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Posts Tagged ‘decision making’

Morning star has a story about rise of passive investing which is an interesting use case of human as a hero vs. human as a hazard discussion. They conclude:

.. the growing use of passive investment vehicles reflects the times in which we live. With algorithms helping determine which online ads we’re exposed to each day and new metrics being invented all the time to aid in arenas as diverse as business, politics, and sports, perhaps it should come as no surprise that more people are willing to rely on an inexpensive, systematic, formula-based approach to investing rather than on the judgment and decision-making ability of a living, breathing fund manager.

The article also talks about the obvious next step – that the simple index tracking form of passive investing will be supplemented with smarter algorithms for those investment decisions.

More recently, increasing attention has been paid to alternative indexing approaches–so-called smart beta–that are built around specific factors (stock price/earnings ratios, company performance, share-price volatility, to name a few). Some consider this a hybrid of indexing and active management styles.  

This seems to be a case where the human as a hazard point of view is winning and the design choice has been to upfront capture human insights in algorithms (as in smart beta approaches). Going back to the factors that influence the design choice, the factor that matters here is the most is the type of decision that is being influenced. Investment returns is not about managing extreme decisions but improving the average across several small decisions with well-established rules of good vs. bad decisions.

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Theme 6: Delivering the prediction at the point of decision is critical

[tweetmeme source=”atripathy” only_single=false]In 2007, my wife worked as a hospitalist in Charlotte.  Around that time, I noticed a strange pre-work ritual she followed. She took printouts of a few pages from the Wal-Mart website before going to work.  The behavior was surprising and one day I asked her about the mysterious printouts. It turns out that she was printing out the list of generic drugs that were covered in Wal-Mart’s $4 prescription plan.

Like most physicians, she used to struggle with the fact that some of her patients were not complying with their medications as they were not able to afford the medicines she was prescribing. Wal-Mart had introduced a plan, where they sold some common generic drugs at $4 per prescription.  A lot of patients were able to afford the Wal-Mart medicines and it was popular amongst the doctors to prescribe from the Wal-Mart covered drugs. However there was one issue. The Wal-Mart covered drug list was not integrated with the Epocrates system, the mobile clinical decision support software that most doctors use to verify drug dosage and interactions prior to writing the prescriptions.  At the time of writing the prescription, the doctor did not know whether the specific drug was covered within the Wal-Mart plan, unless she chose to make the extra effort and carry a printout of covered drugs and refer to it prior to writing every prescription.  A great idea suddenly became less attractive to act upon, as the right information was not made available at the point of decision.  I refer to this as the last mile decision delivery problem of predictive analytics projects.

Most of the effort in analytics projects is spent on defining the problem, aggregating data, building and testing models. Getting the information of the model to the decision maker at the point of decision is at many times an afterthought.  However, the benefits of the project are dependent on solving this critical last mile problem.

In my experience decision delivery is challenging as it requires cross-organizational coordination. Successful analytics projects are a partnership between the analytics, business and IT groups.  The analytics group needs to work very closely with decision makers or the end users to put the analysis results in context of the decision maker’s workflow.  The actual delivery of the information is done through a mobile handheld device to a distributed sales force, CRM system integration for call centers or executive dashboard delivery through reporting system integration. All of them require close collaboration with the IT group which has to take the results of a predictive model and integrate it with the relevant front end or reporting infrastructure.  Then there is end-user training to ensure the end-users know what to do with the new information. The program management effort required to execute such a cross-organization initiative is significant and very often not anticipated or planned by the project sponsors.

A good program manager is critical to most complex predictive analytics projects. He/she is able to coordinate the various stakeholders to align on problem definition, outcome format, technology integration and training to drive user adoption of predictive analytics solution. Something to keep in mind as you plan your predictive analytics initiatives.

Have you seen predictive analytics projects getting derailed due to lack of coordination  between various groups within the organization or under investment in program management resources?

PS: Last year Wal-Mart solved its last mile problem and integrated the covered $4 prescription drug list into the Epocrates application.

Previous parts of the series are available here: part 1, part 2, part 3, part 4, and part 5

Cross posted on TheInformationAdvantage.com

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Theme 5: Good data visualization leads to smarter decisions.

[tweetmeme source=”atripathy” only_single=false]The above visualization has arguably saved more lives than all predictive analytics initiatives combined.  It is visualization created in 1854 by Dr. John Snow. As Miles Dowsett has written,

In 1854 London was the biggest city the world had ever seen, and was literally drowning in its own filth. There was no sewage system in place and those that lived in the capital literally threw their waste and cesspits out into the crowded streets and river Thames. As a result, London was a disgustingly smelly place to be and was periodically engulfed by disease – most notably Cholera.

However at that time it was believed that it was the smell of London that was the root cause of diseases such as cholera. It came to be known as the Miasma theory of disease. However, Dr. Snow was skeptical of the Miasma theory of smell causing disease and in his 1849 essay On the Mode of Communication of Cholera actually introduced the water borne theory of Cholera. He presented his findings to London’s health authorities but couldn’t convince anybody of its merit, and was largely ignored.

In August, 1854 there was another outbreak of cholera in the Broad Street neighborhood. Over a 10 day period cholera decimated the population; 10% of the neighborhood died.

Since Dr. Snow was convinced that Cholera was a waterborne virus, it didn’t take him very long to identify that it was the infected water pump at 40 Broad Street that caused the epidemic.  Instead of solely relying on numbers, he produced a visual of the Broad street neighborhood where he marked the water pump at 40 Broad Street and designated each cholera death with a bar. The concentration of deaths around the pump at 40 Broad Street, which trailed off the further out from the pump one went, was so convincing that the authorities finally accepted the theory of Cholera being water borne. They removed the handle of the pump and it ultimately stopped the spread of the disease in 1854. It also paved the way for a sewage and sanitation systems to be put in place; one of the greatest engineering feats to be undertaken in London’s history, changing the way that urban systems exist and continue to grow to this day.

As the story demonstrates, good data visualization leads to smarter decisions.  Far too often, analysts focus all their efforts on data collection and modeling and pay very little attention to presenting the results in a way that decision makers can relate to.  Every successful predictive analytics project leads to a change from the status quo way of doing things, and it is never easy to convince the decision makers that the new way is better.  Most decision makers need more than R square or a mean absolute percentage error metric to be convinced about the efficacy of a solution based on predictive analysis and feel comfortable approving the change. This is where data visualization skills become important.

James Taylor wrote that visualization is more relevant in context of strategic decisions and not so much for the operational decisions.

Decision making at the operational level is too high-speed, too automated for much in the way of visualization to be useful at the moment of decision.

While I do agree with him, I know of instances where visualization has been creatively used in the very operational environment of call centers. Speech analytics and emotion detection are growing areas in call center technology where depending upon the choice of words, the speech analytics system detects the emotion level of the caller and displays an appropriate emoticon on the agent’s desktop right when the call is transferred to them. Even without understanding the complexity of the caller’s issue, the agent immediately gets a guidance about the emotional state for the caller.

As consultants we are always trying to create that ‘money-visual’ in our presentations, a slide which brings all the analyses together and unambiguously calls out for a need to change or drive action. I feel every predictive analytics project needs one such ‘money-visual’.

Do you have any examples of visuals which you have used to convince people to drive change?

Here are the links to previous postings: part 1, part 2, part 3 and part 4

Cross-posted on TheInfromationAdvantage.com

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Theme 4: Statistical techniques and tools are not likely to provide competitive advantage

[tweetmeme source=”atripathy” only_single=false]I read this interesting post from Sijin describing his journey to master a video game (emphasis added by me)

All this kind of reminded me of my experiences with finding the perfect weapon while playing Call of Duty 4 over the past year. I spent 3 hours a day almost every day for the past one year playing this game, reaching the max prestige level (the “elite” club) in the multi-player version. I became really good at it… no matter what weapon I was using. But I remember when I started out and I really sucked, I became obsessed with finding the perfect weapon with the perfect set of perks and add-ons. I used to wander the forums asking people about which weapons and perks to use on which map and what the best tips were etc. Thinking that having the perfect weapon would make me a good player. In the end, the only thing that mattered was all the hours I put in to learn all the maps, routes, tricks and my ability (I like to think). The surprising thing was that once I mastered the game, it didn’t really matter what weapon I chose, I was able to adapt any weapon and do a decent job.

This story captures the essence of the theme of this post.

The popular statistical techniques frequently used in business analytics like linear regression and logistic regression are more than half-a-century old. System dynamics was developed in 1950s. Even neural networks have been around for more than 40 years. SAS was founded in 1976 and the open source statistical tool R was developed in 1993. The point is that popular analytical techniques and tools have been around for some time and their benefits and limitations are fairly well understood.

An unambiguous definition of the business problem that will impact a decision, a clear analysis path leading to output, thorough understanding of various internal and 3rd-party datasets are all more important aspects of a predictive analytics solution than the choice of the tool. Not to mention having a clear linkage between the problem, the resulting decision, and measurable business value.  The challenge is in finding an expert user who understands the pros and cons and adapts the tools and techniques to solve the problem at hand. Companies will be better served by investing in the right analytical expertise rather than worrying about the tools and technique as the right analytical team can certainly be a source of competitive advantage.

While this theme is fairly well understood within the analytics practitioner community, the same cannot be said about business users and executives. It is still easy to find senior executives who believe that ‘cutting edge’ techniques like neural networks should be used to solve their business problem or predictive analytics tools are a key differentiator while selecting analytics vendors.  The analytics community needs to do a better job in educating the business user and senior executives about this theme.

You can read the previous installments of the series here (part 1, part 2, and part 3).

Cross-posted on TheInformationAdvantage.com

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[tweetmeme source=”atripathy” only_single=false]I, along with two of my colleagues (Anand Rao & Dick Findlay), recently conducted a workshop at the World Research Group’s Predictive Modeling conference at Orlando. As part of the workshop, I spoke about a list of 8 things that organizations should keep in mind as they consider investing in predictive analytics.

In this post, I will list the 8 points and discuss the first one. Subsequent posts will explore the rest of the themes.

  1. Understand the cost of a wrong decision
  2. Strategic and operational decisions need different predictive modeling tools and analysis approaches
  3. Integration of multiple data sources, especially third-party data, provides better predictions
  4. Statistical techniques and tools are mature and by itself not likely to provide significant competitive advantage
  5. Good data visualization leads to smarter decisions
  6. Delivering the prediction at the point of decision is critical
  7. Prototype, Pilot, Scale
  8. Create a predictive modeling process & architecture

Theme 1: Understand the Cost of a Wrong Decision

Is it even worth investing the resources on developing a predictive analytics solution for a problem? That is the first question which should be answered. The best way to answer it is to understand the cost of the wrong decision. I define a decision as ‘wrong’ if the outcome is not a desired event. For example, if the direct mail sent to a customer does not lead the desired call to the 800 number listed, then it was a ‘wrong’ decision to send the mail to that customer.

A few months ago my colleague Bill told a story which illustrates the point.

Each year Bill takes his family to Cleveland to visit his mom. They stay in an old Cleveland hotel downtown. The hotel is pretty nice with all the trappings  that you would expect of an old and reputable establishment. Last time they decided to have breakfast at the hotel across the street at the Ritz. After the breakfast when Bill and his family were in the lobby, the property manager spotted him and the kids and walked over to talk. He chatted for a few minutes and probably surmised that Bill was a reasonably seasoned traveler and told the kids to wait for him.  He walked away and came back with a wagon full of toys.  He let each kid pick a toy out of the wagon.  Think about it. They were not even guests at the Ritz, all they did was have breakfast at the Ritz! The kids loved the manager and Bill remembered the gesture. Fast forward to this holiday season, and sure enough Bill and his family booked a suite at the Ritz for six days. For the price of a few nice toys, the manager converted a stay that generated a few thousand dollars in room charges, meals, and parking.

Now suppose Bill did not go back to the hotel, which was the desired outcome by the hotel manager. What would have been the cost of manager’s ‘wrong’ decision?  The cost of a few toys. The cost compared to the potential upside is negligible. Does it make sense for the hotel to build a predictive model to decide which restaurant diners to offer toys so that they come back and stay? I don’t think so.

Understanding the cost of wrong decision upfront saves one from making low value investments in predictive analytics.

PS: My colleague Paul D’Alessandro has also used this story to illustrate experience design(XD) principles.

Photo credit: GJones

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