Asking the right questions. Seeking the relevant information.

jackLet us play a card guessing game. I have a standard pack of 52 cards. I pick one at random and ask you for the chances it is a Jack. That is not that difficult. It is 1/13.

But it is not any standard pack of cards. We do not know how many cards in it. In fact we do not even know if there are any Jacks in it. It is like those card dispenser contraptions that spit out a card, except of unknown size. An acceptable answer is, “I don’t know”, because the problem is not frequentist probability question. The problem space has switched from risk to uncertainty.

But we can’t end it there. What if we need to find out to help with a business decision? After all, our output as a leader is decision making- make that informed decision making under uncertainty. So we have to push forward. What if we can reduce the uncertainty by asking questions? What if I made available volumes of varied data (BigData) about the card?

Sidebar,  if you ever followed Jonah Lehrer, renowned Bob Dylan scholar who also wrote books on creativity, or you are one of the hustling valley entrepreneur type you might answer  the question by,

“Questions? I won’t ask questions. I will force your hand and turn the card over to see”.

But let us stay realistic here and continue. What questions would you ask? What relevant data would you seek in the BigData?

You could ask: What color is the card?
But is that relevant and help to reduce uncertainty?

BigData could say: In 200,000 card pickings there were 103,568 red and rest black cards
Is that BigData relevant?

You could ask: Is that a picture card?
This  helps to reduce uncertainty. If the answer is no, you are done. If the answer is yes you still have work to do, but are at a better state than before.

BigData could say: In the past 200,000 card pickings a few picture cards were sighted.
This helps too but not as effective as you actively seeking the information.

The role of information is to reduce uncertainty. If it does not help reduce uncertainty in decision making it is useless regardless of its volume and variety (or how many ever Vs you add).  BigData is not a substitution for application of mind. You the decision maker need to ask the right questions for the decision at hand and not let it spew you with interesting findings.

Do you ask the right questions?

Bigdata and Melman’s Hypothesis

Let us revisit our friends from Madagascar again. We find them stranded in the island nation of Madagascar, not knowing where they were.  Their conversation goes like this –

Yeah, here we are.
Where exactly is here?
San Diego.
San Diego?
White sandy beaches, cleverly
simulated natural environment,
wide open enclosures, I'm telling
you this could be the San Diego zoo.Complete with fake rocks.
Wow! That looks real.

Last time I wrote this about Melman’s assertion

A less forgiving view of Melman’s behavior will be that he started with a preconceived notion and then looked for evidence that supported his notion, ignoring those that would contradict it. Once we have made up your mind our cognitive biases nudge us to only talk to those who would support us and ask only questions that will add credence to our premise. No wonder he did not consider the fact that they were on sea shore and the San Diego zoo doesn’t open up to the sea (and the equatorial climate etc).

Let us see how their discussion will go in Bigdata world,

Yeah, here we are.
Where exactly is here?
San Diego.
San Diego?
White sandy beaches, with billions of grains of sand
look at the sand here
look at the sand there
look at sand over there
all these billions of grains of sand say this is San Diego

Unfortunately adding tons of sand (or petabytes of data that carries no additional information and has significant self-correlation ) does not help if you started with flawed pre-conceived notion and fail to ask what data will falsify your original claim.

Even with BigData you are stuck in Madagascar thinking it is San Diego.

Don’t just add data that make Madagascar look like San Diego. Seek data that will show why it is not.

Data, Data everywhere. And not an iota to help with decision making.