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Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing: Delivering the Promise of Business Intelligence (Agile Software Development) Paperback – 27 Jul 2011

2.5 out of 5 stars 2 customer reviews

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Review

“This book does a great job of explaining why and how you would implement Agile Analytics in the real world. Ken has many lessons learned from actually implementing and refining this approach. Business Intelligence is definitely an area that can benefit from this type of discipline.”

―Dale Zinkgraf, Sr. Business Intelligence Architect

 

“One remarkable aspect of Agile Analytics is the breadth of coverage―from product and backlog management to Agile project management techniques, from self-organizing teams to evolutionary design practices, from automated testing to build management and continuous integration. Even if you are not on an analytics project, Ken’s treatment of this broad range of topics related to products with a substantial data-oriented flavor will be useful for and beyond the analytics community.”

―Jim Highsmith, Executive Consultant, ThoughtWorks, Inc., and author of Agile Project Management

 

“Agile methods have transformed software development, and now it’s time to transform the analytics space. Agile Analytics provides the knowledge needed to make the transformation to Agile methods in delivering your next analytics projects.”

―Pramod Sadalage, coauthor of Refactoring Databases: Evolutionary Database Design

 

“This book captures the fundamental strategies for successful business intelligence/analytics projects for the coming decade. Ken Collier has raised the bar for analytics practitioners―are you up to the challenge?”

―Scott Ambler, Chief Methodologist for Agile and Lean, IBM Rational Founder, Agile Data Method

 

“A sweeping presentation of the fundamentals that will empower teams to deliver high-quality, high-value, working business intelligence systems far more quickly and cost effectively than traditional software development methods.”

―Ralph Hughes, author of Agile Data Warehousing

About the Author

Ken Collier has worked with Agile methods since 2003, and pioneered the integration of Agile methods with data warehousing, business intelligence, and analytics to create the Agile Analytics style. He continues to refine these ideas as technical lead and project manager on several Agile DW/BI project teams. Collier frequently trains DW/BI teams in Agile Analytics, and has been a keynote speaker on the subject at HEDW (Higher Education Data Warehouse) 2011 and multiple TDWI (The Data Warehousing Institute) World Conferences. He is founder and president of KWC Technologies, Inc., and a senior consultant in the Cutter Consortium’s Agile Development and Business Intelligence practice areas.


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Format: Paperback
As with most data professionals I wrestle with how to apply agile principles and practises on large datasets on an ongoing basis. As data warehousing and business intelligence are my particular focus I was drawn to this book. but sadly I cannot recommend it.
* It has nothing to do with analytics
* The majority of it is vanilla agile practises and nothing to do with BI/Data warehousing
* It fails to mention what must surely be the prime concern of any BI system, DATA QUALITY
* It completely misses any data profiling activity or data exploration activity
* Where BI is mentioned it is given a very shallow treatment.
* It scatters references to Inmon and Kimball but fails to weave their work into the books theme or deal with conflicts between the agile method proposed and the methods by the fathers of data warehousing/dimensional modelling.

For example, Kimball warns against building a system to produce a particular report as this produces a stovepipe solution. Model the business process correctly and this will provide the solid foundation on which the report (and reports yet to be conceived) will be built. You don't have to model the foundation for the entire house but you do have to build the foundations in a way that is robust enough and extensible enough to prevent stovepiping. The book does not address or discuss the apparent conflict between its message and Kimball's approach.

There is one useful idea in it and that is using a message based architecture for populating the data warehouse. However it then explicitly describes the use of an entity-attribute-value database to provide an agile method of building a long term system of record.
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Format: Paperback
If your business intelligence team has discussed "going agile", this book can give you practical information to help you get there. It's refreshing to see that business intelligence and analytics professionals can adopt practices typically associated with Java, Ruby, and Objective-C developers.

The book is organized into two sections, management methods and technical methods. Most of the technical methods focus on data modeling and data integration (often referred to as Extract, Transform, and Load, or ETL). While these areas are critical to a successful business intelligence system, my role is most often focused on the presentation layer or BI toolset (such as SAP BusinessObjects). So I personally gravitated toward the first half of the book, management methods.

Ken says more than once that the whole point of agile is to "be agile", not just to "do agile". Unfortunately, "agile" can be overused as the latest management buzzword to dress up "hacking" or "unrealistic deadlines". I was actually surprised to read that agile may not improve delivery times. In the short term, delivery times may increase. But the payoff for agility is projects that more quickly respond to changing requirements and users that receive smaller functional deliveries instead of the "big bang" of the waterfall project death march.

While the book is a well-written and easy to read, I found it necessary to read slowly, chapter by chapter, and reflect on what I had read. The book would easily lend itself to a weekly BI book club, where technicians, users, and management meet weekly to discuss the book one chapter at a time. Recommended reading.
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Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: 4.1 out of 5 stars 14 reviews
9 of 11 people found the following review helpful
5.0 out of 5 stars Agile Development meets Data Warehouse 11 Nov. 2011
By Shane Willerton - Published on Amazon.com
Format: Paperback
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.
4 of 6 people found the following review helpful
4.0 out of 5 stars Learn how to "be agile", not just "do agile" 20 Jan. 2012
By Dallas Marks - Published on Amazon.com
Format: Paperback
If your business intelligence team has discussed "going agile", this book can give you practical information to help you get there. It's refreshing to see that business intelligence and analytics professionals can adopt practices typically associated with Java, Ruby, and Objective-C developers.

The book is organized into two sections, management methods and technical methods. Most of the technical methods focus on data modeling and data integration (often referred to as Extract, Transform, and Load, or ETL). While these areas are critical to a successful business intelligence system, my role is most often focused on the presentation layer or BI toolset (such as SAP BusinessObjects). So I personally gravitated toward the first half of the book, management methods.

Ken says more than once that the whole point of agile is to "be agile", not just to "do agile". Unfortunately, "agile" can be overused as the latest management buzzword to dress up "hacking" or "unrealistic deadlines". I was actually surprised to read that agile may not improve delivery times. In the short term, delivery times may increase. But the payoff for agility is projects that more quickly respond to changing requirements and users that receive smaller functional deliveries instead of the "big bang" of the waterfall project death march.

While the book is a well-written and easy to read, I found it necessary to read slowly, chapter by chapter, and reflect on what I had read. The book would easily lend itself to a weekly BI book club, where technicians, users, and management meet weekly to discuss the book one chapter at a time. Recommended reading.
2 of 3 people found the following review helpful
4.0 out of 5 stars Agile Practices for All Data-Centered System Development 13 Sept. 2012
By Methods & Tools Software Development Magazine - Published on Amazon.com
Format: Paperback
This book aims to provide an adaptation of the Agile development approach to the specific characteristics of Datawarehouse (DW) and Business Intelligence (BI) systems development. The book is well written and well structured. The concepts are illustrated with many anecdotes and examples. An important list of references and further reading material is available at the end of the book. My favorite part is the chapter 6 that deals with evolving design, a key factor for successful agile projects.

I will naturally recommend this book to every developer or manager involved DW and BI projects, but this book has also a much broader appeal. The issues specific DW or BI are not far for every large project, where databases play a major role, as it might be for instance in a mainframe environment. There you usually have to balance the architecture, performance and stability needs expressed on the database and operation sides of your organization with the goal of delivering frequently new working software. With his process of adapting Agile to data analytics, Ken Collier provides also a interesting framework for people that are involved in the transition from a traditional project management structure to an Agile approach.
2 of 3 people found the following review helpful
4.0 out of 5 stars Good Agile book 16 Mar. 2013
By Paul Kosinski - Published on Amazon.com
Format: Paperback Verified Purchase
My manager read this book and recommended it to our data engineering team as a reference before we started using Agile for an Analytics project. I haven't completed the book but appreciate how the author discusses the challenges of software development then compares and contrasts that with database projects which have tended to be late adopters of Agile.
5.0 out of 5 stars Great Read for BI/DW project managers 3 Aug. 2016
By Rosemary Hossenlopp - Published on Amazon.com
Format: Kindle Edition
It provides practical approaches to transition BI & DW projects to Agile. I quickly read it cover to cover as it provided key insights into how to make established BI & DW projects more Agile and therefore more failure-proof.

This book leverages Jim Highsmith’s Agile Project Management (APM) framework based on an Envision ->Explore cycle rather than the Plan -> Do approach. This paradigm shift aims to address uncertainty in our project teams. This uncertainty has been addressed in a programmatic, waterfall way in the past. And Lots of Projects have Failed. Ken provides a very clear and readable book which walks teams through transitioning to Agile. He provides many examples of collaboration between team stakeholders throughout the project life cycle that adapt the Envision -> Explore approach to the amount of change experienced by BI & DW teams.

This book elegantly speaks to both business sponsors and technical team members. For Project Managers it provides some of the best examples of early planning, user stories and use Case Diagrams I have seen for BI/DW projects.

Ken has provided thoughtful insights on how to increase Agility:
- Solid Tools - Needed for BI exploration to increase the speed of team and ability to share insights with users/customers.
- Agile Infrastructure - Realigning systems such as config management and testing infrastructure can be significant, yet often, underestimated part of project planning.
- Agile workmanship - Even with solid tools & infrastructure, there is a learning curve for development teams.
- Agile architecture - Make Up Front Design (UFD) more Agile by leveraging proven architectures or patterns; quickly.
- Customer Commitment - BI projects still require external input into planning, capability definition and feature prioritization. So incorporating user education on how results are pulled, calculated and displayed is very important to allow them to be effective in their roles.
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