When Edward De Bono spoke about the six thinking hats way back, he definitely did not visualize the emerging field of data science. The hats were meant to be an aid for lateral thinking and brainstorming. Over the years, the hats have seen many creative uses in the corporate world. 3M used the six thinking hats for new product development, a new type of duct tape. Boeing used the six thinking hats to resolve a management vs. union deadlock. And J. Walter Thompson is reported to have used the method to develop an ad campaign for the Ford Focus.

Can the 6 thinking hats really enable such lateral thinking or am I talking through my hat? And where do data scientists come into this argument?

An average data-scientist does have to wear multiple hats to do his job, from mining through data to delivering insights that make a difference. But the six thinking hats are not just tools for brainstorming, they are the tools for his job; hats she/he needs to juggle everyday, all 6 of them, to be precise, to be successful at work.

Here’s how:

The white hat:

1.Bono says: the White Hat calls for information known or needed. "The facts, just the facts."

The data scientist needs: Data, just the data, in all its myriad shapes and sizes from historical customer transactions to syndicated research papers, from the unstructured text data in social media to the king of all data, Big Data. Data is his morning caffeine fix, the starting point for his work, his job, his life.

The blue hat:

2.Bono says: the blue hat is used to manage the thinking process. It ensures that the 'Six Thinking Hats' guidelines are observed.

The data scientist needs: The blue hat to generate insights from the cartloads of data he sees every hour. The blue hat applies to the statistical algorithms he needs to use; shuffling from logistic regression one day to perfecting Natural Language processing the next; from analyzing normality one day to researching Black Swan events the next, as easily as if they were multi-colored candies to choose from a candy store.

The black hat:

3.Bono says: The Black Hat is judgment, the devil's advocate or why something may not work.

The data scientist needs the black hat to develop hypothesis based analysis. The fanciest statistical model may not work if the assumptions behind it are not sound. Donning a black hat, the data scientist can play the roles of the lawyer and the accused, both at the same time and free himself from the paralysis that data can lead to without arguing the whys and hows of analysis at every step of the way.

The yellow hat:

4. Bono says: The Yellow Hat symbolizes brightness and optimism.

The data scientist needs large doses of the yellow hat, everyday. Just when the deadline approaches, the project scope gets changed; just when he thought his model was working perfectly, a new variable gets literally dropped out of a hat, not one of Bono’s 6. Yellow hat, is not just a hat, it’s a survival tool for the data scientist’s day in the highs and lows of the data valley.

The red hat:

5. Bono says: The Red Hat: signifies feelings, hunches and intuition.

Intuitively, the data scientist does not depend on intuition, right? He has his data to back him. But then there are dark spaces that even the biggest of big data cannot reach, there are business rules that need to be applied for every statistical model; just that right way of looking at data that develops into the “Erureka” beer and diaper insights moment every data scientist dreams of making on the road to creating new “Moneyball” history. Call it trial and error, or simply, red hat.

The green hat:

6. Bono says: The Green Hat: focuses on creativity: the possibilities, alternatives and new ideas.

The data scientist needs the green hat to think out of his self-made data box. In today’s data rich, insight poor (DRIP) world, the green hat helps the data scientist to think of new innovative techniques, use visualization and infographic tools and make his data and insights show the real business value to busy executives who need something that stands out from the data clutter in their constant effort to hang on to their hats and take decisions that spell ROI and business impact.

There it is. 6 thinking hats in the life of the data scientist. Does it make you want to toss your hat into the ring? Juggling six hats all day long to get an ounce of insight from an ocean of data?

Pablo Picasso once said, “To draw, you must close your eyes and sing.” But juggling six hats? That might deserve a resounding hats off, all 6 of them.