Agile Data Warehouse Design: How to Create Dimensional Models that Deliver Business Value
- Definition: What is agile data warehouse design and how it differs from traditional approaches - Benefits: What are the advantages of agile data warehouse design for business intelligence H2: How to Apply Agile Data Warehouse Design? - Methodology: What are the steps and principles of agile data warehouse design - Tools: What are the tools and techniques for agile data warehouse design, such as BEAM, modelstorming, 7Ws, storyboarding, visual modeling, etc. - Examples: What are some real-world examples of agile data warehouse design projects and their outcomes H2: How to Learn Agile Data Warehouse Design? - Resources: What are the best books, courses, blogs, podcasts, etc. to learn agile data warehouse design - Community: What are the online forums, groups, events, etc. to connect with other agile data warehouse design practitioners and experts - Certification: What are the options and requirements for getting certified in agile data warehouse design H1: Conclusion - Summary: What are the main points and takeaways from the article - Call to action: What are the next steps for the readers who want to learn more or apply agile data warehouse design Table 2: Article with HTML formatting What is Agile Data Warehouse Design?
Data warehousing is the process of collecting, integrating, transforming, and storing data from various sources for business intelligence (BI) purposes. BI is the process of analyzing data to gain insights and support decision making. Data warehousing and BI are essential for any organization that wants to leverage data as a strategic asset and gain a competitive edge in the market.
Agile Data Warehouse Design - Collaborative Dimensional Modeling, from Whiteboard to Star Schema PDF
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However, traditional data warehousing approaches often face challenges such as long development cycles, high costs, low quality, poor performance, and lack of alignment with business needs. These challenges can result in data warehouses that are rigid, complex, outdated, and irrelevant for BI users.
This is where agile data warehouse design comes in. Agile data warehouse design is a step-by-step guide for capturing data warehousing/BI requirements and turning them into high performance dimensional models in the most direct way: by modelstorming (data modeling + brainstorming) with BI stakeholders. It describes BEAM, an agile approach to dimensional modeling, for improving communication between data warehouse designers, BI stakeholders and the whole DW/BI development team.
Dimensional modeling is a technique for designing data warehouses that organizes data into facts (measures) and dimensions (attributes). Facts are numerical values that represent business events or processes, such as sales amount or order quantity. Dimensions are descriptive values that provide context for facts, such as product name or customer location. Dimensional models are often represented by star schemas, which consist of a central fact table surrounded by dimension tables.
Agile data warehouse design differs from traditional approaches in several ways:
It focuses on business events and processes rather than entities and relationships
It uses data stories rather than use cases or user stories to capture requirements
It uses the 7Ws (who, what, when, where, how many, why and how) rather than crow's feet or UML notation to describe details
It uses sketching rather than software tools to create visual models
It uses iterative rather than waterfall development cycles
It uses feedback rather than documentation to validate models
It uses patterns rather than rules to solve common problems
The benefits of agile data warehouse design are manifold:
It reduces development time and cost by eliminating unnecessary complexity and rework
It improves quality and performance by ensuring optimal design and implementation
It increases alignment and collaboration by involving business users throughout the process
It enhances usability and adoption by delivering data warehouses that meet the needs and expectations of BI users
How to Apply Agile Data Warehouse Design?
The methodology of agile data warehouse design consists of four phases: discover, design, develop, and deploy. Each phase has a set of activities and deliverables that are guided by the following principles:
Think dimensionally from the outset
Model by example, not abstraction
Storyboard the data warehouse to plan iterative development
Sketch timelines, charts and grids to model complex process measurement
Enhance star schemas with dimensional shorthand notation
Solve difficult DW/BI problems with proven dimensional design patterns
The tools and techniques for agile data warehouse design include:
BEAM: Business Event Analysis & Modeling, an agile approach to dimensional modeling that uses data stories, 7Ws, visual models, and patterns to capture and communicate requirements
Modelstorming: data modeling that is quicker, more inclusive, more productive, and frankly more fun! It involves data warehouse designers and BI stakeholders working together in interactive sessions to create and validate models using whiteboards, sticky notes, and sketches
Data stories: narratives that describe business events or processes using the 7Ws. They are used to elicit and document requirements, as well as to test and demonstrate models. Data stories can be organized into themes that represent different aspects or perspectives of the business domain
Storyboarding: a technique for planning the data warehouse development by identifying the conformed dimensions and facts that are shared across different data stories. It helps to prioritize the most important and common data elements and to define the scope and sequence of iterations
Visual modeling: a technique for creating simple and intuitive diagrams that represent complex data structures and relationships. It uses timelines, charts, and grids to show how facts and dimensions vary over time, space, and other dimensions
Dimensional shorthand notation: a notation that enhances star schemas with symbols and annotations that indicate the type, role, hierarchy, granularity, cardinality, and format of dimensions and facts. It helps to document and communicate the design decisions and assumptions made during modeling
Dimensional design patterns: reusable solutions for common DW/BI problems, such as handling slowly changing dimensions, accumulating snapshots, multivalued dimensions, bridge tables, outriggers, degenerate dimensions, role-playing dimensions, junk dimensions, mini-dimensions, etc.
The examples of agile data warehouse design projects and their outcomes are numerous and diverse. Some of them are:
A global retailer that used agile data warehouse design to deliver a customer-centric data warehouse that enabled cross-channel analysis and personalization
A healthcare provider that used agile data warehouse design to create a clinical data warehouse that supported quality improvement and patient safety initiatives
A financial services company that used agile data warehouse design to build a risk management data warehouse that complied with regulatory requirements and provided timely and accurate reporting
A media company that used agile data warehouse design to develop a content management data warehouse that facilitated content creation and distribution across multiple platforms
A manufacturing company that used agile data warehouse design to implement a supply chain data warehouse that optimized inventory management and demand forecasting
How to Learn Agile Data Warehouse Design?
The resources for learning agile data warehouse design are plentiful and varied. Some of the best ones are:
Books: Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema by Lawrence Corr and Jim Stagnitto, The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling by Ralph Kimball and Margy Ross, Agile Data Warehousing Project Management: Business Intelligence Systems Using Scrum by Ralph Hughes
Courses: Agile Data Warehouse Design Workshop by DecisionOne Consulting, Dimensional Modeling in Depth by Kimball Group, Agile Data Warehousing with Scrum by Agile DW Project Management
Blogs: BEAM Blog by Lawrence Corr, Kimball Group Blog by Kimball Group, Agile Data Warehousing Blog by Ralph Hughes
Podcasts: Dimensional Modeling Conversations by Lawrence Corr, The Kimball Group Reader Podcast by Ralph Kimball, Agile Data Warehousing Podcast by Ralph Hughes
The community for agile data warehouse design is active and supportive. Some of the ways to connect with other practitioners and experts are:
by Kimball Group, Agile Data Warehousing Forum by Agile DW Project Management
Groups: Agile Data Warehouse Design LinkedIn Group by DecisionOne Consulting, Kimball Group LinkedIn Group by Kimball Group, Agile Data Warehousing LinkedIn Group by Agile DW Project Management
Events: Agile Data Warehouse Design Conference by DecisionOne Consulting, Kimball University Courses and Events by Kimball Group, Agile Data Warehousing Meetups by Agile DW Project Management
The certification for agile data warehouse design is optional but beneficial. It can help to demonstrate your knowledge and skills in the field and to advance your career. Some of the options and requirements for getting certified are:
BEAM Certified Practitioner (BCP): a certification offered by DecisionOne Consulting that validates your ability to apply BEAM methodology and tools for agile data warehouse design. To get certified, you need to attend a BEAM workshop and pass an online exam
Certified Data Vault 2.0 Practitioner (CDVP2): a certification offered by Data Vault Alliance that verifies your proficiency in using Data Vault 2.0, an agile data modeling technique that complements dimensional modeling. To get certified, you need to complete a CDVP2 course and pass an online exam
Certified ScrumMaster (CSM): a certification offered by Scrum Alliance that confirms your understanding and practice of Scrum, an agile project management framework that can be used for data warehousing projects. To get certified, you need to attend a CSM course and pass an online test
Conclusion
Agile data warehouse design is a modern and effective way to create data warehouses that deliver value and satisfaction to BI users. It is based on dimensional modeling, a proven technique for designing data warehouses that are easy to understand and fast to query. It also incorporates agile principles and practices, such as collaboration, iteration, feedback, and simplicity, to ensure alignment with business needs and quality of results.
If you want to learn more about agile data warehouse design, you can start by reading the book Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema by Lawrence Corr and Jim Stagnitto. It is a comprehensive and practical guide that covers all the aspects of agile data warehouse design, from methodology and tools to examples and patterns. You can also check out the other resources and community mentioned in this article.
If you want to apply agile data warehouse design, you can start by modelstorming with your BI stakeholders using data stories and the 7Ws. You can also use sketching and dimensional shorthand notation to create visual models that communicate your design decisions and assumptions. You can also use storyboarding to plan your iterative development and use dimensional design patterns to solve common problems.
If you want to get certified in agile data warehouse design, you can choose from the options listed in this article. You can also combine different certifications to enhance your credentials and expertise.
Agile data warehouse design is not only a skill but also a mindset. It requires you to think dimensionally from the outset, model by example not abstraction, sketch timelines charts and grids, enhance star schemas with dimensional shorthand notation, and solve difficult DW/BI problems with proven dimensional design patterns. By doing so, you will be able to create data warehouses that are agile, dimensional, and awesome!
FAQs - Q: What is the difference between agile data warehouse design and traditional data warehouse design? - A: Agile data warehouse design is a step-by-step guide for capturing data warehousing/BI requirements and turning them into high performance dimensional models in the most direct way: by modelstorming (data modeling + brainstorming) with BI stakeholders. Traditional data warehouse design is a more rigid and formal process that involves gathering requirements from documents or interviews, creating logical and physical models using software tools, and following a waterfall development cycle. - Q: What are the benefits of agile data warehouse design? - A: The benefits of agile data warehouse design are: it reduces development time and cost by eliminating unnecessary complexity and rework; it improves quality and performance by ensuring optimal design and implementation; it increases alignment and collaboration by involving business users throughout the process; it enhances usability and adoption by delivering data warehouses that meet the needs and expectations of BI users. - Q: What are the tools and techniques for agile data warehouse design? - A: The tools and techniques for agile data warehouse design include: BEAM (Business Event Analysis & Modeling), an agile approach to dimensional modeling that uses data stories, 7Ws, visual models, and patterns to capture and communicate requirements; modelstorming, data modeling that is quicker, more inclusive, more productive, and frankly more fun; data stories, narratives that describe business events or processes using the 7Ws; storyboarding, a technique for planning the data warehouse development by identifying the conformed dimensions and facts that are shared across different data stories; visual modeling, a technique for creating simple and intuitive diagrams that represent complex data structures and relationships; dimensional shorthand notation, a notation that enhances star schemas with symbols and annotations that indicate the type, role, hierarchy, granularity, cardinality, and format of dimensions and facts; dimensional design patterns, reusable solutions for common DW/BI problems. - Q: What are some examples of agile data warehouse design projects and their outcomes? - A: Some examples of agile data warehouse design projects and their outcomes are: a global retailer that used agile data warehouse design to deliver a customer-centric data warehouse that enabled cross-channel analysis and personalization; a healthcare provider that used agile data warehouse design to create a clinical data warehouse that supported quality improvement and patient safety initiatives; a financial services company that used agile data warehouse design to build a risk management data warehouse that complied with regulatory requirements and provided timely and accurate reporting; a media company that used agile data warehouse design to develop a content management data warehouse that facilitated content creation and distribution across multiple platforms; a manufacturing company that used agile data warehouse design to implement a supply chain data warehouse that optimized inventory management and demand forecasting. - Q: How can I get certified in agile data warehouse design? - A: You can get certified in agile data warehouse design by choosing from the following options: BEAM Certified Practitioner (BCP), a certification offered by DecisionOne Consulting that validates your ability to apply BEAM methodology and tools for agile data warehouse design; Certified Data Vault 2.0 Practitioner (CDVP2), a certification offered by Data Vault Alliance that verifies your proficiency in using Data Vault 2.0, an agile data modeling technique that complements dimensional modeling; Certified ScrumMaster (CSM), a certification offered by Scrum Alliance that confirms your understanding and practice of Scrum, an agile project management framework that can be used for data warehousing projects. 71b2f0854b