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Build an Extensible Data Warehouse Foundation
Establish a well-architected core model with just enough oversight and governance.
- Data warehouse implementation is a costly and complex undertaking, and can end up not serving the business' needs appropriately.
- Too heavy a focus on technology creates a data warehouse that isn’t sustainable and ends up with poor adoption.
- Emerging data sources and technologies add complexity to how the appropriate data is made available to business users.
Our Advice
Critical Insight
- A data warehouse is a project; but successful data warehousing is a program. An effective data warehouse requires planning beyond the technology implementation.
- Governance, not technology needs to be the core support system for enabling a data warehouse program.
- Understand business processes at the operational, tactical, and ad hoc levels to ensure a fit-for-purpose DW is built.
Impact and Result
- Leverage an approach that focuses on constructing a data warehouse foundation that is able to address a combination of operational, tactical, and ad hoc business needs.
- Invest time and effort to put together pre-project governance to inform and provide guidance to your data warehouse implementation.
- Develop “Rosetta Stone” views of your data assets to facilitate data modeling.
- Select the most suitable architecture pattern to ensure the data warehouse is “built right” at the very beginning.
Build an Extensible Data Warehouse Foundation Research & Tools
Start here – read the Executive Brief
Read our concise Executive Brief to find out why the data warehouse is becoming an important tool for driving business value, review Info-Tech’s methodology, and understand the four ways we can support you in completing this project.
1. Prepare for the data warehouse foundation project
Begin the data warehouse foundation by defining the project and governance teams, as well as reviewing supporting data management practices.
2. Establish the business drivers and data warehouse strategy
Using the business activities as a guide, develop a data model, data architecture, and technology plan for a data warehouse foundation.
3. Plan for data warehouse governance
Start developing a data warehouse program by defining how users will interact with the new data warehouse environment.
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Develop a Predictive Analytics Plan
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Available Soon
Develop a Predictive Analytics Plan
Check back soon to watch this webinar on demand.
About Info-Tech
Info-Tech Research Group is the world’s fastest-growing information technology research and advisory company, proudly serving over 30,000 IT professionals.
We produce unbiased and highly relevant research to help CIOs and IT leaders make strategic, timely, and well-informed decisions. We partner closely with IT teams to provide everything they need, from actionable tools to analyst guidance, ensuring they deliver measurable results for their organizations.
What Is a Blueprint?
A blueprint is designed to be a roadmap, containing a methodology and the tools and templates you need to solve your IT problems.
Each blueprint can be accompanied by a Guided Implementation that provides you access to our world-class analysts to help you get through the project.
Need Extra Help?
Speak With An Analyst
Get the help you need in this 3-phase advisory process. You'll receive 7 touchpoints with our researchers, all included in your membership.
Guided Implementation 1: Prepare for the data warehouse foundation project
- Call 1: Discuss structuring your project team, defining success metrics and risks, and organizing a steering committee.
- Call 2: Discuss the impacts of other data management practices on your data warehouse foundation project.
Guided Implementation 2: Establish the business drivers and data warehouse strategy
- Call 1: Walk through how to characterize operational, tactical, and ad hoc business processes that will guide the data warehouse.
- Call 2: Discuss the four “Rosetta Stones” for data modeling.
- Call 3: Develop an architecture strategy based on business and data needs, and review the data warehouse vendor landscape.
Guided Implementation 3: Plan for data warehouse governance
- Call 1: Plan for the formation of a data warehouse center of excellence.
- Call 2: Discuss defining standard operating procedures and service-level agreements for the data warehouse.
Authors
Daniel Ko
Kolade Odetoyinbo
Contributors
- Chris Debo, Senior Manager, Schneider Downs & Co., Inc.
- Jaison Dominic, Lead Architect, Enterprise Data Warehouse, Moffitt Cancer Center
- Liselle Ramcharan, Project Manager, TD Insurance
- Randy Piscione, Enterprise Data Architect, BMO Financial Group
- Sree Pulapaka, Director of Enterprise Business Innovation and Analytics, Metropolitan Washington Airports Authority
- 1 anonymous contributor
Related Content: Big Data
Search Code: 75707
Last Revised: July 29, 2016