How big data can evolve if our overall ability to convert data into really valuable info that then can drive effective decision making is so limited?

Use Delighted: https://delighted.com/

As someone who has built TAM models for various industries - I can attest this isn;t always easy. Many analyst firms (former analyst here) typically do some surveys and research among public company revenue reports for the year and then do some pixie dust extrapolating. The hard thing about a market where startups like Datameer play is that it is a) a nascent market and b) not a "zero sum" game. This makes it difficult to a) fully understand WHICH companies actually are in the market for this flavor of BI and b) know how the space is developing in terms of white space AND disrupting the entrenched BI giants. You might want to look at the press published versions (read: free) of analyst market share reports, and extrapolate from their. Also, back-channel gossip may give you some revenue numbers on these players - and if you assume pipeline is 3-4x revenue, build a model from there. Hope this helps.

There are so many ways to do it... Do you need this data for yourself, or you are planning to make a product around it? From what I see you can use Twitter API and Facebook Graph API (Are you comfortable programming?) Most of the students are active on social media so you will find lots of data. Facebook graph API will give you a number of likes and comments to all the posts of you competitors. You can analyze all the posts of your competitors. Using Twitter API you can get all the twits that use certain hashtags or mentions. If you are not into coding, but still want to get social media information, you can take a look at tools like IBM Watson ANalytics ($30 for personal use), it natively connects to Twitter API, and you don't have to be a programmer at all. It is intuitive and easy to learn. Analytics Canvas connects to Facebook Graph API (it's free for 30 days of trial). Unfortunately, you would not be able to collect any personal information from social media at large scale (age, income, gender, etc.), because it violates all the laws about privacy on the Internet. You can use census data instead. Google Sheets are a very handy tool if you are planning to use this information for personal research. You can set up a spreadsheet and add some Java script to make it collect all information from competitor's blogs, and also sites like Reddit. Finally, you can try web scraping (it's not the best, but can speed up the process). A tool like OutWitHub will collect information from websites (such as website reviews) based on the structure you provide (select html tags). You can collect thousands of reviews in one day if you automate it (paid version). Very easy to use. Note: not all the websites are open to this method, review their policies to make sure you are not violating their terms of service. Reviews belong to the website where they were published. If you REALLY need personal data (like how much they earn and how much they spend, etc.), just print out 100 questionnaires and go to Student Union Building of Dalhousie University. Most of the students will share any personal data in exchange for a Tim Horton's gift card that gets them a free coffee. It is probably the least technical and fastest way to get all the data you need. Hope this helps.

In my experience coaching hundreds of people, anytime you go against your gut (aka intuition) you will lose. Data is important to consider, but your gut often knows the path.

You'll find that looking at top line revenue alone won't provide much value to you if you are asking the question "Is my advertising spend worth it?" Typically, in advertising I want to know the Lifetime Value of the customer or the Average Revenue Per User (ARPU). Then, it boils down to : If I spend $100 to acquire this new customer and he spends $150 over the lifetime of his paid relationship with me then, yes--- my ad spend is worth it. On the other hand: I could spend 10K this month in ads. I could sign up 20K in users/customers/top line revenue. But my data will tell me I spent MORE per customer/user than they are actually worth over the average lifetime. So that's what usually answers the "Is it worth it" question in any advertising scenario. 1. How much to acquire the user? 2. How much does the user spend over the avg lifetime? If it doesn't make sense, it's not worth it, regardless of what your top line revenue tells you. You might find this blog post on cohort analysts valuable as it goes into more detail. (I have no affiliation.) https://apsalar.com/blog/2012/01/how-to-use-cohort-analysis-to-improve-revenue/