By Ken W. Collier
Utilizing Agile tools, you could carry a ways larger innovation, price, and caliber to any facts warehousing (DW), company intelligence (BI), or analytics undertaking. in spite of the fact that, traditional Agile tools needs to be conscientiously tailored to deal with the original features of DW/BI initiatives. In Agile Analytics, Agile pioneer Ken Collier indicates how you can do exactly that.
Collier introduces platform-agnostic Agile ideas for integrating infrastructures together with diversified operational, legacy, and uniqueness platforms that blend advertisement and customized code. utilizing operating examples, he exhibits tips on how to deal with analytics improvement groups with greatly varied ability units and the way to aid huge, immense and fast-growing info volumes. Collier’s options provide optimum price even if your initiatives contain “back-end” information administration, “front-end” enterprise research, or both.
Part I specializes in Agile undertaking administration innovations and supply group coordination, introducing middle practices that form the way in which your Agile DW/BI undertaking group can collaborate towards success
Part II offers technical tools for permitting non-stop supply of commercial worth at production-quality degrees, together with evolving enhanced designs; test-driven DW improvement; model keep an eye on; and venture automation
Collier brings jointly confirmed recommendations you could observe correct now—whether you’re an IT decision-maker, info warehouse expert, database administrator, enterprise intelligence professional, or database developer. along with his support, you could mitigate venture threat, increase company alignment, in achieving higher results—and celebrate alongside the way in which.
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Additional resources for Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing (Agile Software Development Series)
In my years as a DW/BI consultant and practitioner I have learned three consistent truths: Building successful DW/BI systems is hard; DW/BI development projects fail very often; and it is better to fail fast and adapt than to fail late after the budget is spent. First Truth: Building DW/BI Systems Is Hard If you have taken part in a data warehousing project, you are aware of the numerous challenges, perils, and pitfalls. Ralph Kimball, Bill Inmon, and other DW/BI pioneers have done an excellent job of developing reusable architectural patterns for data warehouse and DW/BI implementation.
Measures of progress and success must focus more on value delivery than on traditional metrics of on schedule, on budget, and to spec. ” This statement reflects the notion that incrementally evolving a system by frequently seeking and adapting to customer feedback will result in building the right solution, but it may not be the solution that was originally planned. The Difficulties of Agile Analytics Applying Agile methods to DW/BI is not without challenges. Many of the project management and technical practices I introduce in this book are 6.
The keyword here is sufficient. Sufficiency implies that there is a legitimate purpose for the document, and when that purpose is served, there is no need for additional documentation. In my work with teams that are learning and adopting the Agile DW/BI development style, I often find that they are looking for a prescriptive methodology that makes it very clear which practices to apply and when. This is a natural inclination for new Agile practitioners, and I will provide some 13 DATA WAREHOUSING A RCHITECTURES AND SKILL SETS recommendations that may seem prescriptive in nature.