Master “The Art of Thinking Clearly” Before Making Your Analytics Investments!

I lead the technology industry business unit for a fast-growing analytics firm. And one of the perks of my job is the numerous conversations I get to have with a variety of prospects or clients.

I recently read the novel ‘The Art of Thinking Clearly’ by Rolf Dobelli, which deals with systematic cognitive errors of human beings as a result of our evolution.

I enjoyed reading the book immensely, relating those cognitive errors to what I experience every day. I am selecting a few rampant cognitive errors mentioned in the book and linking them to situations I have come across.

Neo-mania

Def: The mania for all things new and shiny.

The desire to have marketing automation, machine learning models, artificial intelligence, and other such cutting-edge capabilities is intense in organizations today and probably justified. However, it’s alarming to see many of these efforts promise a very large ROI but never deliver it.

I once had an executive of a large organization say without batting an eyelid -

“We have invested millions in a big data environment, and we now need to figure how to use it.”

I am always disappointed when I hear things like — “Can you tell me how I can use machine learning capabilities in my business/function?” I wonder if anyone would say it if I replaced ‘machine learning’ with ‘statistics’. Conversely, the best use of ML/AI is when the need or a business problem was identified first and ML/AI emerged as a solution.

Swimmer’s Body Illusion

Def: Confusing the process of selection for effect.

Rolf explains in the book that swimmers don’t have great bodies because they swim a lot; they are swimmers because their physique is suitable for swimming.

I have seen companies falling to this illusion when they look to replicate an analytics success story from companies that seem to be doing very well and claiming that analytics is driving their success. It’s safe to say that the retail industry has the most mature analytics capability, but nobody is blaming the analytics team at Sears and Macy’s for their decline.

In my opinion, most companies would succeed in their analytics initiatives if they just are more rational and use data to be just that.

(Read more: Building an analytics-first organization)

Authority Bias

Def: The tendency of human beings to follow authority without question, a culture most visible in religion but equally prominent in businesses.

When I ask my clients why they were following a particular strategy, the responses most often indicate an authority bias. Interestingly, the bias seems more obvious when the perceived results from the strategy are less obvious.

My advice to my clients has always been to focus on outcomes and not on the process. I am amazed even now that such a conversation isn’t always easy.

The Paradox of Choice

Def: Too many choices causing decision paralysis and subsequently leading to poor decisions.

The number of analytics solutions, vendors, and technologies today is confounding. One would expect then that most clients would be happy with the choices they make. However, I haven’t seen a company that is entirely happy with its selection of business intelligence (BI) or analytics platforms.

If only they hadn’t compared the tools with each other but against a prioritized list of their own needs, they would have come to a better decision.

Default Effect/Status Quo Bias

Def: A strong tendency to cling to the way things are, even when they put us at a disadvantage.

Most tech companies with revenues of $500 million to $2 billion are hyper-focused on growth.

The most important part of achieving growth is to size the market right, find the account segments to focus on, and then have the sales force or channel partners gain market share.

One would imagine that these companies would have nailed this process well. I am yet to come across one.

(Read more: Business Transformation — How Big Data Analytics Helps)

Alternative Blindness

Def: Systematically forgetting to compare an existing offer with the next-best alternative.

I have run across companies with no analytics capabilities looking to hire a team first, spending a million dollars, and losing quite a bit of time before they see any capability in action. When we provide them the alternative to spend less than half a million and have the capabilities stand up in six months, one would imagine it would be a no-brainer. It is, but only to our clients.

*Please note that the terms and their descriptions are from Rolf Dobelli’s book.

(This article was authored by Maruti Peri and first appeared on LinkedIn, the BRIDGEi2i blog & Datafloq.)