Month: June 2014

India’s Supercomputing Stature Continues to Slide

The half-yearly list (June 2014) of the world’s fastest supercomputers, maintained by is out. India, which at one time, was home to the 4th fastest supercomputer in the world, continues with its downward journey in the ranking.

The country’s fastest supercomputer, an IBM-based system at the Indian Institute of Tropical Meteorology, ranks a lowly 52nd rank globally. The same system, a year back, was ranked 36th fastest in the world. With with no improvement in its LINPACK performance, which is used to rank these systems, its rank dropped to 44 in the November 2013 list, before dropping further in the latest list.

The chart shows the highest ranks an Indian supercomputer obtained in all the successive lists starting with November 2003, when a supercomputer at Tata Sons, occupied the 4th rank globally.


Supercomputers India Rank

The top slot in the recent list, released on Monday, 23rd June 2014, is taken by a Chinese supercomputer. China, which has 76 supercomputers in the list of top 500, has continued to climb the ranking, both in terms of number of supercomputers in the list, as well as highest performance/rank. Here is a comparison of how India and China have fared in the list. The parameter is number of supercomputers in the list of top 500.


Supercomputers India China

Here are the top countries in the list, in terms of number of sites featuring in the list


top supercomoputing countries



So, which are the best countries in the world?

Best…in what sense? Across what parameters? The question sounds too naive, too simplistic.

But it may not be. Going by data—the only thing we swear by—indeed, some countries may be better than rest of us in almost all areas, across all parameters.

DataJourno decided to carefully compare countries across measurable parameters in different aspects of life, society and economy. But instead of getting into individual data points, we decided to bank on already established systems that exist—in the form of global rankings and ratings.

By doing this,

  1. we avoided trying to reinvent the wheel, with our limited knowledge in many of those areas
  2. we avoided getting caught in standardization issues
  3. we short-circuited on time
  4. we banked on the credibility and quality of these ratings, which have only become better over the years.

After going through several such lists, we zeroed in on six such global rankings that are most credible and respected.They measure competitiveness, economic freedom, natural environment, human development, integrity of people, and quality of life. Of course, we went by their latest ranking, without trying to standardize on year. That would have made us take older rankings in many cases just because one list is older.

These lists are

  • Global Competitiveness Ranking by World Economic Forum 2013-14
  • United Nations Development Programme (UNDP) Human Development Index Rankings 2012
  • Transparency International Corruption Perception Index 2013
  • The Economist Intelligence Unit Where To Be Born (earlier called Quality of Life) Index 2013
  • The Heritage Foundation-Wall Street Journal Index of Economic Freedom 2014
  • Yale University Environment Performance Index 2014

The last two are comparatively new  and probably not as established as the other other four. But we still decided to include them as both these (economic freedom and environment) are important parameters and they are clearly the best in class, when it comes to those categories.

We decided to look at the top 20 ranking countries in each of those lists.

And this is what we found.

  • There are just 38 countries in this list of lists, which could potentially have 120 countries, if there was no overlap. A list of 60-80 would have meant fairly good overlap. A list of just 38 means a very high overlap.
  • If you remove all such countries that feature among top 20 in just one of the lists, there are 24 countries that feature in two or more lists.
  • There are as many as 12 countries that feature in 5 or 6 lists. Out of which, five countries (Singapore, Sweden, Netherlands, Denmark, Germany) feature in all the lists.

So, why can’t we call these five (or even for that matter, these 12), the world’s best countries? 

Here is a tabular summary of the country ranking.


In short, some countries are better than others in almost all respects.

But the bigger conclusion is: the parameters probably have a stronger correlation than we think they have.

Based on this, we create a composite ranking, giving equal weightage to all parameters. We decided to look at just the rankings and not the scores, as scores are not easily comparable, the scales being different.

Based on their ranks in all the lists, this is how the overall top 20 looks like.

  1. Singapore (features in all the 6 lists)
  2. Australia (features in 5 of the 6 lists)
  3. Switzerland (features in 5 of the 6 lists)
  4. Sweden (features in all the 6 lists)
  5. Netherlands (features in all the 6 lists)
  6. Norway (features in 5 of the 6 lists)
  7. Denmark (features in all the 6 lists)
  8. Germany (features in all the 6 lists)
  9. Hong Kong (features in 5 of the 6 lists)
  10. New Zealand (features in 5 of the 6 lists)
  11. Finland (features in 5 of the 6 lists)
  12. United States (features in 5 of the 6 lists)
  13. Canada (features in 4 of the 6 lists)
  14. Ireland (features in 4 of the 6 lists)
  15. United Kingdom (features in 4 of the 6 lists)
  16. Luxembourg (features in 3 of the 6 lists)
  17. Austria (features in 4 of the 6 lists)
  18. Japan (features in 3 of the 6 lists)
  19. Belgium (features in 4 of the 6 lists) & Iceland (features in 3 of the 6 lists)

As one can see, though richer countries do better, it is not necessarily smaller versus bigger. The US, Germany, Japan, UK, Canada and Australia feature in the list as do Luxembourg, Hong Kong and Singapore.

Here is a visual representation of how balanced the top countries look, across different parameters.



The more regular a graph looks, the more balanced is the country across parameters.


Is Twitter the new official communication channel of the Modi government?

After Prime Minister Narendra Modi asked his ministers not to speak to media directly, there have been media reports that he may follow his Gujarat Model here too.  This is what wrote

These instructions may well be the first serious step to turn New Delhi into Gandhinagar, where during Modi’s three terms as chief minister, members of his cabinet would not speak to the press unless they had obtained permission from him. Even the customary press briefings after the state cabinet meetings – which in other states are addressed by ministers – are either not held at all in Gujarat or are addressed by spokesmen of the state government.

The opposition has, naturally, seen it as disempowering ministers. But the question is: is it really disempowering or is it trying to cut off media from the communications channel? other than, of course, the “official” policy communications, which ministry spokespersons will continue to do?

What the media has failed to notice is that Modi may be directly or indirectly influencing his ministers to “communicate directly” with people, as he himself has done, choosing to speak to media only when he wants and “in his terms”.

Twitter, his preferred channel, is largely one-sided communications; can be better managed than a press conference to show what you want to show; and once in a while, both genuine compliments/suggestions as well as planted ones can be responded to give that “interaction with common people” feeling.

Here is a list of ministers with Twitter accounts and their number of followers. While Modi himself leads the pack with Sushama Swaraj (a veeteran tweeple) as a distant No 2, others are way behind, though some of them are catching up fast. Arun Jaitley, for example, started this account only in November 2013 and has already close to 350,000 followers.

Union Ministers on Twitter (Click to enlarge)


The bar graph, of course, shows the number of followers (it is not exactly to scale but the mentioned numbers are actual), as of  9th June 2014.

But what is more interesting is how long they have been on Twitter, represented in the chart through use of different shades. Out of 32 cabinet ministers and ministers of state with independent charge, as many as 24 (that is 75%) are in Twitter. That is fairly high, as compared to the UPA government.

But what is interesting and supports the theory that they may be trying to impress their leader is the time of their joining. As many as 10 0f those have joined Twitter after Modi was officially anointed the chief of campaign committee in June 2013. As many as 14 have joined after he emerged as the No 1 prime ministerial candidate. And if you leave out genuine prolific users such as Modi himself, Sushma Swaraj, Nirmala Sitharaman and Smriti Irani, there are just a handful of them who have joined after 2011 but before Modi emerged as the No 1 leader; in other words, for natural reasons.  There is absolutely no one among the ministers who is between 2 -3 years old in Twitter.

In fact, those who are wondering what made Modi choose Smriti Irani as a cabinet minister with a plum portfolio (others are either veterans, come with a professional background like Gen VK Singh or who have proven themselves in party work such as Dharmendra Pradhan and Piyush Goyal), the Twitter stats may give a clue. In Modi’s “virtually real”  world, she has delivered the best performance, creating the largest follower base in Twitter after Modi and Sushma Swaraj, the later herself an aspirant for PM post before Modi’s emergence.

Here is a list of all the cabinet ministers and ministers of state with independent charge with their age and Twitter handle, just for your reference.

Minister Age Twitter Handle
Narendra Modi 63 @narendramodi
Rajnath Singh 62 @BJPRajnathSingh
Sushma Swaraj 62 @Sushmaswaraj
Arun Jaitley 61 @arunjaitley
Venkaiah Naidu 64 @MVENKAIAHNAIDU
Nitin Gadkari 58 @nitin_gadkari
D. V. Sadananda Gowda 61 @DVSBJP
Uma Bharti 55 @umasribharti
Najma Heptullah 74 NA
Ram Vilas Paswan 67 @iramvilaspaswan
Maneka Gandhi 57 @ManekaGandhi
Ananth Kumar 54 @AnanthKumar_BJP
Ravi Shankar Prasad 59 @rsprasad_bjp
Ashok Gajapati Raju 62 NA
Anant Geete 62 NA
Harsimrat Kaur 47 @harsimrat_badal
Narendra Singh Tomar 56 NA
Jual Oram 53 @jualoram
Thawar Chand Gehlot 66 NA
Kalraj Mishra 73 @Kalraj_Mishra
Radha Mohan Singh 64 @singhradhamohan
Harsh Vardhan 59 @drharshvardhan
Smriti Irani 38 @smritiirani
Vijay Kumar Singh 63 @Gen_VKSIngh
Inderjit Singh Rao 63 @Rao_InderjitS
Santosh Kumar Gangwar 66 NA
Shripad Yasso Naik 61 NA
Dharmendra Pradhan 44 @dpradhanbjp
Sarbananda Sonowal 51 NA
Prakash Javadekar 63 @PrakashJavdekar
Piyush Goyal 49 @PiyushGoyal
Dr. Jitendra Singh 42 NA
Nirmala Sitharaman 54 @nsitharaman


Is opinion superior?

It is difficult to understand the fascination for the words “column” and “opinion” among Indian journalists, as compared to “stories” and “reports”.  Many think if you get an “opportunity” to write an opinion column, you have arrived as a journalist.

This perceived sense of superiority of “opinion” often makes media pass off good reporting and even data analysis as opinion. This piece in Mint, Why India’s sanitation crisis is a public health emergency, is a fairly good example of data journalism, which tries to corelate India’s widespread practice of open defecation with malnutrition. The accompanying map too is a fairly good, if not extraordinary, visualization.

But why the hell should it be labeled as opinion? Is it to give it that supposed importance or is there no other sections that the editors can fit it into?

In fact, data journalism is not as new or rare as we think it is in India. Stories like these are actually data journalism pieces. Just that many publications do not realize it.



Data Journalism: Why definitions matter as much as the numbers…

Shyamanuja Das

Data journalism is journalism first—and last. And there should not be any doubt in anyone’s mind about that.

Even as we celebrate the increased access to authentic data and availability of great anlaytics and visualization tool that has given a lot of power to the journalist community, we must not forget that the basic premises of journalism still stand. We must tell good stories. And we must question.

One of the most important questions about any data is the exact definition of what that data actually represents. One can say XXX is India’s largest e-commerce company. But what does “largest” mean? Highest revenue? Maximum number of users who buy from their site? Maximum number of transactions? It also depends on where the story appears and only a journalist knows what her readers would naturally assume it to be. For example, in a site like, the readers will assume the parameter to be either valuation or revenue; in a Times of India, few readers will naturally think about valuation.

The above may be an over-simplified example. In many cases, the fine prints need to be read carefully to question the data, especially when it looks counter-intuitive. There lies the irony. While is it true that the more counter-intuitive is the conclusion, the bigger is the story; it is also true that the more counter intuitive is the finding, the harder one must question. And you come back to the basics—nothing in life comes easy; surely not a good story. Data journalism or no data journalism.

There is an excellent example in today’s Times of India. In a story, India over-reporting green cover, the report points out that the flaw may lie with the definition of what is called forest. “A large area that the government has been including under the forest category actually comprises commercial plantations, including those for coffee, arecanut, cashew, rubber, fruit orchards, parks and gardens,” the story says, quoting researchers from Indian Institute of Science Bangalore. This, the researchers attribute to the definition of forest cover by the Forest Survey of India (FSI). It defines forest cover to be “all lands more than one hectares in area, with tree canopy density of more than 10%, irrespective of ownership and legal status”. This definition could well mean that man-made forests or monocultures (farmland used to grow only one type of crop) are being considered forests, it says.

If true, it challenges a basic fact all of us have believed: that India’s forest cover is growing. Over the last few years, media has reported this in a celebratory tone. Here is such a story published in Times of India in 2009: India’s forest cover rises to over 21%.

Here, no one had questioned it even if it looked a little counter intuitive, till the IISc researchers did. To prove their point, they have even given data on what exactly is the area covered by plantations and orchads, though it is not exactly clear from the  story whether the FSI has actually included these areas in its survey.

While this is an example of how questioning the definition has brought out the truth, you need not go further than the same day’s Times of India to find an example where these basic questions have not been considered. Take the story, Indian B-School graduates get jobs easily. It claims, quoting a survey by Graduate Management Admission Council, which conducts GMAT, that 92% of Indian management students had an offer of employment. The survey was conducted among 2014 batch of students.

So, what do you make out of the story? That 92% of management graduates in India land up with a job. Now, take a look at the data from the All India Survey of Higher Education, which was the basis for the preceding post in this site, And you thought an MBA degree is so exclusive. According to it, as many as 5.6 lakh students enrolled in management programs in 2010-11, the latest year for which the data is available. In 2012-13, which was the enrollment year for 2014 batch, that number must have been more. Even if we assume that it is the same as it was in 2010-11 (approx 5.5 lakh) and further assume that only 70% would complete it, going by the 92% figure, it means more than 3.5 lakh of those will land up with a job.

Now, let us look at how the Graduate Management Admission Council arrive at this 92% figure? By doing a survey among 111 universities in 20 countries. What is India’s share? We do not know, but it is safe to assume that it cannot be more than 10-12 at best. And which are these institutes/universities? Are they representative of all of India’s management schools? Or are they just the  tier-1 schools like the IIMs, ISB and FMS?

These are questions that must be asked. While the survey may be right in its own way if it says it is true only about tier 1 schools, the story does not even vaguely mentions that—such as “top B schools”. Without that, it means that it is true for whole of Indian management schools.

Exactly the kind of stuff that the mushrooming private management schools in India want to quote while selling a dream to unsuspecting students and parents.

This is the danger of relying on data without questioning what that data represents. Data today surely means more credibility. But it will soon lose that credibility if it is not questioned, not understood or not put in the proper context. Nice visualizations cannot compensate for lack of authenticity and context.

Data journalism is not so much about data as it is about journalism.

[Shyamanuja Das is a former editor and is currently a director at market research firm, Juxt.  He advises businesses, investors and marketers on effective use of public data and teaches data journalism. He is a co-founder of DataJourno


And you thought an MBA degree is so exclusive?

Imagine how many young men and women think enrolling for an MBA degree in some university or college means they have arrived in life; they are now part of an exclusive club.

The data from the All India Survey on Higher Education (AISHE) by the Department of Higher Education busts that myth. Getting into an MBA is no more as exclusive as it is thought out to be.

In 2010-11, the latest year for which all enrollment data is available, as much as 5.6 lakh students enrolled for MBA programs—that is 22% of all enrollments in post graduate degree programs. It is next only to the widely available Master of Arts (MA) program, which accounted for 35% of all enrollments. With just 2.3 lakh enrollments M. Sc is a distant third.

In short, the educational institutions imparting MBA education have leveraged the myth that an MBA education is a sureshot way to land in with a lucrative job.

Ironically, MBA is the only male bastion that remains among top degree programs. Out of the top five degrees (MA, MBA, MSc, MCA and MCom), it is only in MBA that males completely dominate. While in M.Sc, women account for slightly less than half (49.8%) of all enrollments, in all other three degrees they outnumber men.

Apart from degree (MBA, M.Sc, MA…)-based classification, the AISHE data also gives discipline wise classification of PG enrollments and Ph. D enrollments, which throws some light on where the interests are.  Of course, social science and management topped, with as much as 46.5% of all enrollments being in these two disciplines. The analysis of data throws some interesting facts.

  • The ratio of PhD enrollments to post graduate enrollments is the highest (as much as 89%) in Marine Science/Oceanography, followed by 34% in Gandhian studies. In most other disciplines, the figure is less than 20%. While the ratio per se  is not exactly meaningful, as the number of Ph. D enrollment in a year can more meaningfully be related to PG enrollments a few years back, it nevertheless is an indicator of where things are going.
  • The sex ratio in PhD programs is more than one only in three areas, where female enrollments outnumbered male enrollments. Two of them throw no surprise: Home Science and Women Studies. Linguistics is the only  other area where females outnumbered males in Ph. D enrollments.
  • While social sciences and management lead in number of PG enrollments, it is Science (29.3%) and Engineering & Technology (19.7%) that led in Ph. D enrollments.

Here are the top disciplines when it comes to PG enrollments.

PG enrollments

And here are the top disciplines in terms of Ph. D enrollments


The datasets used for this analysis were taken from, India’s open data portal.