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Filtering by Category: Big data

In an age of big data, asking the right (business) question shapes value

Herve Utheza

As the 2014 Consumer Electronics Show opens today in Las Vegas, the buzz is already on wearables and intelligent health related sensors, captors, body activity measurement.

I will forget for a while the whole “UltraHD / 4K” noise. It is an understandable next push of the industry to keep the TV set market afloat. But it is also, in my humble opinion, one wave which comes too soon at consumers, who are not yet ready to surf it. 3D came and went, and I think that 4K comes at least 5 to 8 years too soon.


Back to health monitoring and big data in general.

As we enter, irreversibly, in a world where data is created, generated, tracked, stored, manipulated about every physical object, human being, behaviors, we are entering in a new age of humanity.

This is exciting, and fuels the conversations on the buses of Google, Twitter, and other tech giants’ young engineers who never had to conceive a world without this big data.

However, this all reminds me of the California Gold Rush.

There was a lot of dirt moved, for very few to find gold, and just a few more to make in fine much more money just selling jeans to the workers, and picks and shovels.

How ready is Marge Simpson to see a daily histogram of her heart beats, number of steps, and draw by herself the right conclusions to change behavior?

If the recent public debates with the FDA is any indication, there is a vast task at hand: educating the consumer marketplace about all those health and medical related tools, for them to be understood correctly. Billions of dollars of liability, malpractice, consumer product liability and related insurances ride on it.

So I posit that asking the right business questions is going to be the way we extract value out of this big data world.

·         What is the valuable question we can take action on?

·         What actions are we taking, based on which data set?

·         How are we applying the right filters to the raw data to ensure that we extract meaningful responses?

·         What are the mathematical algorithms and rules we will apply to the selected dataset, to make sense of it?

·         How do we visually represent the results? (note that this is the big danger of the big data world: there may not be a “product” anytime soon if every use requires some form of custom visualization… venture capitalists, beware)

·         How do we map those results and translate them into laymen language?


Those questions are applicable to both a B2B, B2C and B2B2C environment.


A big data strategy has to start by asking the right questions.