One Minute Bottom Line
|Agile Analytics is an excellent introduction to the broad spectrum of Agile methodology as well as being a timely and appropriate adaptation to Business Intelligence and Data Warehousing. Agile Analytics is packed with real-world scenarios to aid a Business Intelligence project in making the transition from 'doing Agile' and 'being Agile.' This is both a good introduction to Agile techniques for Data Warehousing projects as well as an excellent reference for avoiding the common pitfalls and misconceptions associated with Agile techniques. I recommend this book for any one associated with a BI/DW project from project sponsor, developer and project manager.
The Data Warehousing development arena lags behind the application development area as far as adopting project management and development techniques. Some of the common excuses to not even look at Agile adoption include: Data Warehousing is fundamentally different than application development; The first thing to get abandoned with Agile is data integrity and documentation; Agile is just an excuse to scrap architecture and planning; Agile is just another word for developer laziness. Those are just some of the excuses that I have used recently.
I have seen too many 'Agile' projects that have been abandoned after twenty or more three-week iterations when the final mess that was produced was unusable and a maintenance nightmare.
After reading Agile Analytics, however, I am beginning to understand what the author means by the difference between 'doing Agile' and 'being Agile'. Agile techniques, on their own, are not a replacement for Data Warehousing methodology, but rather a complement.
On the other side of the Agile fence, I have been involved in several large projects utilizing the waterfall project management strategy that suffered from inevitable scope creep; missed deadlines; missed requirements; building throw-away products that will never provide value just to meet an arbitrary deadline.
The first section of Agile Analytics is geared more to a generalized audience in that it introduces the reader to the broad spectrum of Agile literature and how it applies to Data Warehousing.
The second section is geared more to the Agile team members in that it provides them with the tools and frameworks for adjusting on a daily basis to the dynamism and challenges associated with Agile techniques.
The premise that software development should have all requirements defined and well-understood before any development begins is out-dated. The 'Big Design Up Front' assumes that no challenges will arise in development, with data quality or that the consumers of the data warehouse won't come to a more advanced understanding of the business. Agile techniques require a partnership with the business and the development staff with constant and frequent feedback and continuous involvement in evolving requirements.
The emphasis on fulfilling User Stories rather than project dates and Test-Driven-Development can go along way to addressing the data warehouse planning issues. The author does an excellent job of pulling the best practises of the Agile development movement and adapting it for more data-oriented projects.
I strongly recommend this book, both as an introduction to Agile for BI/DW and as a reference for the practical tools for a day-to-day adjustment of a truly Agile project. This book can make the difference between 'doing Agile' and truly 'being Agile.' I am passing this book around my team to see how we can better provide concrete business value to our internal and external data consumers.