What are some of the common data warehousing mistakes?
• Not implementing a comprehensive meta data strategy
• Not deploying a centralized warehouse administration tool
• Not cleaning or intergrating transactional data
• Expecting the warehouse to stay static
• Underestimating refresh and update cycles
• Using a poor definition and approach
• Poor design and data modeling
• Using inexperienced personnel
There are a lot of data warehouse horror stories; however, there are also a lot of phenomenal success stories. What are the keys to a successful implementation?
• Executive sponsorship is a must.
• A full-time project team with experienced staff is necessary.
• Both IT and business units must be involved in the project.
• Business analysts who understand the business objective as well as the data warehouse and the data mining technology must be involved.
• The project's scope must be focused and achievable.
• Activities must support the business goals.
• An iterative approach must be used to build, test, and implement the solution.
• Proven technology components must be used.
• Data quality is a priority.
• Think globally. Act locally.
• Implement short term. Plan long term.
Now let's look at some data warehousing "Best Practices":
• Transactional systems flow up to a consolidating layer where cleansing, integration, and alignment occur.
This Operational Data Store (ODS) layer feeds a dimensionally modeled data warehouse, which typicallyfeeds application or departmentalized data marts.
• Data definitions are consistent, data is cleaned, and a clear understanding of a single system of record exists— "one version of the truth."
• Meta data standards and systems are deployed to ease the change process. All new systems are meta data driven for cost, speed, and flexibility.
• Technology complexity of databases is hidden by catalog structures. Clean interfaces to standard desktop productivity tools. Self-service is set up for end users with business meta data, so they can get their own data with easy-to -use tools.
As in data mining and model development, building and implementing a data warehouse require careful planning, dedicated personnel, and full company support. A well-designed data warehouse provides efficient access to multiple sources of internal data.
• Not implementing a comprehensive meta data strategy
• Not deploying a centralized warehouse administration tool
• Not cleaning or intergrating transactional data
• Expecting the warehouse to stay static
• Underestimating refresh and update cycles
• Using a poor definition and approach
• Poor design and data modeling
• Using inexperienced personnel
There are a lot of data warehouse horror stories; however, there are also a lot of phenomenal success stories. What are the keys to a successful implementation?
• Executive sponsorship is a must.
• A full-time project team with experienced staff is necessary.
• Both IT and business units must be involved in the project.
• Business analysts who understand the business objective as well as the data warehouse and the data mining technology must be involved.
• The project's scope must be focused and achievable.
• Activities must support the business goals.
• An iterative approach must be used to build, test, and implement the solution.
• Proven technology components must be used.
• Data quality is a priority.
• Think globally. Act locally.
• Implement short term. Plan long term.
Now let's look at some data warehousing "Best Practices":
• Transactional systems flow up to a consolidating layer where cleansing, integration, and alignment occur.
This Operational Data Store (ODS) layer feeds a dimensionally modeled data warehouse, which typicallyfeeds application or departmentalized data marts.
• Data definitions are consistent, data is cleaned, and a clear understanding of a single system of record exists— "one version of the truth."
• Meta data standards and systems are deployed to ease the change process. All new systems are meta data driven for cost, speed, and flexibility.
• Technology complexity of databases is hidden by catalog structures. Clean interfaces to standard desktop productivity tools. Self-service is set up for end users with business meta data, so they can get their own data with easy-to -use tools.
As in data mining and model development, building and implementing a data warehouse require careful planning, dedicated personnel, and full company support. A well-designed data warehouse provides efficient access to multiple sources of internal data.
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