Big Data : Case Studies, Best Practices and Why America Should Care

We know that Knowledge is Power. Due to Data Explosion more Data Scientists will be needed and being a Data Scientist becomes increasingly a “cool” profession. Needless to say that America should be preparing for the increased need for Predictive Analytics professionals in Research and Businesses.

Being able to collect, analyze and extract knowledge from a huge amount of Data is not only about Businesses being able to make the right decisions but also critical for a Country as a whole.

Being able to collect, analyze and extract knowledge from a huge amount of Data is not only about Businesses being able to make the right decisions but also critical for a Country as a whole. The more efficient and fast this cycle is, the better for the Country that puts Analytics to work.

This Blog post is actually about the words and phrases being used for this post : All words and phrases on the title of the post (and the introductory text) were carefully selected to produce specific thoughts which can be broken down in three parts :

Being a Data Scientist has high value.

“Case Studies” and “Best Practices” communicate to readers successful applications and knowledge worthwhile reading.

“America should”. This phrase obviously creates specific emotions and feelings to Americans.

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An Introduction To Hands-On Text Analytics In Python

“Case Study” and “Best Practices” were phrases found to be commonly associated with posts of high visibility. You might also get many views if you create a post which proves that whatever concept you are writing about is the right thing to do (for example write a post that clearly demonstrates yet another reason to use Social Media and have this post shown to Social Media Professionals). Regarding our example : It is very probable (and logical) for Data Miners to look at and then re-tweet (or otherwise share) information which is a “proof” about Data Mining being useful and also a “cool” profession. The higher concept / motive which works behind the scenes is that “I am doing the right job and this post proves it”.

You might also get many views by submitting a post which disproves well-accepted concepts or posts that demonstrate the difficulties that well-accepted concepts face : For example, if you were a Data Scientist or a BI Professional, you would be inclined to read a post titled “Big Data is a Big Hype”. Whether you will re-tweet or share the post is of course under your discretion. At this point it should be noted that there is a big difference between number of clicks of a post and the number of shares it got (by Retweeting it, Liking it, etc) because sharing a post means that this post is considered worthwhile to read.

All of the above (and much more) have been found by analyzing thousands of Blog posts along with their number of clicks and shares they got (either by RT’s , FaceBook “Likes”, etc) and this is what i will be presenting in Text Analytics World in New York this October. It was also very interesting to see that some findings are in tandem with findings discussed by Joseph Carrabis during the Text Analytics Summit 2011 in Boston back in May.

Of course it is not suggested that by using specific words and phrases you are guaranteed a successful post being re-tweeted from thousands of people and there are many reasons for this which i will not get into here. Additionally, Text Analytics cannot infer the higher meaning and concepts suggested within Text and this problem deserves a post on its own. This analysis however identifies concepts and/or phrases that point Bloggers and Marketers to look at a specific direction and with this knowledge to have increased probabilities for a successful Web presence. Again, this is an example of true Social Media Intelligence. Not (just) Reports.

So, in case that this post title immediately got your attention from other posts, you’ve just had a little taste of Predictive Analytics in action.