How to choose analytical projects?

Even though terms like "big data" and "data scientists" are biggest online "term" polluters - because they mean different things to different people, there is one word that is getting more and more deserved prominence.

And that is word "actionable"! And beauty of it is: it means what it says!

So, we all know about actionable analytics, actionable data patterns, even actionable reporting - and underlying message is simple - don't call it actionable unless you can do something about it, or with it!

And premise is - if you act on it - there will be some controllable, positive reaction that can be measured within business context in business terms.

And business context is clear: reduce costs, risks, expenses and increase revenue, profits, productivity, benefits, opportunities - regardless if our action carries once-off, or tactical benefits as oppose to strategic one. Action must map somehow to higher business goals.

And if it cannot - it is just science project and not valid use case!

And I would argue that similar logic should be applied when choosing big data projects. And litmus test is simple one: if you cannot act on it, make business case of it - it is a science project and not something that will have business impact. This is not to say that there is no place for science projects in analytical and research labs of commercial entities - but, it should be obvious where the focus should be!

And for so long - it wasn't!

And when academics are telling us that - "pragmatic" and "greedy" business should be in driving seat of technological super cars (and big data technology is one the fastest and most expensive) - maybe it is time to go fishing, come back and then rethink how we should drive this "monster".

1 Response

  1. Alzmtaaak-informaaion found, problem solved, thanks!

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