The Harvard Business Review just published an article entitled Why You’re Not Getting Value from Your Data Science.

The article describes how data scientists are overwhelmed by the complexity and quantity of data, and how business experts, for their part, are underwhelmed by the tangible output of those data scientists.

Why this failure to generate business value?

The data scientists were obsessed with fine tuning machine learning models rather than answering fresh questions, losing sight of the main purpose of their work: generating business value. In fact, when the author asked a room filled with 150 data scientists which of them had ever generated proven business value, no one raised their hand.

Surprised?

Me too. I would have expected about five hands to go up.

Although today's technology has given data scientists great tools, along with an abundance of low-hanging fruit, the problem remains that a recent surge in unfocused data science programs, staffed with newly minted analysts, has created a high potential for program failure.

A recent surge in unfocused data science programs, staffed with newly minted analysts, has created a high potential for program failure

From my experience leading and evaluating data science programs over the years, I would call out three key problems commonly seen within data science and big data analytics programs.

A failure to bridge the gap between business goals and analytic efforts

Math-y people focus on math-y work, producing a stream of faster, more accurate machine learning tools while leveraging the latest artificial intelligence toolkits. The ‘good but irrelevant’ data science tools are carefully crafted into ‘even better but still irrelevant’ tools.

A failure to work in a lean and agile manner

Focusing on a few high-risk efforts while receiving little or no intermediate feedback translates into a high likelihood of project failure. It’s called ‘putting all of your eggs in one basket’, and it’s a bad idea.

Lack of discipline in measuring results

I’d bet that most of the 150 data scientists surveyed in this study did produce measurable business value, despite not knowing it. I’d also bet that no one had the discipline to measure that business value.

The ‘good but irrelevant’ data science tools are carefully crafted into ‘even better but still irrelevant’ tools

Four tips to start getting business value from your data science efforts

Here is my advice for businesses that are uncertain about the value they are getting from their data science programs

Live and breathe business intuition. There are non-technical people in your company who have developed a deep understanding of the customer, the product, the market. Talk with them before starting your analytic efforts. Harvest their intuition. Go back to them every few days to show your data and initial results. They will tell you if you’re doing something blatantly wrong. Oh, and make a habit of using your company’s website / product / services yourself (this is known as ‘eating your own dog food’). Measure Results. Don’t start a data science project unless you know why you’re doing it and what it looks like when it succeeds. Are you looking to increase conversion rates? Marketing ROI? Market Share? Customer Lifetime Value? Measure where you are now, where you think you could be, and how much revenue that translates into. As they say, ‘if you’re not keeping score, you’re just practicing’ Work in an agile manner. Incorporate many feedback cycles. Work in terms of minimum viable products and short delivery cycles. Agile project management is key to making an analytics program effective, and it ties in closely with the first point above. Business intuition will fuel the feedback loops and provide input for specifying many of the project deliverables. Appoint a leader with understanding of both business goals and analytic possibilities. Ideally, one person can match business value with analytic potential and lead a team of data scientists in attaining high value results. Finding such a person can be difficult (see my article Recruiting a Chief Data Scientist).

Harvest the intuition of your non-technical colleagues

As with most things that are worth doing, making a data science program effective can take substantial effort and will require several iterations before the program is structured in an effective way. Don’t give up if the initial program seems to be failing. As Mckinsey said in their recent report, The age of analytics: Competing in a data-driven world, "Organizations that can harness these capabilities effectively will be able to create significant value and differentiate themselves, while others will find themselves increasingly at a disadvantage."

"Organizations that can harness these capabilities effectively will be able to create significant value and differentiate themselves, while others will find themselves increasingly at a disadvantage"

I’d love to hear your thoughts and experiences in the comments below.

This article was originally posted on my data science blog. I've written extensively on business uses of data science in my book Big Data Demystified.