Agile AND Industrial Analytics

By James Taylor, Aug 30, 2010

I wrote a post called “It’s time to industrialize analytics” for Smart Data Collective a little while ago and it prompted Tom Davenport to reply with Agile vs Industrialized.

To recap, the key point of my post was that we need to move away from analytics as a pure craft to one that has a more systematic focus. We need analytic teams that are focused on the end goal, whether that is a high-throughput operational system (a propensity model for use in a web marketing system for instance), a dashboard, report or visualization. Such a focus necessitates limitations on the freedom of the analytic team to use their favorite tools or bring whatever data seems helpful into the model. If we focus on the need to operationalize this model - to make it affect our business - then we will not be able to have total freedom in our analytic work. This is more true when models are being deployed into operational systems than when they are being deployed into more interactive, low-volume environments but it is always true at some level. Rolls Royce cars may be hand made in places but this work is still part of an industrial process - the need for it to fit into a finished product is still paramount. So it is with analytics - even when we are hand-tooling something, we should be aware of the “industrial” context in which we operate.

Tom’s follow-on point that industrialization is not appropriate for analytic discovery work is a valid one. Organizations often don’t know how analytics might be able to improve their business and must spend time and effort in a discovery phase. It is entirely appropriate to try new things, to do things one-off while figuring out what might be helpful. Analytics are not yet, in most companies, a standard part of the way they do business. Even if they are there will be times when the area being investigated is not well known enough to allow for a systematic approach - we will need to be agile about where and how to investigate. But remember, as I said in my original post

If the model is accurate but impractical to implement then it adds no business value and should, therefore, be considered a bad  model.

It does not matter if operationalization means putting the model into a high-volume process, an executive dashboard or sophisticated visualization. If you don’t impact business results then the model is no good. You can, and should, be agile about developing new analytics. But you should keep an eye on the end objective and make sure you can deliver business results.

About the author

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James Taylor is the CEO of Decision Management Solutions. James is the leading expert in Decision Management Systems — systems that are active participants in improving business results. Decision Management Systems applies business rules, predictive analytics and optimization technologies to address the toughest issues facing businesses today, changing the way organizations are doing business. James is passionate helping companies develop agile, analytic and adaptive processes and systems. James has over 20 years developing software and solutions for clients and has led Decision Management efforts for leading companies in insurance, banking, health management and telecommunications.

In addition to strategy and implementation consulting, James is an experienced and highly rated keynote speaker at conferences in the U.S. and around the world. James also regularly runs webinars for clients, and as educational outreach for Decision Management Solutions. James has just completed “Real World Decision Modeling with DMN” with Jan Purchase (Meghan Kiffer, 2016). He previously wrote “Decision Management Systems: A Practical Guide to Business Rules and Predictive Analytics” (IBM Press 2012), “The Microguide to Process and Decision Modeling” with Tom Debevoise, and “Smart (Enough) Systems” (Prentice Hall, 2007) with Neil Raden. He has contributed chapters to several books on business rules and business analytics.