Google Launches a Data Prediction API 70
databuff writes "Google has released a data prediction API. The service helps users leverage historical data to make predictions that can guide real-time decisions. According to Google, the API can be used for prediction tasks ranging from product recommendations to churn analysis (predicting which customers are likely to switch to another provider). The API involves three simple steps: upload the data, train the model, then generate predictions. The API is currently available on an invitation-only basis." Google also recently announced several other API additions, including Buzz, Fonts, and Storage.
Data mining (Score:3, Informative)
When I used to work in the financial services industry we used to call this "data mining". The result is usually at best worthless and at worst dangerous as it is so often misused.
It's worth remembering the saying with data: "if you look hard enough, you can find anything you want to".
Available to US developers only (Score:2, Informative)
Google require you to have a current Storage-For-Developers account, which is only available for US parties currently.
Data mining certainly not worthless (Score:3, Informative)
It's absolutely data mining, but it's far from worthless.
Every time you go to Amazon and it recommends something to you, guess what, that's data mining using basically the same techniques that this service will use. And as you might expect, that equates to big $$$ for them (or else they wouldn't be bothering).
Many many fields use the technology, particularly the medical fields for analyzing the relationships between a large number of input variables (which may or may not be correlated) and some desired output variable. Spam filters, Google Search itself... all data mining algorithms. Nah, no money to be made there...
Now, the reality isn't as simple as 'upload the data, training the model, and generate predictions' normally. It takes time to figure out what factors to include, ETL'ing the training data from the actual source(s), plugging in algorithm parameters, and carefully validating your output model. Most models I've worked have taken several iterations to get right as you learn more about your input data relationships as you use the model.
And your second sentence is sadly true, if management wants a certain output, then the endeavor is pointless. But when used appropriately (and it's on the experts to explain the limitations of the tech to the users), this stuff is really powerful.
But will a lot of businesses be willing to send their 10 year history of accepted/declined credit card transactions with all the related demographic data to the cloud? Or their medical scenarios with the medical details of each patient? I think not. The type of data most mining projects use is critically sensitive. So I predict this will be limited to experimental users 'playing around', nothing more.