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If you type the words “business intelligence expert” in Google. you may get the top ranked result as http://goo.gl/pCqUh or Peter James Thomas, a profound name as it can be as it spans three of the most important saints in the church.
The current post for this is very non business -intelligence topic called Wager. http://peterjamesthomas.com/2011/07/20/wager/
It details how Peter, a virtual friend whom I have never met, and who looks suspiciously like Hugh Grant with the hair, and Ajay Ohri (myself) waged a wager on which cricket team would emerge victorious in the ongoing test series . It was a 4 match series, and India needed to win atleast the series or avoid losing it by a difference of 2, to retain their world cricket ranking (in Tests) as number 1.
Sadly at the end of the third test, the Indian cricket team have lost the series, the world number 1 ranking, and some serious respect by 3-0.
What is a Test Match? It is a game of cricket played over 5 days.
Why was Ajay so confident India would win. Because India won the one day world championship this April 2011. The one day series is a one day match of cricket.
There lies the problem. From an analytic point of view, I had been lulled into thinking that past performance was an indicator of future performance, indeed the basis of most analytical assumptions. Quite critically, I managed to overlook the following cricketing points-
1) Cricket performance is different from credit performance. It is the people and their fitness.
India’s strike bowler Zaheer Khan was out due to injury, we did not have any adequate replacement for him. India’s best opener Virender Sehwag was out due to shoulder injury in the first two tests.
Moral – Statistics can be misleading if you do not apply recent knowledge couple with domain expertise (in this case cricket)
2) What goes up must come down. Indeed if a team has performed its best two months back, it is a good sign that cyclicality will ensure performance will go down.
Moral- Do not depend on regression or time series with ignoring cyclical trends.
3) India’s cricket team is aging. England ‘s cricket team is youthful.
I should have gotten this one right. One of the big and understated reasons that the Indian economy is booming -is because we have the youngest population in the world with a median age of 28.
India has more than 50% of its population below the age of 25 and more than 65% hovers below the age of 35. It is expected that, in 2020, the average age of an Indian will be 29 years, compared to 37 for China and 48 for Japan; and, by 2030, India’s dependency ratio should be just over 0.4
India’s population is 1.21 billion people, so potentially a much larger pool of athletes , once we put away our laptops that is.
the total population of the United Kingdom was 58,789,194 (I dont have numbers for average age)
Paradoxically India have the oldest cricket team in the world . This calls for detailed investigation and some old timers should give way to new comers after this drubbing.
Moral- Demographics matters. It is the people who vary more than any variable.
4) The Indian cricket team has played much less Test cricket and much more 20:20 and one day matches. 20:20 is a format in which only twenty overs are bowled per side. In Test Matches 90 overs are bowled every day for 5 days.
Stamina is critical in sports.
Moral- Context is important in extrapolating forecasts.
Everything said and done- the English cricket team played hard and fair and deserve to be number ones. I would love to say more on the Indian cricket team, but I now intend to watch Manchester United play soccer.
Here is the winner of the Data Mining Research People Award 2010: Ajay Ohri! Thanks to Ajay for giving some time to answer Data Mining Research questions. And all the best to his blog, Decision Stat!
Data Mining Research (DMR): Could you please introduce yourself to the readers of Data Mining Research?
Ajay Ohri (AO): I am a business consultant and writer based out of Delhi- India. I have been working in and around the field of business analytics since 2004, and have worked with some very good and big companies primarily in financial analytics and outsourced analytics. Since 2007, I have been writing my blog at http://decisionstats.com which now has almost 10,000 views monthly.
All in all, I wrote about data, and my hobby is also writing (poetry). Both my hobby and my profession stem from my education ( a masters in business, and a bachelors in mechanical engineering).
My research interests in data mining are interfaces (simpler interfaces to enable better data mining), education (making data mining less complex and accessible to more people and students), and time series and regression (specifically ARIMAX)
In business my research interests software marketing strategies (open source, Software as a service, advertising supported versus traditional licensing) and creation of technology and entrepreneurial hubs (like Palo Alto and Research Triangle, or Bangalore India).
DMR: I know you have worked with both SAS and R. Could you give your opinion about these two data mining tools?
AO: As per my understanding, SAS stands for SAS language, SAS Institute and SAS software platform. The terms are interchangeably used by people in industry and academia- but there have been some branding issues on this.
I have not worked much with SAS Enterprise Miner , probably because I could not afford it as business consultant, and organizations I worked with did not have a budget for Enterprise Miner.
I have worked alone and in teams with Base SAS, SAS Stat, SAS Access, and SAS ETS- and JMP. Also I worked with SAS BI but as a user to extract information.
You could say my use of SAS platform was mostly in predictive analytics and reporting, but I have a couple of projects under my belt for knowledge discovery and data mining, and pattern analysis. Again some of my SAS experience is a bit dated for almost 1 year ago.
I really like specific parts of SAS platform – as in the interface design of JMP (which is better than Enterprise Guide or Base SAS ) -and Proc Sort in Base SAS- I guess sequential processing of data makes SAS way faster- though with computing evolving from Desktops/Servers to even cheaper time shared cloud computers- I am not sure how long Base SAS and SAS Stat can hold this unique selling proposition.
I dislike the clutter in SAS Stat output, it confuses me with too much information, and I dislike shoddy graphics in the rendering output of graphical engine of SAS. Its shoddy coding work in SAS/Graph and if JMP can give better graphics why is legacy source code preventing SAS platform from doing a better job of it.
I sometimes think the best part of SAS is actually code written by Goodnight and Sall in 1970’s , the latest procs don’t impress me much.
SAS as a company is something I admire especially for its way of treating employees globally- but it is strange to see the rest of tech industry not following it. Also I don’t like over aggression and the SAS versus Rest of the Analytics /Data Mining World mentality that I sometimes pick up when I deal with industry thought leaders.
I think making SAS Enterprise Miner, JMP, and Base SAS in a completely new web interface priced at per hour rates is my wishlist but I guess I am a bit sentimental here- most data miners I know from early 2000’s did start with SAS as their first bread earning software. Also I think SAS needs to be better priced in Business Intelligence- it seems quite cheap in BI compared to Cognos/IBM but expensive in analytical licensing.
If you are a new stats or business student, chances are – you may know much more R than SAS today. The shift in education at least has been very rapid, and I guess R is also more of a platform than a analytics or data mining software.
I like a lot of things in R- from graphics, to better data mining packages, modular design of software, but above all I like the can do kick ass spirit of R community. Lots of young people collaborating with lots of young to old professors, and the energy is infectious. Everybody is a CEO in R ’s world. Latest data mining algols will probably start in R, published in journals.
Which is better for data mining SAS or R? It depends on your data and your deadline. The golden rule of management and business is -it depends.
Also I have worked with a lot of KXEN, SQL, SPSS.
DMR: Can you tell us more about Decision Stats? You have a traffic of 120′000 for 2010. How did you reach such a success?
AO: I don’t think 120,000 is a success. Its not a failure. It just happened- the more I wrote, the more people read.In 2007-2008 I used to obsess over traffic. I tried SEO, comments, back linking, and I did some black hat experimental stuff. Some of it worked- some didn’t.
In the end, I started asking questions and interviewing people. To my surprise, senior management is almost always more candid , frank and honest about their views while middle managers, public relations, marketing folks can be defensive.
Social Media helped a bit- Twitter, Linkedin, Facebook really helped my network of friends who I suppose acted as informal ambassadors to spread the word.
Again I was constrained by necessity than choices- my middle class finances ( I also had a baby son in 2007-my current laptop still has some broken keys – by my inability to afford traveling to conferences, and my location Delhi isn’t really a tech hub.
The more questions I asked around the internet, the more people responded, and I wrote it all down.
I guess I just was lucky to meet a lot of nice people on the internet who took time to mentor and educate me.
I tried building other websites but didn’t succeed so i guess I really don’t know. I am not a smart coder, not very clever at writing but I do try to be honest.
Basic economics says pricing is proportional to demand and inversely proportional to supply. Honest and candid opinions have infinite demand and an uncertain supply.
DMR: There is a rumor about a R book you plan to publish in 2011 Can you confirm the rumor and tell us more?
AO: I just signed a contract with Springer for ” R for Business Analytics”. R is a great software, and lots of books for statistically trained people, but I felt like writing a book for the MBAs and existing analytics users- on how to easily transition to R for Analytics.
Like any language there are tricks and tweaks in R, and with a focus on code editors, IDE, GUI, web interfaces, R’s famous learning curve can be bent a bit.
Making analytics beautiful, and simpler to use is always a passion for me. With 3000 packages, R can be used for a lot more things and a lot more simply than is commonly understood.
The target audience however is business analysts- or people working in corporate environments.
Ajay Ohri has been working in the field of analytics since 2004 , when it was a still nascent emerging Industries in India. He has worked with the top two Indian outsourcers listed on NYSE,and with Citigroup on cross sell analytics where he helped sell an extra 50000 credit cards by cross sell analytics .He was one of the very first independent data mining consultants in India working on analytics products and domestic Indian market analytics .He regularly writes on analytics topics on his web site www.decisionstats.com and is currently working on open source analytical tools like R besides analytical software like SPSS and SAS.
- Skills of a good data miner (zyxo.wordpress.com)
- Data Mining with WEKA (r-bloggers.com)
- How Data Mining Can Help You Score on the First Date (volokh.com)
- Upcoming webinar on investigative analytics (dbms2.com)
- IBM SPSS 19 Now Available to the Global Academic Community via e-academy’s OnTheHub eStore (prweb.com)
My profile on Quora