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Archive for the ‘Data Mining’ Category

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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[tweetmeme source=”atripathy” only_single=false] Ken Rona tweeted earlier today about a subject which stuck a chord. He was writing about the difference between a business analyst and data analyst (or data scientist as they are increasingly called). I wanted to expand on the idea as It is important to distinguish between the two roles. I have seen a lot of confusion around the definitions and some executives thinking they are one and the same and others who believe they are totally different. The truth is probably somewhere in between. Here is my attempt at comparing along the key skill set dimensions:

Business Analyst Data Scientist
Business domain knowledge Expertise in industry domain Very good working knowledge of industry domain
Data handling
skills
Ability to handle multiple CSV files and import them into Access or Excel for analysis Ability to write SQL queries to extract data from databases and join multiple datasets together
Analytics skills Knowledge of simple business statistics(statistical significance, sampling), Able to use statistics functions in Excel Proficiency in advanced mathematics/statistics (regressions, optimization, clustering analysis etc.)
Insight presentation skills Storytelling skills using PowerPoint Storytelling skills using information visualization and PowerPoint
Problem solving
skills
Proficiency in hypotheses driven approach is good to have Proficiency in hypotheses driven approach is must have
Tools Access, Excel, PowerPoint etc MS SQL, Oracle, Hadoop, SQL SAS, SPSS, Excel, R, Tableau etc

I think this is a good starting point but can be refined further. Feedback/comments are welcome.

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Interesting stories on information & decisions that influenced my thinking and were tweeted by me:

  1. Extending the value of operational #data to serve your customers. Netflix ISP comparison http://tinyurl.com/4whjd3o
  2. Coal economics and computer chips. Demand driver for need for more #analytics http://bit.ly/f6Hme2
  3. Excellent paper for #analytics practitioners on customer lifetime value and RFM models http://bit.ly/dkdkaa
  4. #visualization of loobying efforts http://reporting.sunlightfoundation.com/lobbying/
  5. #Analytics of dating http://blog.okcupid.com/
  6. LinkedIn’s @PeteSkomoroch on the key skills that data scientists need. http://oreil.ly/hXZTVJ

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Theme 7: Prototype, Pilot, Scale

Edison did not invent the light bulb. He took a working concept and developed hundreds of prototypes rapidly, tested them and along the way figured out improvements that were required to scale his invention for commercial use. Julian Trubin writes about the prototyping process:

In 1879 Edison obtained an improved Sprengel vacuum pump, and it proved to be the catalyst for a breakthrough. Edison discovered that a carbon filament in an oxygen-free bulb glowed for 40 hours. Soon, by changing the shape of the filament to a horseshoe it burned for over 100 hours and later, by additional improvements, it lasted for 1500 hours.

Edison’s primary contribution to the development of light bulb was that he carried the idea from laboratory to commercialization, taking into consideration not only technical problems, but also issues like economics and the manufacturing of bulbs.

We took a leaf from Edison’s book when we developed, our prototype, pilot and scale approach to deploy analytics solutions for clients.

In our experience rapid prototyping is essential to show the value of the initiative to senior executives.  One of our health care clients wanted help in institutionalizing data driven culture within its sales organization especially in identifying and focusing sales effort on high potential customers. At first, we developed a prototype predictive scoring model to identify the high potential customers. Mapping the results of the model to existing effort demonstrated that greater than 50% sales force time was used ineffectively and the client was leaving a lot of dollars on the table.

However, for organizations to see bottom line benefit, adoption of predictive analytics based solution is key. Piloting helps refine the prototype and plan for potential adoption pitfalls amongst the end users.  At our healthcare client, we knew that there were skeptics amongst the sales people who do not trust the model and there were change management blind spots which we wanted to discover prior to the national roll out. We designed a pilot with the following objectives:

  1. Prove the validity of the predictive model
  2. Create evangelists from the sales team of the pilot regions
  3. Identify the big data gaps and establish a process of continually refining CRM data
  4. Establish and refine the key performance metrics to report to senior management
  5. Understand the key questions and concerns of the sales team in adopting the system

We collected a lot of rich quantitative and qualitative data during the pilot phase, which conclusively proved the value of the predictive model but also provided us with insights to incorporate into the roll out process.  For instance we learned that in a few instances customer address data was not getting updated  in the data warehouse and that sales managers wanted to understand the factors that went into calculating the predictive score of customer before they felt comfortable using it.

Scaling the pilot requires cross organization coordination and strong program management to ensure that the pilot learnings are incorporated in the roll out, there is a positive word of mouth buzz for the solution and there is minimal impact to day-to-day business. The inputs from pilot helped us better design the compensation rules and reporting metrics, which helped us roll out the system which head the trust of the sales force.

Our client saw significant uplift in revenue in the first 3 months of rollout. The sales organization started realizing the value of data driven approach and hired a team to support other sales analytics initiatives.

Are there other tips and tricks which you have successfully used to deploy predictive analytics solutions?

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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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