Good comparison and very thoughtful insights into the data scientist role

Very hard to find some guy who is an industry expert (working knowlegde), an IT Guy and knows mathematics/Statistics.

How about providing a tool to the industry expert abstracting the complexity of later 2 from him

Regards
Sid

I like the way you talk about real time decision making and knowledge discovery

@Rama, really liked the way you put it.

An implicit assumption behind a lot of the breathless commentary in the blogosphere seems to be this: if you can unearth an insight from data and present it to the client/customer/end-user, they will immediately embrace it, act on it, get value from it, and thank you profusely for it.

In reality, the response from a customer is more like: “oh great. one more so-called insight. now i have to worry about what the heck to do with this darn thing, on top of all the stuff that’s part of my day job. thanks a lot!”

What customers need are decision recommendations with supporting evidence, not “insights”.

Totally agree with your assessment: starting with raw data is asking for trouble.

The way we have set up our analytics pipelines is that they always work towards a decision point and then we have a feedback mechanism to test in the future if our decision was the right one. If it turns out to have been the wrong decision, we can go back and figure out what we did wrong in our analysis. For real-time decision making it is typically a bit easier to be disciplined as the analytics coding is all done to trigger some test. For knowledge discovery I believe you need two ingredients:

1- critical mass of statistical thinking, and
2- deep statistical skills with a solid historical perspective

Critical mass is needed so that you don’t have one person in the corner generating reports that the rest of the organization simply ignores. Deep skills and historical perspective are needed to properly direct the proper algorithms and sentiment. I truly believe in the value of historical perspective as it grounds the analytics with a richer context.

By focusing on a decision it is easier to properly allocate the right analytical resources, although that is still a very difficult process if you don’t have infinite skills and resources.

In your example you have a business problem that you are trying to solve, a defined set of data, a process to systematically test on a continuous basis and measurable metrics you want to influence. You are certainly using more data to your advantage.

However, when I hear about big data in most cases the starting point is the data and not the problem/metric, which I contend is not the best place to start.