introduction to Business Intelligence
Introduction to Business IntelligenceIntroduction to Business Intelligence
Introduction to Business IntelligenceAlmog Ramrajkar
 
Big Data: Banking Industry Use Case
Big Data: Banking Industry Use Case Big Data: Banking Industry Use Case
Big Data: Banking Industry Use Case Ramandeep Kaur Bagri
 
DATA PREPROCESSING AND DATA CLEANSING
DATA PREPROCESSING AND DATA CLEANSINGDATA PREPROCESSING AND DATA CLEANSING
DATA PREPROCESSING AND DATA CLEANSINGAhtesham Ullah khan
 
Business Intelligence - A Management Perspective
Business Intelligence - A Management PerspectiveBusiness Intelligence - A Management Perspective
Business Intelligence - A Management Perspectivevinaya.hs
 
Most Common Data Governance Challenges in the Digital Economy
Most Common Data Governance Challenges in the Digital EconomyMost Common Data Governance Challenges in the Digital Economy
Most Common Data Governance Challenges in the Digital EconomyRobyn Bollhorst
 
Data Quality - Standards and Application to Open Data
Data Quality - Standards and Application to Open DataData Quality - Standards and Application to Open Data
Data Quality - Standards and Application to Open DataMarco Torchiano
 

Mais procurados (20)

Business intelligence
Business intelligenceBusiness intelligence
Business intelligence
 
Impuls-Vortrag Data Strategy
Impuls-Vortrag Data StrategyImpuls-Vortrag Data Strategy
Impuls-Vortrag Data Strategy
 
Business Intelligence Presentation (1/2)
Business Intelligence Presentation (1/2)Business Intelligence Presentation (1/2)
Business Intelligence Presentation (1/2)
 
Modern Business Intelligence - Design and Implementations
Modern Business Intelligence - Design and ImplementationsModern Business Intelligence - Design and Implementations
Modern Business Intelligence - Design and Implementations
 
Data monetization pov
Data monetization   povData monetization   pov
Data monetization pov
 
Segmentation
SegmentationSegmentation
Segmentation
 
Data Quality
Data QualityData Quality
Data Quality
 
Components of a Data-Warehouse
Components of a Data-WarehouseComponents of a Data-Warehouse
Components of a Data-Warehouse
 
Business intelligence vs business analytics
Business intelligence  vs business analyticsBusiness intelligence  vs business analytics
Business intelligence vs business analytics
 
Big Data Architecture
Big Data ArchitectureBig Data Architecture
Big Data Architecture
 
Data Audit Approach To Developing An Enterprise Data Strategy
Data Audit Approach To Developing An Enterprise Data StrategyData Audit Approach To Developing An Enterprise Data Strategy
Data Audit Approach To Developing An Enterprise Data Strategy
 
Introduction to Business Intelligence
Introduction to Business IntelligenceIntroduction to Business Intelligence
Introduction to Business Intelligence
 
Big Data: Banking Industry Use Case
Big Data: Banking Industry Use Case Big Data: Banking Industry Use Case
Big Data: Banking Industry Use Case
 
DATA PREPROCESSING AND DATA CLEANSING
DATA PREPROCESSING AND DATA CLEANSINGDATA PREPROCESSING AND DATA CLEANSING
DATA PREPROCESSING AND DATA CLEANSING
 
Business Intelligence - A Management Perspective
Business Intelligence - A Management PerspectiveBusiness Intelligence - A Management Perspective
Business Intelligence - A Management Perspective
 
Most Common Data Governance Challenges in the Digital Economy
Most Common Data Governance Challenges in the Digital EconomyMost Common Data Governance Challenges in the Digital Economy
Most Common Data Governance Challenges in the Digital Economy
 
Data warehouse
Data warehouseData warehouse
Data warehouse
 
Data analytics
Data analyticsData analytics
Data analytics
 
Data Cleaning Techniques
Data Cleaning TechniquesData Cleaning Techniques
Data Cleaning Techniques
 
Data Quality - Standards and Application to Open Data
Data Quality - Standards and Application to Open DataData Quality - Standards and Application to Open Data
Data Quality - Standards and Application to Open Data
 

Destaque

Microsoft Testing Tour - Setting up a Test Environment
Microsoft Testing Tour - Setting up a Test EnvironmentMicrosoft Testing Tour - Setting up a Test Environment
Microsoft Testing Tour - Setting up a Test EnvironmentAngela Dugan
 
Date warehousing concepts
Date warehousing conceptsDate warehousing concepts
Date warehousing conceptspcherukumalla
 
Data Warehouse Back to Basics: Dimensional Modeling
Data Warehouse Back to Basics: Dimensional ModelingData Warehouse Back to Basics: Dimensional Modeling
Data Warehouse Back to Basics: Dimensional ModelingDunn Solutions Group
 
SQL - Parallel Data Warehouse (PDW)
SQL - Parallel Data Warehouse (PDW)SQL - Parallel Data Warehouse (PDW)
SQL - Parallel Data Warehouse (PDW) Karan Gulati
 
Agile Data Engineering - Intro to Data Vault Modeling (2016)
Agile Data Engineering - Intro to Data Vault Modeling (2016)Agile Data Engineering - Intro to Data Vault Modeling (2016)
Agile Data Engineering - Intro to Data Vault Modeling (2016)Kent Graziano
 
Data Warehousing Datamining Concepts
Data Warehousing Datamining ConceptsData Warehousing Datamining Concepts
Data Warehousing Datamining Conceptsraulmisir
 
Dimensional Modeling
Dimensional ModelingDimensional Modeling
Dimensional Modelingaksrauf
 
Architecting a Data Warehouse: A Case Study
Architecting a Data Warehouse: A Case StudyArchitecting a Data Warehouse: A Case Study
Architecting a Data Warehouse: A Case StudyMark Ginnebaugh
 
Difference between ER-Modeling and Dimensional Modeling
Difference between ER-Modeling and Dimensional ModelingDifference between ER-Modeling and Dimensional Modeling
Difference between ER-Modeling and Dimensional ModelingAbdul Aslam
 
Cassandra By Example: Data Modelling with CQL3
Cassandra By Example: Data Modelling with CQL3Cassandra By Example: Data Modelling with CQL3
Cassandra By Example: Data Modelling with CQL3Eric Evans
 
Big data architectures and the data lake
Big data architectures and the data lakeBig data architectures and the data lake
Big data architectures and the data lakeJames Serra
 
DATA WAREHOUSING
DATA WAREHOUSINGDATA WAREHOUSING
DATA WAREHOUSINGKing Julian
 
Data warehouse architecture
Data warehouse architectureData warehouse architecture
Data warehouse architecturepcherukumalla
 
Data Warehouse Modeling
Data Warehouse ModelingData Warehouse Modeling
Data Warehouse Modelingvivekjv
 
Building an Effective Data Warehouse Architecture
Building an Effective Data Warehouse ArchitectureBuilding an Effective Data Warehouse Architecture
Building an Effective Data Warehouse ArchitectureJames Serra
 

Destaque (18)

Microsoft Testing Tour - Setting up a Test Environment
Microsoft Testing Tour - Setting up a Test EnvironmentMicrosoft Testing Tour - Setting up a Test Environment
Microsoft Testing Tour - Setting up a Test Environment
 
A19 amis
A19 amisA19 amis
A19 amis
 
Date warehousing concepts
Date warehousing conceptsDate warehousing concepts
Date warehousing concepts
 
Data Warehouse Back to Basics: Dimensional Modeling
Data Warehouse Back to Basics: Dimensional ModelingData Warehouse Back to Basics: Dimensional Modeling
Data Warehouse Back to Basics: Dimensional Modeling
 
SQL - Parallel Data Warehouse (PDW)
SQL - Parallel Data Warehouse (PDW)SQL - Parallel Data Warehouse (PDW)
SQL - Parallel Data Warehouse (PDW)
 
Agile Data Engineering - Intro to Data Vault Modeling (2016)
Agile Data Engineering - Intro to Data Vault Modeling (2016)Agile Data Engineering - Intro to Data Vault Modeling (2016)
Agile Data Engineering - Intro to Data Vault Modeling (2016)
 
Data Warehousing Datamining Concepts
Data Warehousing Datamining ConceptsData Warehousing Datamining Concepts
Data Warehousing Datamining Concepts
 
Dimensional Modeling
Dimensional ModelingDimensional Modeling
Dimensional Modeling
 
Architecting a Data Warehouse: A Case Study
Architecting a Data Warehouse: A Case StudyArchitecting a Data Warehouse: A Case Study
Architecting a Data Warehouse: A Case Study
 
Difference between ER-Modeling and Dimensional Modeling
Difference between ER-Modeling and Dimensional ModelingDifference between ER-Modeling and Dimensional Modeling
Difference between ER-Modeling and Dimensional Modeling
 
Cassandra By Example: Data Modelling with CQL3
Cassandra By Example: Data Modelling with CQL3Cassandra By Example: Data Modelling with CQL3
Cassandra By Example: Data Modelling with CQL3
 
Data Warehouse 101
Data Warehouse 101Data Warehouse 101
Data Warehouse 101
 
Big data architectures and the data lake
Big data architectures and the data lakeBig data architectures and the data lake
Big data architectures and the data lake
 
DATA WAREHOUSING
DATA WAREHOUSINGDATA WAREHOUSING
DATA WAREHOUSING
 
Data warehouse architecture
Data warehouse architectureData warehouse architecture
Data warehouse architecture
 
DATA WAREHOUSING AND DATA MINING
DATA WAREHOUSING AND DATA MININGDATA WAREHOUSING AND DATA MINING
DATA WAREHOUSING AND DATA MINING
 
Data Warehouse Modeling
Data Warehouse ModelingData Warehouse Modeling
Data Warehouse Modeling
 
Building an Effective Data Warehouse Architecture
Building an Effective Data Warehouse ArchitectureBuilding an Effective Data Warehouse Architecture
Building an Effective Data Warehouse Architecture
 

Semelhante a Data Provisioning & Optimization

Finding business value in Big Data
Finding business value in Big DataFinding business value in Big Data
Finding business value in Big DataJames Serra
 
Data Mesh in Azure using Cloud Scale Analytics (WAF)
Data Mesh in Azure using Cloud Scale Analytics (WAF)Data Mesh in Azure using Cloud Scale Analytics (WAF)
Data Mesh in Azure using Cloud Scale Analytics (WAF)Nathan Bijnens
 
Building a Big Data Solution
Building a Big Data SolutionBuilding a Big Data Solution
Building a Big Data SolutionJames Serra
 
Datawarehousing
DatawarehousingDatawarehousing
Datawarehousingwork
 
Webinar - Accelerating Hadoop Success with Rapid Data Integration for the Mod...
Webinar - Accelerating Hadoop Success with Rapid Data Integration for the Mod...Webinar - Accelerating Hadoop Success with Rapid Data Integration for the Mod...
Webinar - Accelerating Hadoop Success with Rapid Data Integration for the Mod...Hortonworks
 
The Warranty Data Lake – After, Inc.
The Warranty Data Lake – After, Inc.The Warranty Data Lake – After, Inc.
The Warranty Data Lake – After, Inc.Richard Vermillion
 
Big Data's Impact on the Enterprise
Big Data's Impact on the EnterpriseBig Data's Impact on the Enterprise
Big Data's Impact on the EnterpriseCaserta
 
Top SAP Online training institute in Hyderabad
Top SAP Online training institute in HyderabadTop SAP Online training institute in Hyderabad
Top SAP Online training institute in HyderabadAadhyaKrishnan
 
Dataware housing
Dataware housingDataware housing
Dataware housingwork
 
8.17.11 big data and hadoop with informatica slideshare
8.17.11 big data and hadoop with informatica slideshare8.17.11 big data and hadoop with informatica slideshare
8.17.11 big data and hadoop with informatica slideshareJulianna DeLua
 
Datawarehouse Overview
Datawarehouse OverviewDatawarehouse Overview
Datawarehouse Overviewashok kumar
 
Big data an elephant business opportunities
Big data an elephant   business opportunitiesBig data an elephant   business opportunities
Big data an elephant business opportunitiesBigdata Meetup Kochi
 
What is OLAP -Data Warehouse Concepts - IT Online Training @ Newyorksys
What is OLAP -Data Warehouse Concepts - IT Online Training @ NewyorksysWhat is OLAP -Data Warehouse Concepts - IT Online Training @ Newyorksys
What is OLAP -Data Warehouse Concepts - IT Online Training @ NewyorksysNEWYORKSYS-IT SOLUTIONS
 
Is your big data journey stalling? Take the Leap with Capgemini and Cloudera
Is your big data journey stalling? Take the Leap with Capgemini and ClouderaIs your big data journey stalling? Take the Leap with Capgemini and Cloudera
Is your big data journey stalling? Take the Leap with Capgemini and ClouderaCloudera, Inc.
 
Overview of business intelligence
Overview of business intelligenceOverview of business intelligence
Overview of business intelligenceAhsan Kabir
 
2015 02 12 talend hortonworks webinar challenges to hadoop adoption
2015 02 12 talend hortonworks webinar challenges to hadoop adoption2015 02 12 talend hortonworks webinar challenges to hadoop adoption
2015 02 12 talend hortonworks webinar challenges to hadoop adoptionHortonworks
 
Bridging the Last Mile: Getting Data to the People Who Need It (APAC)
Bridging the Last Mile: Getting Data to the People Who Need It (APAC)Bridging the Last Mile: Getting Data to the People Who Need It (APAC)
Bridging the Last Mile: Getting Data to the People Who Need It (APAC)Denodo
 
CDI-MDMSummit.290213824
CDI-MDMSummit.290213824CDI-MDMSummit.290213824
CDI-MDMSummit.290213824ypai
 
Semantic 'Radar' Steers Users to Insights in the Data Lake
Semantic 'Radar' Steers Users to Insights in the Data LakeSemantic 'Radar' Steers Users to Insights in the Data Lake
Semantic 'Radar' Steers Users to Insights in the Data LakeCognizant
 

Semelhante a Data Provisioning & Optimization (20)

IT Ready - DW: 1st Day
IT Ready - DW: 1st Day IT Ready - DW: 1st Day
IT Ready - DW: 1st Day
 
Finding business value in Big Data
Finding business value in Big DataFinding business value in Big Data
Finding business value in Big Data
 
Data Mesh in Azure using Cloud Scale Analytics (WAF)
Data Mesh in Azure using Cloud Scale Analytics (WAF)Data Mesh in Azure using Cloud Scale Analytics (WAF)
Data Mesh in Azure using Cloud Scale Analytics (WAF)
 
Building a Big Data Solution
Building a Big Data SolutionBuilding a Big Data Solution
Building a Big Data Solution
 
Datawarehousing
DatawarehousingDatawarehousing
Datawarehousing
 
Webinar - Accelerating Hadoop Success with Rapid Data Integration for the Mod...
Webinar - Accelerating Hadoop Success with Rapid Data Integration for the Mod...Webinar - Accelerating Hadoop Success with Rapid Data Integration for the Mod...
Webinar - Accelerating Hadoop Success with Rapid Data Integration for the Mod...
 
The Warranty Data Lake – After, Inc.
The Warranty Data Lake – After, Inc.The Warranty Data Lake – After, Inc.
The Warranty Data Lake – After, Inc.
 
Big Data's Impact on the Enterprise
Big Data's Impact on the EnterpriseBig Data's Impact on the Enterprise
Big Data's Impact on the Enterprise
 
Top SAP Online training institute in Hyderabad
Top SAP Online training institute in HyderabadTop SAP Online training institute in Hyderabad
Top SAP Online training institute in Hyderabad
 
Dataware housing
Dataware housingDataware housing
Dataware housing
 
8.17.11 big data and hadoop with informatica slideshare
8.17.11 big data and hadoop with informatica slideshare8.17.11 big data and hadoop with informatica slideshare
8.17.11 big data and hadoop with informatica slideshare
 
Datawarehouse Overview
Datawarehouse OverviewDatawarehouse Overview
Datawarehouse Overview
 
Big data an elephant business opportunities
Big data an elephant   business opportunitiesBig data an elephant   business opportunities
Big data an elephant business opportunities
 
What is OLAP -Data Warehouse Concepts - IT Online Training @ Newyorksys
What is OLAP -Data Warehouse Concepts - IT Online Training @ NewyorksysWhat is OLAP -Data Warehouse Concepts - IT Online Training @ Newyorksys
What is OLAP -Data Warehouse Concepts - IT Online Training @ Newyorksys
 
Is your big data journey stalling? Take the Leap with Capgemini and Cloudera
Is your big data journey stalling? Take the Leap with Capgemini and ClouderaIs your big data journey stalling? Take the Leap with Capgemini and Cloudera
Is your big data journey stalling? Take the Leap with Capgemini and Cloudera
 
Overview of business intelligence
Overview of business intelligenceOverview of business intelligence
Overview of business intelligence
 
2015 02 12 talend hortonworks webinar challenges to hadoop adoption
2015 02 12 talend hortonworks webinar challenges to hadoop adoption2015 02 12 talend hortonworks webinar challenges to hadoop adoption
2015 02 12 talend hortonworks webinar challenges to hadoop adoption
 
Bridging the Last Mile: Getting Data to the People Who Need It (APAC)
Bridging the Last Mile: Getting Data to the People Who Need It (APAC)Bridging the Last Mile: Getting Data to the People Who Need It (APAC)
Bridging the Last Mile: Getting Data to the People Who Need It (APAC)
 
CDI-MDMSummit.290213824
CDI-MDMSummit.290213824CDI-MDMSummit.290213824
CDI-MDMSummit.290213824
 
Semantic 'Radar' Steers Users to Insights in the Data Lake
Semantic 'Radar' Steers Users to Insights in the Data LakeSemantic 'Radar' Steers Users to Insights in the Data Lake
Semantic 'Radar' Steers Users to Insights in the Data Lake
 

Mais de Ambareesh Kulkarni

Travel Management Dashboard application
Travel Management Dashboard applicationTravel Management Dashboard application
Travel Management Dashboard applicationAmbareesh Kulkarni
 
Carlson Wagonlit: Award winning application
Carlson Wagonlit: Award winning applicationCarlson Wagonlit: Award winning application
Carlson Wagonlit: Award winning applicationAmbareesh Kulkarni
 
Analyze Optimize Realize - Business Value Analysis
Analyze Optimize Realize - Business Value AnalysisAnalyze Optimize Realize - Business Value Analysis
Analyze Optimize Realize - Business Value AnalysisAmbareesh Kulkarni
 
Evolution of Client Services functions
Evolution of Client Services functionsEvolution of Client Services functions
Evolution of Client Services functionsAmbareesh Kulkarni
 
Packaged Dashboard Reporting Solution
Packaged Dashboard Reporting Solution Packaged Dashboard Reporting Solution
Packaged Dashboard Reporting Solution Ambareesh Kulkarni
 
Actuate Certified Business Solutions for SAP
Actuate Certified Business Solutions for SAPActuate Certified Business Solutions for SAP
Actuate Certified Business Solutions for SAPAmbareesh Kulkarni
 
Professional Services Project Delivery Methodology
Professional Services Project Delivery MethodologyProfessional Services Project Delivery Methodology
Professional Services Project Delivery MethodologyAmbareesh Kulkarni
 
Actuate BI implementation for MassMutual's SAP BW
Actuate BI implementation for MassMutual's SAP BW Actuate BI implementation for MassMutual's SAP BW
Actuate BI implementation for MassMutual's SAP BW Ambareesh Kulkarni
 
Professional Services packaged solutions for SAP
Professional Services packaged solutions for SAPProfessional Services packaged solutions for SAP
Professional Services packaged solutions for SAPAmbareesh Kulkarni
 
Zero Touch Operating Systems Deployment
Zero Touch Operating Systems DeploymentZero Touch Operating Systems Deployment
Zero Touch Operating Systems DeploymentAmbareesh Kulkarni
 
Ambareesh Kulkarni, Professional background
Ambareesh Kulkarni, Professional backgroundAmbareesh Kulkarni, Professional background
Ambareesh Kulkarni, Professional backgroundAmbareesh Kulkarni
 
Professional Services Roadmap 2011 and beyond
Professional Services Roadmap 2011 and beyondProfessional Services Roadmap 2011 and beyond
Professional Services Roadmap 2011 and beyondAmbareesh Kulkarni
 
Professional Services Automation
Professional Services AutomationProfessional Services Automation
Professional Services AutomationAmbareesh Kulkarni
 
Storage Provisioning for Enterprise Information Applications
Storage Provisioning for Enterprise Information ApplicationsStorage Provisioning for Enterprise Information Applications
Storage Provisioning for Enterprise Information ApplicationsAmbareesh Kulkarni
 
Professional Services Sales Techniques & Methodology
Professional Services Sales Techniques & MethodologyProfessional Services Sales Techniques & Methodology
Professional Services Sales Techniques & MethodologyAmbareesh Kulkarni
 

Mais de Ambareesh Kulkarni (20)

Travel Management Dashboard application
Travel Management Dashboard applicationTravel Management Dashboard application
Travel Management Dashboard application
 
Carlson Wagonlit: Award winning application
Carlson Wagonlit: Award winning applicationCarlson Wagonlit: Award winning application
Carlson Wagonlit: Award winning application
 
Analyze Optimize Realize - Business Value Analysis
Analyze Optimize Realize - Business Value AnalysisAnalyze Optimize Realize - Business Value Analysis
Analyze Optimize Realize - Business Value Analysis
 
Evolution of Client Services functions
Evolution of Client Services functionsEvolution of Client Services functions
Evolution of Client Services functions
 
Building the Digital Bank
Building the Digital BankBuilding the Digital Bank
Building the Digital Bank
 
Packaged Dashboard Reporting Solution
Packaged Dashboard Reporting Solution Packaged Dashboard Reporting Solution
Packaged Dashboard Reporting Solution
 
Actuate Certified Business Solutions for SAP
Actuate Certified Business Solutions for SAPActuate Certified Business Solutions for SAP
Actuate Certified Business Solutions for SAP
 
Professional Services Project Delivery Methodology
Professional Services Project Delivery MethodologyProfessional Services Project Delivery Methodology
Professional Services Project Delivery Methodology
 
Windows 10 Migration
Windows 10 MigrationWindows 10 Migration
Windows 10 Migration
 
Actuate BI implementation for MassMutual's SAP BW
Actuate BI implementation for MassMutual's SAP BW Actuate BI implementation for MassMutual's SAP BW
Actuate BI implementation for MassMutual's SAP BW
 
Professional Services packaged solutions for SAP
Professional Services packaged solutions for SAPProfessional Services packaged solutions for SAP
Professional Services packaged solutions for SAP
 
SAP R3 SQL Query Builder
SAP R3 SQL Query BuilderSAP R3 SQL Query Builder
SAP R3 SQL Query Builder
 
Zero Touch Operating Systems Deployment
Zero Touch Operating Systems DeploymentZero Touch Operating Systems Deployment
Zero Touch Operating Systems Deployment
 
Ambareesh Kulkarni, Professional background
Ambareesh Kulkarni, Professional backgroundAmbareesh Kulkarni, Professional background
Ambareesh Kulkarni, Professional background
 
Professional Services Roadmap 2011 and beyond
Professional Services Roadmap 2011 and beyondProfessional Services Roadmap 2011 and beyond
Professional Services Roadmap 2011 and beyond
 
1E and Servicenow integration
1E and Servicenow integration1E and Servicenow integration
1E and Servicenow integration
 
Enterprise BI & SOA
Enterprise BI & SOAEnterprise BI & SOA
Enterprise BI & SOA
 
Professional Services Automation
Professional Services AutomationProfessional Services Automation
Professional Services Automation
 
Storage Provisioning for Enterprise Information Applications
Storage Provisioning for Enterprise Information ApplicationsStorage Provisioning for Enterprise Information Applications
Storage Provisioning for Enterprise Information Applications
 
Professional Services Sales Techniques & Methodology
Professional Services Sales Techniques & MethodologyProfessional Services Sales Techniques & Methodology
Professional Services Sales Techniques & Methodology
 

Último

Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .Alan Dix
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxLoriGlavin3
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxLoriGlavin3
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfPrecisely
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr BaganFwdays
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxLoriGlavin3
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteDianaGray10
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxLoriGlavin3
 
Advanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionAdvanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionDilum Bandara
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersRaghuram Pandurangan
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Commit University
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfAddepto
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxLoriGlavin3
 
unit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptxunit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptxBkGupta21
 

Último (20)

Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test Suite
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 
DMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special EditionDMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special Edition
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
 
Advanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionAdvanced Computer Architecture – An Introduction
Advanced Computer Architecture – An Introduction
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information Developers
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdf
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
 
unit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptxunit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptx
 

Data Provisioning & Optimization

Notas do Editor

  1. Information consumers have an insatiable appetite for information. When you ask a business user the question “What information do you need?” the answer usually is “I need everything and I need it quickly” In the real world there is always a conflict between business requirements and performance and usability.
  2. Dimension Tables Contain information by which a Fact can be presented Include multiple levels of the Dimension (example: Market) Values for all levels of the Dimension are known (example: Time) Related to Fact Table at lowest level of Dimension (example: Market) Exist in a one-to-many relationship with the Fact Table Fact Table Designed to answer questions for one business measure Contains only single-valued Facts Represents derived values Must be applicable to all attached Dimension Tables One per Star Schema