How can I aggregate data from online sources about a specific topic?

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.

It's hard to get a definite answer to this as these companies will not tell us how their algorithms work. Having said that: Based on my experience working with these APIs, it is totally possible to implement a real-time search like "socialmention.com" purely based on the APIs from Google, Twitter, Yahoo, or Facebook. On the other hand, crawling and saving all that data just for the eventuality that a customer might search for it is probably not economically viable and will also violate the Terms of Services of most of these APIs. Bottom line is: They are probably not crawling and caching.

I would disagree with the premise that "big data" isn't being converted into valuable information. Modern advertising is being driven by exactly this ability. The problem lies not with the information, or lack thereof, but with the decision makers. Lack of vision at both the strategic and tactical levels of decision, fear of change, among other things are at the root of the problem.

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.

First off, I have several people I could introduce. I'd also like to know the industry you're operating in, what the data looks like, where it comes from, and how much it needs to be cleaned up if putting into a relational database, or if the better solution would be a distributed file system like MongoDB where you don't necessarily need to normalize the data. Also, if you're a startup, or if the company is well established with many existing customers and if this is for a new initiative. Assuming you're working with a relational database, which it sounds like you are, you will want to implement something like Tableau or build out a custom dashboard using Google Charts, HighCharts, D3Js, or any number of other potential dashboarding/visualization solutions, which usually involves some programming/scripting in JavaScript. There are paid solutions like Tableau (which is amazingly powerful), and then there are free/open source options. I'd be happy to talk about possible ways to architect the solution, and discover who you would need once I understand the variables more. If you're building a web application, then you will likely need someone who is also a full stack developer, meaning they can handle building the back end and the front end in addition to the data requirements. Many early startups choose Ruby on Rails (because there is a ton of open source code out there for it) with Twitter Bootstrap (modified) and in order to visualize the data, they will need to work with JavaScript. It makes sense to have this person act as initial product and to derive the insights out of the data. They're pretty much the only person who can do this anyways, because they're the ones on the data. If you're in an early stage startup, I would recommend the strongest business owner (usually the early stage startup's CEO) be directly involved with this person on communicating what value your solution brings to clients, and what they pay you for, and in brainstorming on potential features and reports. Once the solution becomes established, and many customers start using it directly, there should be a different product person interfacing to those customers over time, gathering feature requests from customers and bringing it back to the Data Scientist/Analyst who spends their time working on the data. Depending on whether the solutions are SQL or NoSQL or hybrid, there are different types of Data Science professionals you should consider: 1. Data Scientist 2. Data Engineer 3. Data Modeler/Analyst 1. The Data Scientist handles experimenting with the data, and is able to prove statistically significant events and statistically valid arguments. Normally, this person would have modeling skills with Matlab, R, or perhaps SAS, and they should also have some programming/scripting skills with C++ or Python. It really depends on your whole environment and the flow of data. In my experience, Data Scientists that exclusively use SAS are sometimes extremely skilled PhD level statisticians and focused exclusively on the accuracy of the models (which is okay), but often not sufficiently skilled to fit within an early startup's big data environment in today's world and handle all of the responsibilities you'd like them to handle described in your question. I'm am not bad mouthing SAS people as they are often the MOST talented mathematicians and I have a great deal of respect for their minds, but if they do not have the programming skills, they become isolated within a group without a Data Engineer helping them along. Often a SAS user trying to fit into this environment will force you to use a stack of technologies that a skilled Data Architect would not recommend using. It takes programming in some object oriented language to fit into today's big data environments, and the better Data Scientists are using hybrid functional and OOP programming languages like Scala. Extremely hard to find Data Scientist can also work with graph databases like Neo4j, Titan, or Apache Giraph. 2. The Data Engineer, if you're dealing with a firehose of data like Twitter and capturing it into a NoSQL architecture, this is the person who would prepare the data for the Data Scientist to analyze. They often are capable of using machine learning libraries like WEKA to transform data, or techniques like MapReduce on Hadoop. 3. The Data Modeler/Analyst is someone who can use a tool like SAS, SPSS, Matlab, or even R, probably a very strong advanced Excel user, but likely won't be a strong programmer, although perhaps they will have a computer science degree and have some academic programming experience. The most important thing to watch out for is someone who is too academic, and has not been proven to deliver a solution in the real world. This will really screw you up if you're a startup, and could be the reason you fail. Often, the startup will run out of money due to the time it takes to deliver a complete solution or in the startup's case, a minimally viable product. Ask for examples of their work, and specifically dig into what it is that they did for that solution. I've tried to cover a pretty broad range of possibilities here, but it's best to talk in specifics. I'd be happy to discuss this with you in detail. To answer your question, is it perfectly reasonable for someone to handle all of the responsibilities described in your question, if you find the right type of person with the appropriate skills, and a history of success.