Agile AND Industrial Analytics
By James Taylor, Aug 30, 2010
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
James Taylor is the CEO and a Principal Consultant of Decision Management Solutions. He is the leading expert in how to use decision modeling, business rules, and analytic technology to build Decision Management Systems. James is passionate about helping companies improve decision-making and develop an agile, analytic, and adaptive business. He provides strategic consulting to companies of all sizes, working with clients in all sectors to adopt decision making technology. James has spent the last 20 years developing approaches, tools, and platforms that others can use to build more effective information systems. He has led Decision Management efforts for leading companies in insurance, banking, health management, and telecommunications.
James is the author of “Decision Management Systems: A practical guide to using business rules and predictive analytics” (IBM Press, 2011) and of Process and Decision Modeling with BPMN/DMN with Tom Debevoise. He previously wrote Smart (Enough) Systems: How to Deliver Competitive Advantage by Automating Hidden Decisions (Prentice Hall) with Neil Raden, and has contributed chapters on Decision Management to multiple books as well as many articles to magazines. In addition to strategy and implementation consulting, James delivers keynotes, webinars, workshops, and training.
James was previously a Vice President at FICO where he developed and refined the concept of decision management. The best-known proponent of the approach, James helped create the emerging Decision Management market and is a passionate advocate of decision management. He understands how companies buy and use these technologies, and he has helped companies successfully adopt these technologies and apply them in the context of Business Process Management and Business Intelligence initiatives.