Amazon launched as Junglee in India earlier this week. It is a price comparison site for various merchants to list their products and provide prices. Already quite a few significant Indian vendors (homeshop18, indiaplaza, healthkart, uRead, bookadda, etc) have listed their products on the website.
So, with the brand name of Amazon, one would think this will be a major development in the Indian online market. Right?
Well I don't think so.
Amazon has launched this price comparison site (and not an ecommerce website) only because Indian law prohibits 100% foreign direct investment (FDI) in multi-brand retail. Foreign investors who want to own Indian retail operations (e.g., international private equity firms, foreign internet retailers) have typically invested in the 'supply chain back-end' operations of retailers or in 'wholesale cash-and-carry' operations. Amazon apparently is not interested in playing such games.However let's be clear that Amazon will launch its own store-front in India the very day that Indian government allows FDI in multi-brand retail.
Given that scenario, it is likely that Junglee is only an attempt by Amazon to learn about the Indian ecommerce market and understand the price sensitivities of the Indian consumer. They are also getting pricing information freely from their competitors !! I assume many of the sellers listed would only be too happy to be acquired by Amazon when the time is right.
However some of the bigger players in the Indian market (flipkart, snapdeal, dealsandyou, fashionandyou, yebhi, myntra, etc) seem to be staying away from Junglee. They seem to understand the threat of giving structured pricing data to their future competitor.That is why, Junglee is unlikely to be a great force in the Indian e-commerce landscape.
Current Alexa rankings for India:
snapdeal 26
flipkart 30
homeshop18 124
Myntra 153
koovs 186
infibeam 208
indiaplaza 284
Junglee 533
While these rankings are based on 3 month averages and Junglee is likely to rise in rankings over the next 3 months, there is also a honeymoon effect which is likely to wane (also as buyers see lot of items with only 1-2 sellers).
My bet is that Junglee will make it to top 200 within the next 3 months, but will not generate as much traffic as the leading Indian e-commerce sites.
Thursday, February 9, 2012
Saturday, January 21, 2012
The big fuss about 'big data'
There has been growing interest in the idea of big data in the past few years. Indeed as McKinsey wrote about 'big data' (Read here) in May 2011, there has been an exponential rise in data available to businesses for taking better decisions. Indeed there will be a shortage of big data analysts in the US (and much of the Western world).
However in the growing swell of interest in big data, I find all sorts of companies and people talk about their data as 'big data'. This brings me to think: "How big should a dataset be to qualify as 'big data'?"
According to Wikipedia: "In information technology, big data[1] consists of datasets that grow so large that they become awkward to work with using on-hand database management tools. Difficulties include capture, storage,search, sharing, analytics,and visualizing. This trend continues because of the benefits of working with larger and larger datasets allowing analysts to "spot business trends, prevent diseases, combat crime."Though a moving target, current limits are on the order of terabytes, exabytes and zettabytes of data.
To me,it is not just important how big the data is, it is critically important how fast the data is generated and how fast it needs to be analyzed.
As this blog article from ikasoft correctly puts it:"The answer is not "Now the data is big" -- the answer is "Now the data is fast!" Google didn't become Google because their data was big -- Google went to MapReduce so they could keep growing the number of sites-crawled while still returning results in < 100 milliseconds, and now they're going to Google Instant because even 200 milliseconds isn't fast enough anymore. Consider all the action we're seeing today in NoSQL data stores -- the point is NOT that they are big -- the point is that apps need to quickly serve data that is globally partitioned and remarkably de-normalized. Even the best web-era app isn't successful if it isn't fast."
To me, only companies who generate terabytes of data every second (Google, Linkedin, facebook, twitter, Akamai, Yahoo, etc) are truly in the age of 'big data'. Companies who have terabyte+ databases over a year's time period can still stick to their RDBMS databases (and should quit calling themselves 'big data' companies).
Would love your thoughts!
However in the growing swell of interest in big data, I find all sorts of companies and people talk about their data as 'big data'. This brings me to think: "How big should a dataset be to qualify as 'big data'?"
According to Wikipedia: "In information technology, big data[1] consists of datasets that grow so large that they become awkward to work with using on-hand database management tools. Difficulties include capture, storage,search, sharing, analytics,and visualizing. This trend continues because of the benefits of working with larger and larger datasets allowing analysts to "spot business trends, prevent diseases, combat crime."Though a moving target, current limits are on the order of terabytes, exabytes and zettabytes of data.
To me,it is not just important how big the data is, it is critically important how fast the data is generated and how fast it needs to be analyzed.
As this blog article from ikasoft correctly puts it:"The answer is not "Now the data is big" -- the answer is "Now the data is fast!" Google didn't become Google because their data was big -- Google went to MapReduce so they could keep growing the number of sites-crawled while still returning results in < 100 milliseconds, and now they're going to Google Instant because even 200 milliseconds isn't fast enough anymore. Consider all the action we're seeing today in NoSQL data stores -- the point is NOT that they are big -- the point is that apps need to quickly serve data that is globally partitioned and remarkably de-normalized. Even the best web-era app isn't successful if it isn't fast."
To me, only companies who generate terabytes of data every second (Google, Linkedin, facebook, twitter, Akamai, Yahoo, etc) are truly in the age of 'big data'. Companies who have terabyte+ databases over a year's time period can still stick to their RDBMS databases (and should quit calling themselves 'big data' companies).
Would love your thoughts!
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