By Ken W. Collier
Utilizing Agile equipment, you could carry some distance better innovation, price, and caliber to any facts warehousing (DW), company intelligence (BI), or analytics venture. despite the fact that, traditional Agile equipment has to be conscientiously tailored to handle the original features of DW/BI initiatives. In Agile Analytics, Agile pioneer Ken Collier exhibits how one can just do that.
Collier introduces platform-agnostic Agile ideas for integrating infrastructures along with assorted operational, legacy, and uniqueness structures that blend advertisement and customized code. utilizing operating examples, he exhibits tips on how to deal with analytics improvement groups with extensively different ability units and the way to help huge, immense and fast-growing info volumes. Collier’s strategies supply optimum worth no matter if your initiatives contain “back-end” facts administration, “front-end” enterprise research, or both.
Part I specializes in Agile undertaking administration ideas and supply group coordination, introducing middle practices that form the best way your Agile DW/BI venture neighborhood can collaborate towards success
Part II provides technical tools for permitting non-stop supply of commercial worth at production-quality degrees, together with evolving more desirable designs; test-driven DW improvement; model keep an eye on; and venture automation
Collier brings jointly confirmed ideas you could follow correct now—whether you’re an IT decision-maker, information warehouse expert, database administrator, enterprise intelligence expert, or database developer. together with his support, you could mitigate undertaking danger, enhance enterprise alignment, in attaining higher results—and rejoice alongside the best way.
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Additional resources for Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing (Agile Software Development Series)
While you and I may appreciate the difficulty of complex data architectures, elegant data models, efficient ETL scripts, and so forth, users generally couldn’t care less about these things. What users of DW/BI systems care about is the presentation of and access to information that helps them either solve a business problem or make better business decisions. Every iteration must produce at least one new user-valued feature in spite of the fact that user features are just the tip of the architectural iceberg that is a DW/BI system.
Jim Highsmith also introduced me to Scott Ambler along the way. Scott has led the charge in applying Agile to data-centric systems development. com Web site. I have benefited greatly from the conversations I’ve had with him, as well as from his writings on Agile Modeling, Agile Data, Agile Unified Process, and Database Refactoring (together with Pramod Sadalage). In the early days of my focus on Agile in DW/BI, Scott and I regularly lamented our perceptions that the data community wasn’t paying attention to the benefits of agility, while the software community wasn’t paying attention to the unique challenges of database development and systems integration.
Organizations often set out to build an enterprise data warehouse, or at least a broad-reaching data mart, which makes the process more complex. Domain knowledge versus subject matter expertise. DW/BI practitioners often have extensive expertise in business intelligence but not in the organization’s business domain, causing gaps in WHY DO WE NEED AGILE A NALYTICS ? 17 understanding. Business users typically don’t know what they can, or should, expect from a DW/BI system. Unrealistic expectations.
Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing (Agile Software Development Series) by Ken W. Collier