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James Taylor

I will use this blog to discuss business challenges and how technologies like analytics, optimization and business rules can meet those challenges.

About the author >

James is the CEO of Decision Management Solutions and works with clients to automate and improve the decisions underpinning their business. James is the leading expert in decision management and a passionate advocate of decisioning technologies business rules, predictive analytics and data mining. James helps companies develop smarter and more agile processes and systems and has more than 20 years of experience developing software and solutions for clients. He has led decision management efforts for leading companies in insurance, banking, health management and telecommunications. James is a regular keynote speaker and trainer and he wrote Smart (Enough) Systems (Prentice Hall, 2007) with Neil Raden. James is a faculty member of the International Institute for Analytics.

Cross-posted from JTonEDM

I saw this interesting McKinsey piece recently - Rediscovering the art of selling - McKinsey Quarterly - Retail & Consumer Goods - Strategy & Analysis - and I was struck by the value of analytics in this context. What retailers really need to do, according to McKinsey, is focus on hiring sales people with personality, extroverts motivated by helping customers. And they need to spend time training this folks on sales techniques, approachability, reading body language (to tell who wants to be left alone) and much more. Do this, the article says, and your closing, cross- and up-selling will be far more successful. So far so good.

But the reality of a modern retailer is that there are a tremendous number of products with lots of potential cross- and up-sells to choose from. Even someone with the skills you need might not be good at, say, color matching making it hard for them to make the right clothing choice for an outfit. Add in multiple discounts, loyalty programs and other forms of dynamic pricing and you have a complex environment. Retailers feel that have to invest resources and time in training staff about these things, reducing the time available for sales skills training, and even that they must hire for an ability to understand this complexity even at the expense of the personality and sales skills they need. A case of being between a rock and a hard place?

No, enter analytics. With decent analysis of their historical data and a focus on the decisions that have to happen during the sales process, retailers can spend their time and energy training staff on the sft skills they need and let their systems and analytics do the rest of the work. They can use business rules/analytics and decisioning to answer questions like what discount does this customer get, what's the best up-sell for this customer given their purchases, what's the best cross-sell that will complete the outfit they are buying. They can analyze sales data, loyalty card data and external data and use rules derived from this data or from their best sales people. If they adopt the "swipe first" loyalty card approach they can empower their staff to do even more by leveraging everything they know right at the start of a conversation.

Your staff don't need to be able to make all these "technical" decisions - you can build systems that act as effective advisers to them freeing them to work on their people skills, customer interactions and actually selling.

Posted November 23, 2010 9:37 AM
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A friend sent me a link to a webinar on "The End of BI as We Know It" that promised "A fresh look at what business analytics means". It wasn't clear who was speaking or what company was sponsoring but the title intrigued me (as it was meant to). But when I looked at the body of the description I was underwhelmed. There was, frankly, nothing new. The webinar promised to explain several things - presumably things that were "fresh" or not "BI as we know it". But here's the list (edited to summarize):

  • Speed to deploy, to build, to get analysts serving themselves is critical
  • Must be able to analyze data from production databases and handle millions or billions of records
  • It's critical to combine multiple data sources from the data warehouse to Excel
  • Must be able to easily and quickly build dashboards

All this is pretty mainstream as far as I am concerned - no-one wants a BI tool that is slow, that can't access data from various sources, that can't handle lots of rows or that doesn't let you build dashboards.

And there was nothing about decision making, nothing about supporting different kinds of decision-making (from collaborative, strategic decisions to high-volume operational decisions), nothing about data mining or predictive analytics, nothing that fundamentally changes how companies can put data to work improving their business.

I gave a speech some months back in South Africa called "Does BI Matter" (audio and slides here in a large PDF) and I have blogged before about Why thinking about decisions should be a BI best practice. If you think you can improve day-to-day operations by giving everyone dashboard or reports then you haven't visited your call center lately. If you think that the way your systems work, the way your website works, should not also be improved by applying analytics then you underestimate the extent to which your systems are your business. If you think the time it takes to build reports or the ease with which you can build dashboards are the critical measures of success then you are focused on means and not ends.

The reason you spend money on Business Intelligence is to provide intelligence about your business not just so you can have a BI platform. You have BI to make better decisions, to improve the way you run your business. If you don't know which decisions you are improving then you are unlikely to make progress.

If that list really counts as "fresh" and "the end of BI as we know it" then we should all be worried....

Posted October 7, 2010 9:48 AM
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I was struck today by a short but effective Information Builders PowerPoint - Four Worst and Four Best Practices in Business Intelligence. I really liked the worst practices - especially the one about assuming that business people have the skills or time to learn to use a BI tool. I blogged not long ago about the problem that most people are not very good at math and this is just as true when considering BI more generally as when thinking about data mining and analytics.

It's also true that many of the people targeted by BI tools don't have the time to use drill-down and analysis tools. Think about the folks in the call center - they want answers, not an ability to explore, so that they can finish the call. This is why it is important to think about the decisions involved and who you want making them. Knowing the decision and the decision maker will help you determine if you need BI tools to help decide or analytics and rules to automate that decision. And remember, just because someone passes on the result of a decision does not mean that the same person is qualified to make the decision. A call center representative might be the one to pass on a denial of a refund for instance but you might want someone else to decide which refunds get denied. Automating the decision can allow one person to control how the decision is made while others pass on these decisions.

I was also struck by the worst practice of selecting a BI tool without a specific business need. I spoke about this when I presented in South Africa earlier this year. If the reason you buy a BI tool is just to have BI then you probably aren't helping your company as much as you could be. Understand the business drivers - the decisions that must be made, the reports required for compliance - and you will do better. You can check out the slides and audio from this presentation on my website - Does BI Matter? (large file warning)

And this brings us back to my favorite Best practice - identify your business need upfront. Or, I would say, begin with the decision in mind.

Posted September 9, 2010 6:30 PM
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Posted August 30, 2010 11:59 AM
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There was a great article this week over on the Requirements Network - Data Warehouse / Business Intelligence Requirements Elicitation. Where do You Begin? I really liked the fact that early in the discussion the author said:
After establishing these strategic objectives, make it a priority to get your users talking about their work day, struggles, obstacles, and how they make business decisions as pertains to data (my emphasis).
I think it is essential when working with data to focus on decisions and on how data and analytics might improve those decisions. I also liked the focus on data mining as one of the steps - not just reporting and "soft" analysis tools - though I would add that deploying the results of data mining needs some serious consideration also. In this vein I also wrote about data integration and the importance of keeping the decision in mind.

Posted August 24, 2010 9:46 AM
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