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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.

October 2009 Archives

I am at the Premier Business Leadership Series, SAS/BetterManagement.com's event, and I got to attend a great panel on Analytics in the Executive Suite. Barbara Pindar of Aeropostale, Eric Webster of State Farm Insurance, Cameron Davies of Disney and Keith Collins of SAS made up the panel. Each panelist gave a quick introduction:

  • Eric is in State Farm's marketing arm and is focused on using analytics for demand generation - where is the next customer going to come from. They have noticed that while you can create more and more analytics you have to think about who will consume them. Their have had great success by burying analytics into a transactional system - embed the intelligence  in systems that everyone touches. Decision Management in other words. Great example of giving agents a system that lets them specify their marketing budget and get an analytically driven marketing plan back.
  • Barbara talked about Aeropostale where it is about having the right product. They have focused on marrying the art of designing products with the science of predicting what will work. Their "artists" value the science, which is critical. A lot of upfront analytics to make the initial purchases right - make sure that customers can come into the store to find what you want, the right size and color.
  • Cameron from Disney got the attention of his management team when they showed how analytics, applied to just 30-40% of the business, impact the earnings per share of Disney. Now they are working with non-theme park elements of the company. Advertising is an interesting area because there are agencies, multiple companies involved but you need to target consumers. Partnering with Nielsen to predict ratings so can manage the advertising inventory and then use analytics to segment advertising opportunities so can sell them to agencies.

Moving on to trends:

  • The first was the ability to handle and process quickly much larger volumes of data. A customer can drop TBs of data and an analytic proof of concept only takes a few weeks making it easy to prove the value.
  • Eric sees a trend of executives asking about the numbers first, see what the numbers imply and then build a business plan rather than just using numbers to validate a plan. This shift is recent but making a big difference to analytic decision making at the executive level.
  • Barbara is from an industry, retail, that has been a lot less analytic than insurance for example. But this industry is also moving and is focused on the value of the inventory they purchase. Even in a fast moving fashion business much of the inventory has to be purchased months in advance. Analytics can really help mitigate the inventory risk by having lower inventories, turn it faster and makes sure the colors and sizes are right.
  • Broad adoption is creating centralized analytic teams. Cameron talked about having experts managed centrally but ensuring that there was a layer of business-focused folks who could take requirements from business units and interface to the experts.
  • Social media is obviously a hot topic but the analytic impact of this was obviously zero as none of the panelists had anything to say about analytics and social media. Social media is another channel for communication, and direct to consumer communication but not something of analytic import yet. Cameron talked about some uses of social media data but it all seems very disconnected and not yet of real impact.

Next, the organizational issues:

  • Is analytics a centralized function, a central department? Eric made a great point that there is an advantage in the ability to hire real experts and give them a place to work but a downside that this can become too separate from the business groups who need to integrate the analytics into what they do every day. Need a balance between centralization for expertise and decentralization for business impact, to embed it in the fabric of daily activities. Get people to the point where they want to check in on the analytics as they move along.
  • Barbara too is part of a centralized group but very matrixed into cross-functional teams across the company. Trust, especially at the executive level, is essential. Executives must be sure that the analytic team understand what decisions are going to be made with the data/analytics being presented
  • Cameron emphasized measurement and delivering proof that the work being done is useful. Taking the predicted value and measuring how well the results match to that predicted value. Team members must be closely linked to their clients and must understand their business so that they trust his advice - back to validating that the analytics team understand the decisions being made. This need to be "part of the business team" is also something Barbara sees as critical

Begin with the decision in mind - all the panelist emphasized the need to understand the decisions being made with the analytics before gathering data and doing analytics. Executive sponsorship, of course, came up repeatedly.

When it comes to finding talent a number of good points were made:

  • Analytics is a word that means too many things to different people and this makes hiring a challenge.
  • Finding people who can play in both worlds - analytics and business - is critical. Easier to find expertise on the business side and on the analytics/statistics side is doable but the cross-overs are hard to hire.
  • Business people can learn the numbers, as long as they have numbers-people to back them up, but having the numbers people learn business skills is much harder. Create people who understand the numbers and can make business recommendation as a result.

Last topic - how to get started:

  • Establish a long range plan - understand your end goal for analytics
  • Partner with the technology team because technology will be critical
  • Invest in the right people, don't be cheap
  • Invest in the right tools, don't be cheap there either
  • Get some low hanging fruit - something you can do quickly and show a great return - and put together "brag boards" to show how well you did to build support
  • Make sure analytics people get some time to pursue ideas not driven by the business - let them see what could be done not just do what the business thinks it wants.

Fabulous panel!


Posted October 28, 2009 1:29 PM
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I am spending some time with SAS this week and I was struck by a couple of announcements related to the ongoing SAS/Teradata partnership. First, the two companies have partnered with Elder Research to create a Business Analytics Innovation Center. This will both pilot new analytics (something at which John and his team at Elder Research excel) and help customers with proofs of concept. I think the combination of SAS and Teradata is an interesting one as I have said before. SAS's focus on analytic modeling tools, Elder Research's experience with finding the right analytic technique for a particular problem (even if it is a novel one as discussed here by John Elder at Predictive Analytics World) and Teradata's focus on making a company's transaction-level detail available in a high-performance data warehouse (as discussed by SingTel Optus for instance) is a very strong one and the BAI Center will be interesting to watch as it gears up next year. Teradata and SAS have also announced a Business Insight Advantage program that is designed to make it easier to adopt the technologies in an integrated way.

Posted October 27, 2009 7:10 AM
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Also published on jtonedm.com

I presented today at Predictive Analytics World (I will post slides later) and John Elder, one of my favorite data mining presenters, gave a great session on the ROI of data mining. John started by giving me a great plug and then pointed out that one of the reasons data mining has survived as an activity is that it is often, typically used to improve a bottom line number. John covered the hype cycle of data mining and lessons learned as well as the 3 major ways data mining helps organizations:

  • Streamline/automate processes
  • eliminate the bad
  • Discover the good

John began by discussing the whole issue of artificial intelligence and made their great point that people and machines are complementary and that the issue is how best to use them together - how to load share between people and machines. Data mining has been proven and is reaching what Gartner calls the plateau of productivity and this compares favorably with the failures of artificial intelligence to really deliver business value. Anyway, on the three major ways data mining can help. He went on to discuss the 9 projects he was using as examples. He made a great point - all 9 were technical successes but only about half were a business success.

Streamline or semi-automatic decisions

  • HSBC cross-sell/up-sell
    A classic - what is the product that will interest a customer next (to target better marketing campaigns, especially to small groups where marketing can be costly). In particular to use the teller window as a sales opportunity - turn a cost (teller) into a possible gain. They used analytic techniques to develop a heat matrix of product associations so people could see the possible products of interest. This was popular but died when groups were merged and the new executive sponsor was not interested. No business success.
  • Anheuser Busch image recognition
    Anheuser Busch spends a lot of time and money managing the layout of its products in stores. They build a schematic of the layout and it takes about 4 hours to build the picture manually. Using data mining techniques they were able to automate 90% of the recognition for a 10x improvement in time to build. Also identified stock outs and "competitor creep" automatically. Follow-on to proof of concept was due to be signed on 9/11 and was delayed until the executive sponsors left/retired. No business success.
  • Lumidigm bio-metrics
    Idea behind this start up was to shine light on your skin and identify disease. Turned out to be impossible to separate out personal characteristics so they instead focused on identifying you from the reflection of your skin. The challenge was to use data mining to identify you at an accuracy rate that makes sense for the solution. Ended up being used at Disney World to track original purchasers of tickets to prevent re-sale. Business Success
  • Peregrine systems business service modeling
    "Sim City for IT" - help build an environment where could simulate impact of changes in IT systems on service level agreements for help tickets etc. Analytics were used to keep uncertainty to the end. This one worked and company was purchased by HP and this solution was part of the reason. Business Success
  • Social Security Administration disability decisions
    A third of people  are accepted but about 50% of people who appeal disability rejections get accepted on appeal - can take 2 years to work through the appeal. Data mining used to fast track people who are easy. Challenge was text analysis - 51 spellings of "learning disability" plus a whole bunch of other ways to say the same thing - a web of concepts. 20% of approvals could be automated immediately. And as always the model is sometimes more accurate than the humans and sometimes the model found missing or mis-applied rules. About to work on the deployment but political embarrassment at the success of the project caused the whole group to be abolished. No business success.

Eliminate the bad

  • IRS fraud detection
    Built a model that uses past fraud situations to score a return and helps focus the analysts on the most likely to be fraudulent. Able to identify 100 returns with 25 frauds instead of the old 1/100. Business Success
  • Consumer electronics service fraud
    Tips indicated that there was fraud and the model was designed to take known cases of fraud to build a model that would predict other cases. This company made $20M in 9 months by detecting fraud. Business Success

Highlight the good

  • WestWind Foundation hedge fund strategy
    Managing commodities by predicting whether to go long or sell (could not short). Very painful, although the model did well over all, as the short term fluctuations made it hard to see the value of the model. Had to answer the question if the advantage of the model was just random. Found, with a resampling simulation, that for 985/1000 simulations the model did better. And this led the customer to believe in the model and invest in it. Worked for years until the edge the model gave disappeared, which the model detected, then stopped. Business Success
  • Pharmacia and Upjohn drug discovery
    Had to decide from 1,000 data points (double blind studies) each of which cost $10,000 to get (on top of the drug work) to keep going or not - to invest another $1Bn! They were not sure the results showed enough benefit. Did some advanced data mining/visualization to show the difference between a placebo and the drug in 3 dimensions (there were 3 ways the drug impacted the patients). And the difference became clear leading to the drug being continued with. Became a commercial drug. Business Success

Lessons learned - ingredients for success:

  • Gain expected - either an incremental improvement matters or there is low hanging fruit
  • Interdisciplinary team
  • Data vigilance
  • Time to learn over many cycles
  • Business champion, and a persistent one


Posted October 20, 2009 9:59 AM
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A little while back I got to spend a few minutes talking about analytics and optimization with Jack Mason of IBM. He posted the resulting video over on the Smarter Planet blog. Enjoy.


Posted October 19, 2009 5:25 AM
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