The next big thing on the net

Someone directed me to a link to the new project by Wolfram people called Wolfram Alpha saying that it was the next big thing on the net. My initial reaction to the claim was - "Ah, yet another big claimer!"

But 5 minutes into the site, and I was taken over by it. This project seeks to integrate all knowledge into one. You can put in any query and it gives you the "right" results. Much unlike Google, which directs you to further sites that might or might not (though they generally do) contain relevant information - Alpha gives you numbers and statistics about anything that can be represented by numbers. Like you could key in GDP of South Africa - And know all that you wanted to know in terms of numbers. Likewise if you key in "GE" in the stocks section, you could see how the trend for GE stocks has been for the last 1 week, 1 month, 1 year and 5 years! besides it shows you its market capitalization and the beta. If you search for AIDS deaths, then you get statistics for deaths across the world.

This thing could indeed become the next big thing in the net!

PS- The afore mentioned friend was Abhishek Gandhi, fondly(?) known as Tool here at IITM. He claims his connect to the first family of the country, but we are all too used to people who fake surnames to gather mileage !

Logic of a "free" customer

For the past few days, I have been thinking about how much is a 'free' customer worth.
For example, All the social networking sites like Facebook, Orkut, MySpace offer free logins for anyone. Similar is the case for eBay.

For example on eBay, all buyers are free registrants . Only the sellers pay - and that too when they get a product to sell. This gives rise to an interesting situation - without 'free' buyers, there are no sellers - and without the sellers the revenue model fails.

Similar is the case with Orkut - without the free users, there are no ads, and hence no money. But its slightly complicated here as Orkut is owned by Google. so there might be opportunity costs involved. like for example, when someone clicks on ads by google from a third party site, google must be paying something to that site as well (as it does in adsense). So when Google values its Orkut users, it will also factor in these savings.

Facebook epitomizes this type of revenue model - it allows advertisers to select their target group very effectively. Allowing them to streamline their ad via features such as
* Location
* Age
* Sex

* Keywords
* Education
* Workplace

* Relationship Status
* Relationship Interests
* Languages



But exactly how useful are these customers? there might be some customers who never click on any ads. Or for that matter, sell products on eBay. So these people never make money for the company. On second thoughts, these might still generate some revenue for eBay - by increasing the selling price through competitive bidding.


This thought cropped up in my head while listening to a presentation on the revenue models followed by browsers. Since then I have not been able to stop thinking about it. So HAD to publish it. Anyone who has some idea on this, please do comment/ contact me. I wish to learn more about this fascinating concept.

MicroFinance - The double bottom Line

MFIs(Micro Finance Institutions) are businesses, they exist to make money; as do any other business.
One can not charity on borrowed/dreamt-up money. Or as the recent banking goof-ups(?) have told us, One cant enjoy luxury on dreamt-up money.

But for now, we are talking about MFIs, which take pride (and rightly so) in doing a social good - by lending loans to fulfill dreams, ambitions, needs.

So its only natural that they have two bottom lines, one that talks about the financial status and other, about the social.

This also in turn tells about the company's average loan balance as a % of per capital GDP of the society of operation. Essentially it talks about the the average lending as a % of average earning capacity of the locality.

A double bottom line undoubtedly helps MFIs attract soft lending and investments from socially responsible investors However,having a double bottom line also means that MFIs may also undertake less profitable activities if it fits the social good framework. After all, if it reflects positively on the bottom line, it is a good investment. These efforts can lead to a higher cost structure for the business, although in some cases, this may also be rewarded with higher yields.

PS- I was reading through the ways in which a valuation of MFI is conducted, and this seemed so different from the single minded bottom-line corporate culture that I had an insurmountable urge to write about it.

Theory Vs Data - "All models are wrong, and increasingly you can succeed without them." ?

I am not claiming that I am competent enough to have a deciding say in the newest (and the HOTTEST) debate doing rounds on the web right now.But as a technology follower and leader-to-be, I feel obliged to add my own comments on this.

".......Scientists are trained to recognize that correlation is not causation, that no conclusions should be drawn simply on the basis of correlation between X and Y (it could just be a coincidence). Instead, you must understand the underlying mechanisms that connect the two. Once you have a model, you can connect the data sets with confidence. Data without a model is just noise. But faced with massive data, this approach to science — hypothesize, model, test — is becoming obsolete.........."

".......There is now a better way. Petabytes allow us to say: "Correlation is enough." We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot......"

I dont think that data can ever replace models. True, that Mr. Venter has done a lot of good for modern biology, But he is building on the knowledge of genes and replicating mechanisms discovered earlier, using the scientific methods of observation-hypothesis-validation.

Data is good only up to the limits we already have the theory ready for. It certainly is a great help in fully comprehending the implications and applications of the theoretical background we already have. But it in no means can generate new theory to do the future testing. It can only provide us with what something is but not why it is so.

PS - I love statistics , so please dont cite my short-handedness for stats as the reason why I sided with theory.