Food Data : The next target of Massive Analytics

It has been a very busy period since my last post but also a very interesting one. At the Social Media Analytics panel of the European Text Analytics Summit there was a question on “What would you suggest to new Entrepreneurs when it comes to Text Analytics”. The answer from most of us was “Specialize” : Build an Exceptionally Good vertical solution.

Text Analytics has been put to use for several verticals : Finance, Telecommunications, Pharmaceuticals to name a few. Perhaps the next important vertical for Text Analytics is something as Basic -but necessary- as Food.

Using Analytics for the Food Market is not just about analyzing millions of Tweets to understand and detect Trends on Food consumption, identifying ingredient associations that are liked by Consumers (e.g Olive Oil => Garlic) and the sentiment that a Food experience creates.

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Food Sector is a tremendous Market: Super Markets, Restaurants, Chefs, Books, Magazines, Television Series and Consumers. So Insights from Food Data Analytics could be used by all the “knowledge consumers” mentioned above.

In other words :

– Can we identify emerging trends on the Food Market? And if we can, who are the possible recipients of this knowledge?

– Can we understand and suggest new Food Experiences according to several metrics found whenever Food is discussed in Social Media?

-What other potential sources can be used to collect and then analyze Food Data?

– Can we understand how consumers make choices when it comes to Food?

-Can we Predict Popular Recipes? And how can we monetize from this knowledge?

Text Analytics is a key technology for transforming all the unstructured information on Food found on the Web. Predictive Analytics can be put to use if we can combine unstructured information with a target variable that we wish to predict.

One of the interesting tasks of a Data Miner is to be able to identify several -actionable and interesting- applications of both Data Mining and Text Mining given some Data. Of importance is also to find and/or to create new Data sources that can help in making better predictions. This is a challenging task but with careful considerations and lots of testing it may well prove to be a worthwhile and rewarding experience.

Coming back to Food Data we could potentially use mentions from Tweets, FB Posts and “Likes”, Blog and Website Posts to capture unstructured information. The hardest part is to be able to somehow incorporate more information about Consumer Behavior as this knowledge -and also to be able to predict Consumer Behavior – would be particularly interesting.

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There is a limitation on what Analytics can do especially when we are talking about Predicting Consumer Behavior. As always, proper Data Collection, Pre-processing and thorough Testing is required to reach consistent results.