"In embracing Agile data analytics, we seek to do nothing more or less than create a nimble organizational mind."

-- Joseph Feller, Editor

Welcome to the latest installment of Cutter Benchmark Review, where we're exploring the exciting, if slightly daunting, world of Agile data analytics. For me, the "vision" of Agile analytics is obvious enough. From a data management and decision-making perspective, we want our organizations to have the storage capacity of a jumbo cargo ship along with the rapid maneuverability of a jet ski. It's just as obvious to me that this vision is not going to be easy for most firms to achieve. With this goal and challenge in mind, in this issue we set out to learn more about what drives Agile data efforts; the tools, skills, and attitudes that enable them; and the barriers that stand in the way.

For starters, there is good news: we're all experts in this domain; we just don't know it. The cargo/jet ski vision is actually a good description of a complex information system that all of us have carried since birth: our brains. When psychologists measure mental agility, they look at a very particular set of cognitive capabilities. Mental agility is not about information storage; it's about information in use. It is the ability to store and recall memories rapidly, to quickly relate new information to old memories using multiple senses and multiple neural paths, to filter channels of information in a flexible way, to rapidly flip between diverse perspectives in analyzing problems, to apply old knowledge to new contexts (and vice versa), and so on. Sound familiar? It does to me. In embracing Agile data analytics, we seek to do nothing more or less than create a nimble organizational mind.

To help us get there, this issue, like all issues of CBR, goes straight to the horse's mouth: gathering fresh survey data from the professionals engaged in the Agile analytics phenomenon. To make sense of this data, we recruited a diverse team of experts to analyze the survey results. For the first article, as always, we looked at the academic community: researchers with an expertise in the topic who can bring into focus a high-level view on the issues and provide us with a sense of context. For the second article, we sought out the trenches: professionals with deep practical experience who can help us grasp what's involved in rolling up our sleeves and tackling the challenges in practice. So, without further ado, let me introduce this issue's expert panel.

To help us understand the survey data from an academic perspective, we have two authors: Tadhg Nagle and Dave Sammon. I've had the opportunity to collaborate with both of them before through university research and also from editing their excellent contribution to an issue of Cutter IT Journal (Vol. 23, No. 8) that examined business models in the "Web as platform" era.

In addition to their roles as researchers and lecturers with the Business Information Systems group at University College Cork, Ireland, Tadhg and Dave are Co-Directors of the Master's of Science in Data Business for the Irish Management Institute. Since a core component of this executive education program focuses on helping firms leverage data to respond in an Agile fashion to rapidly changing needs and environments, I was delighted when they agreed to work with us on this issue.

Beyond his work with the data business master's program, Tadhg's research on business modeling, social media, innovation management, cloud computing, and data analytics has been well received by the academic and practitioner communities alike. Likewise, Dave has established himself as an influential voice in the decision support and enterprise systems communities, lately focusing on the compelling but slippery issue of "sense making" in a variety of industry sectors.

In their article, Tadhg and Dave begin with a quick discussion of definition. They succinctly define Agile analytics and, more importantly, give us a sense of how the survey population defines this topic. Building on this definition, they provide insight into both the drivers and barriers involved in the pursuit of analytic agility, drawing attention to some key capability gaps and technological dependencies. For the remainder of their article, the contributors make use of a very tidy framework -- the information supply chain -- to structure the survey data and derive some interesting insights into how our respondents deal with data acquisition, data integration, data analysis, delivery of results, and -- most critically -- governance. Based on these insights, they wrap up their article with some direct and practical advice, namely that firms need to put data standards and data quality in the critical path, diversify their technological tool kit, and invest in initiatives to create organization-wide shared meaning.

I'm very excited about our practitioner article, supplied by Sebastian Hassinger, a Senior Consultant with Cutter's Agile Product & Project Management practice. Sebastian has more than 20 years' experience in major global corporations and is also as an entrepreneur. Most importantly, he is not primarily a data analytics professional; he is an Agile development expert. Since in this issue we define Agile data analytics as "the application of Agile methods to data analytics initiatives in order to increase flexibility, provide faster 'time to value,' and support collaborative relationships between business users and IT developers," we were very keen to involve someone with deep experiences in the Agile development space. We were delighted when Sebastian agreed to share his insights as an Agile developer on what our survey reveals about the drivers, barriers, and enablers of "going Agile" in the data analytics domain.

Sebastian starts his article with some insights into why Agile development exists at all, the problems with traditional waterfall-style development, and the parallels in the data world. From there, he derives some key lessons from his startup and innovation experiences, looking at the experimental nature of value creation and the key role played by data analytics. Sebastian concludes his contribution with some solid advice for organizations to invest in skills and capabilities and to brace themselves for the heavy lifting involved in overcoming organizational inertia -- lessons hard learned in the Agile development domain.

Both articles, and the survey data itself, provide plenty of food for thought. I hope you will find this issue useful to your organization as you explore why and, more importantly, how you can help your organization achieve a more Agile mind. Enjoy the read!

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