Agile Analytics

Dallas Marks head shot

A lot of software development projects fail, and business intelligence and data warehousing projects can fail spectacularly. In the world of software development, the Agile movement has created some practices aimed at speeding up customer delivery and reducing failures. Author Ken Collier explains how to harness Agile techniques in a business intelligence/data warehousing environment in his book Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing.

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.

An Agile DW/BI team is made up of more than just developers. It includes the customer (user) community, who provide requirements; the business stakeholder community, who are monitoring the impact of the BI system on business improvements; and the technical community, who develop, deploy and support the DW/BI system.

Defined in this way, one of the immediate challenges an organization faces in moving toward agility is that it is not an IT-only exercise. Although BI teams can certainly self-organize and practice agility, those efforts will only go so far without support from management and the user community.

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.

It’s refreshing to see that business intelligence and analytics professionals can adopt practices typically associated with Java, Ruby, and Objective-C developers. If your business intelligence team has discussed “going agile”, this book can give you practical information to help you get there.  Definitely recommended reading.

UPDATE: You may also enjoy reading Agile Analytics and the SAP Information Design Tool – An Introduction by Stuart Wallace. (added May 11, 2012)

Disclosure of Material Connection: I received this book free from the publisher. I was not required to write a positive review. The opinions I have expressed are my own. Some of the links in the post above are “affiliate links.” This means if you click on the link and purchase the item, I will receive an affiliate commission. Regardless, I only recommend products or services I use personally and believe will add value to my readers.I am disclosing this in accordance with the Federal Trade Commission’s 16 CFR, Part 255: “Guides Concerning the Use of Endorsements and Testimonials in Advertising.”

Dallas Marks

Dallas Marks

I am an analytics and cloud architect, author, and trainer. An AWS certified blogger, SAP Mentor Alumni and co-author of the SAP Press book SAP BusinessObjects Web Intelligence: The Comprehensive Guide, I prefer piano keyboards over computer keyboards when not blogging or tweeting.