Targets For Dw
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Targets for DW With Case Studies
Three targets for DW
Target – fundamental purpose or objective of DW initiative A point solution for single or a few applications Infrastructure that supports multiple and current future applications Organizational transformation that fundamentally changes how the organization competes in the marketplace
A Point Solution
World Bank
Infrastructure
3M
3M
3M
Organizational Transformation
Harrah’s Entertainment
Harrah’s Entertainment
FAC
FAC
FAC
All Targets Can Bring Success
What it takes to be successful? External market/internal focus – FAC, Harrah’s. World Bank – internal focus, 3M – both internal & external Source of sponsorship –
What it takes to be successful?
Resources & Approval processes
What it takes to be successful?
Potential impacts:
Critical Success Factors Information – DW should have the means of converting mass of data into information. Ease of Access – based on user community Data Standards – corporate standards : data definitions, data quality, data access, data exploitation and data presentation. Dedicated resource – exclusively to queries and DSS.
Critical Success Factors Adequate Performance – DW technologies, components, physical schemas: optimizing the performance of the system as a whole. Corporate Sponsorship – pan-corporate project Operationally Stable – stable & safe : performance, availability, security, and metadata management
Critical Success Factors Agreed infrastructure – support DSS New user culture – users need to change their business culture to be fully aligned with the possibilities of Data Warehousing Technology. Source data – sourced inside and outside the enterprise
DW Project Management Principles
Strategic focus – supporting the known strategic objective of the enterprise. Executive sponsorship – sponsorship and support are needed at the most senior levels Goal and benefits-oriented approach – benefits are unforeseen but the benefits should be expressed in general terms in order to provide clear goals for the project Communication Enterprise survey – high level survey of entire enterprise Application delivery – the first, and each successive, iteration of the data warehouse should be based on the deployment of clearly defined application functionality.
DW Project Management Principles Iterative deployment – The deployment of each phase of the warehouse should be based on an evolutionary model of system deployment. Opportunistic – flexible to detour in pursuit of business oriented opportunities that arise during the course of the project Rapid deployment of first iteration Pragmatism – combination of top-down , bottom-up approach
Data Warehousing Design and Construction – METIS METHODOLOGY
There are eight stages defined by METIS methodology
Elements and Objectives of the METIS methodology
Organizational readiness Assessment – assessing the key business drivers, the organizational culture, the legacy systems, and data quality and the skills required. Business Strategy Definition – capturing the business vision for data exploitation and should result in a comprehensive business strategy model for this purpose. Data Warehousing Architecture Definition – logical architecture and the scope of the warehouse Data Warehousing Infrastructure Design – physical infrastructure components are assembled and integrated. Issues such as performance, metadata management, copy management, data access and systems integration are addressed.
Elements and Objectives of the METIS methodology Design and Build – The physical design of the DW database and the data staging strategy are completed during this stage and the first iteration of data is populated to the target platform. Data Exploitation – By now it is possible to commence the deployment of the applications that are envisioned in the strategy model. Implementation Administration & operations
Evolutionary Cycle of Metis methodology 4. Infrastructure
3. Architecture 2. Business Strategy
5. Design and Build
Evolut ionary Cycles
1. Readiness
6. Exploitation
8. Administration 7. Implementation
Reasons for project failure Technical
An inability to scale the DW, with reference to the volume of data and/or the number of users. An inability to extend the DW infrastructure to accommodate additional application or technology module An inability to effectively manage the exchange of metadata between the different tool components of the DW architecture
Reasons for project failure Technical
An inability to provide real business applications to users as a basis for delivering early ‘hits’ for the project An inability to deliver a flexible and robust DW architecture that provides the basis for flexible application deployment Failure to take advantages of the discoveries made by the DW because of the inflexibility or/and instability of the legacy systems.
Reasons for project failure Non-Technical
Failure to engage the business users in a real commitment to the project Failure to provide a compelling cost/benefit analysis to the board Failure to specify the application requirements of the DW Failure to change the organizational culture and make the enterprise information-aware Failure to take advantages of the discoveries made by the data warehouse because of organizational inertia.
The project team structure Project Sponsor
Subject-area Sponsor
IT Architect
Data Acquisition
Project Manager
Data Management
Application Developmen
Project Charter The project charter represents an agreement between all those who are party to the project. The charter underscores the project plan and is a statement of all of the underlying assumptions of the project. The charter is a written agreement and would normally be supported by a project plan Gantt chart or Pert chart.
Project Charter A typical project charter would include information under the following heads: Project roles and responsibilities Budgets Resources High-level timescales Project management methodology Statement of project objectives Statement of project principles Statement of project scope Risk management process Change management process
Recipe for a Successful Warehouse
For a Successful Warehouse
From day one establish that warehousing is a joint user/builder project Establish that maintaining data quality will be an ONGOING joint user/builder responsibility Train the users one step at a time Consider doing a high level corporate data model in no more than three weeks
For a Successful Warehouse Look closely at the data extracting, cleaning, and loading tools Implement a user accessible automated directory to information stored in the warehouse Determine a plan to test the integrity of the data in the warehouse From the start get warehouse users in the habit of 'testing' complex queries
For a Successful Warehouse Coordinate system roll-out with network administration personnel When in a bind, ask others who have done the same thing for advice Be on the lookout for small, but strategic, projects Market and sell your data warehousing systems
Data Warehouse Pitfalls
You are going to spend much time extracting, cleaning, and loading data Despite best efforts at project management, data warehousing project scope will increase You are going to find problems with systems feeding the data warehouse You will find the need to store data not being captured by any existing system You will need to validate data not being validated by transaction processing systems
Data Warehouse Pitfalls
Some transaction processing systems feeding the warehousing system will not contain detail Many warehouse end users will be trained and never or seldom apply their training After end users receive query and report tools, requests for IS written reports may increase Your warehouse users will develop conflicting business rules Large scale data warehousing can become an exercise in data homogenizing
Data Warehouse Pitfalls
'Overhead' can eat up great amounts of disk space The time it takes to load the warehouse will expand to the amount of the time in the available window... and then some Assigning security cannot be done with a transaction processing system mindset You are building a HIGH maintenance system You will fail if you concentrate on resource optimization to the neglect of project, data, and customer management issues and an understanding of what adds value to the customer
Three targets for DW
Target – fundamental purpose or objective of DW initiative A point solution for single or a few applications Infrastructure that supports multiple and current future applications Organizational transformation that fundamentally changes how the organization competes in the marketplace
A Point Solution
World Bank
Infrastructure
3M
3M
3M
Organizational Transformation
Harrah’s Entertainment
Harrah’s Entertainment
FAC
FAC
FAC
All Targets Can Bring Success
What it takes to be successful? External market/internal focus – FAC, Harrah’s. World Bank – internal focus, 3M – both internal & external Source of sponsorship –
What it takes to be successful?
Resources & Approval processes
What it takes to be successful?
Potential impacts:
Critical Success Factors Information – DW should have the means of converting mass of data into information. Ease of Access – based on user community Data Standards – corporate standards : data definitions, data quality, data access, data exploitation and data presentation. Dedicated resource – exclusively to queries and DSS.
Critical Success Factors Adequate Performance – DW technologies, components, physical schemas: optimizing the performance of the system as a whole. Corporate Sponsorship – pan-corporate project Operationally Stable – stable & safe : performance, availability, security, and metadata management
Critical Success Factors Agreed infrastructure – support DSS New user culture – users need to change their business culture to be fully aligned with the possibilities of Data Warehousing Technology. Source data – sourced inside and outside the enterprise
DW Project Management Principles
Strategic focus – supporting the known strategic objective of the enterprise. Executive sponsorship – sponsorship and support are needed at the most senior levels Goal and benefits-oriented approach – benefits are unforeseen but the benefits should be expressed in general terms in order to provide clear goals for the project Communication Enterprise survey – high level survey of entire enterprise Application delivery – the first, and each successive, iteration of the data warehouse should be based on the deployment of clearly defined application functionality.
DW Project Management Principles Iterative deployment – The deployment of each phase of the warehouse should be based on an evolutionary model of system deployment. Opportunistic – flexible to detour in pursuit of business oriented opportunities that arise during the course of the project Rapid deployment of first iteration Pragmatism – combination of top-down , bottom-up approach
Data Warehousing Design and Construction – METIS METHODOLOGY
There are eight stages defined by METIS methodology
Elements and Objectives of the METIS methodology
Organizational readiness Assessment – assessing the key business drivers, the organizational culture, the legacy systems, and data quality and the skills required. Business Strategy Definition – capturing the business vision for data exploitation and should result in a comprehensive business strategy model for this purpose. Data Warehousing Architecture Definition – logical architecture and the scope of the warehouse Data Warehousing Infrastructure Design – physical infrastructure components are assembled and integrated. Issues such as performance, metadata management, copy management, data access and systems integration are addressed.
Elements and Objectives of the METIS methodology Design and Build – The physical design of the DW database and the data staging strategy are completed during this stage and the first iteration of data is populated to the target platform. Data Exploitation – By now it is possible to commence the deployment of the applications that are envisioned in the strategy model. Implementation Administration & operations
Evolutionary Cycle of Metis methodology 4. Infrastructure
3. Architecture 2. Business Strategy
5. Design and Build
Evolut ionary Cycles
1. Readiness
6. Exploitation
8. Administration 7. Implementation
Reasons for project failure Technical
An inability to scale the DW, with reference to the volume of data and/or the number of users. An inability to extend the DW infrastructure to accommodate additional application or technology module An inability to effectively manage the exchange of metadata between the different tool components of the DW architecture
Reasons for project failure Technical
An inability to provide real business applications to users as a basis for delivering early ‘hits’ for the project An inability to deliver a flexible and robust DW architecture that provides the basis for flexible application deployment Failure to take advantages of the discoveries made by the DW because of the inflexibility or/and instability of the legacy systems.
Reasons for project failure Non-Technical
Failure to engage the business users in a real commitment to the project Failure to provide a compelling cost/benefit analysis to the board Failure to specify the application requirements of the DW Failure to change the organizational culture and make the enterprise information-aware Failure to take advantages of the discoveries made by the data warehouse because of organizational inertia.
The project team structure Project Sponsor
Subject-area Sponsor
IT Architect
Data Acquisition
Project Manager
Data Management
Application Developmen
Project Charter The project charter represents an agreement between all those who are party to the project. The charter underscores the project plan and is a statement of all of the underlying assumptions of the project. The charter is a written agreement and would normally be supported by a project plan Gantt chart or Pert chart.
Project Charter A typical project charter would include information under the following heads: Project roles and responsibilities Budgets Resources High-level timescales Project management methodology Statement of project objectives Statement of project principles Statement of project scope Risk management process Change management process
Recipe for a Successful Warehouse
For a Successful Warehouse
From day one establish that warehousing is a joint user/builder project Establish that maintaining data quality will be an ONGOING joint user/builder responsibility Train the users one step at a time Consider doing a high level corporate data model in no more than three weeks
For a Successful Warehouse Look closely at the data extracting, cleaning, and loading tools Implement a user accessible automated directory to information stored in the warehouse Determine a plan to test the integrity of the data in the warehouse From the start get warehouse users in the habit of 'testing' complex queries
For a Successful Warehouse Coordinate system roll-out with network administration personnel When in a bind, ask others who have done the same thing for advice Be on the lookout for small, but strategic, projects Market and sell your data warehousing systems
Data Warehouse Pitfalls
You are going to spend much time extracting, cleaning, and loading data Despite best efforts at project management, data warehousing project scope will increase You are going to find problems with systems feeding the data warehouse You will find the need to store data not being captured by any existing system You will need to validate data not being validated by transaction processing systems
Data Warehouse Pitfalls
Some transaction processing systems feeding the warehousing system will not contain detail Many warehouse end users will be trained and never or seldom apply their training After end users receive query and report tools, requests for IS written reports may increase Your warehouse users will develop conflicting business rules Large scale data warehousing can become an exercise in data homogenizing
Data Warehouse Pitfalls
'Overhead' can eat up great amounts of disk space The time it takes to load the warehouse will expand to the amount of the time in the available window... and then some Assigning security cannot be done with a transaction processing system mindset You are building a HIGH maintenance system You will fail if you concentrate on resource optimization to the neglect of project, data, and customer management issues and an understanding of what adds value to the customer
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