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Data Analytics: Art or Science

, | September 30, 2016 | By

by Nilesh Kulkarni – 

Art or science?

That’s a question that has echoed through the ages in many different disciplines, including management, medicine, history, and accounting. The very fact that we so frequently find ourselves choosing between art OR science offers testament to the fact that art and science are fundamentally different and mutually exclusive. A thing might be one or the other, but not both.

But what are the differences between art and science? What separates them so dramatically? Here’s how I define them…

Art is an expression or application of human ideas, creativity, imagination and intuition, typically in a visual form such as a painting or a sculpture. Art is also about beauty. But as the wise old saying instructs, beauty lies in the eyes of the beholder, so art is very subjective and intangible. It really can’t be truly measured, evaluated and compared.

Science, on the other hand, is absolutely objective by definition. It can be measured precisely, and its truth indisputably proven. Science is also methodical and predictive and repeatable; two molecules of hydrogen reacting with one molecule of oxygen will produce two molecules of water – every time.

What About Analytics?

Now let’s consider analytics for a moment.

To borrow from Wikipedia1: ” Analytics is the discovery, interpretation, and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics2, computer programming3 and operations research4 to quantify performance. Analytics often favors data visualization5 to communicate insight.”

We employ analytics primarily to review organizational data using statistical techniques, to identify data patterns by analyzing large volumes of data, and to present the results in a way business can understand. And most importantly, we use analytics to improve the quality of the decision-making process, and to shape business decisions and actions to best achieve organizational targets.

So is data analytics science or art?

Based on the definition above, we must conclude that analytics is a science. After all, the term itself was born out of the use of information technology.

But the term ‘analytics’ encompasses several components, including:

  • Applications/systems that capture and feed business data
  • Integration rules, processes, and technologies
  • A semantic layer to express information in business terms
  • Visualization

And we can add one more to the list above: challenges due to the increasing complexities around the volume, variety, velocity, and veracity of data generation. After all, analytics does require extensive technical understanding of the tools and technologies associated, and demands logical thinking to comprehend properly.

Removing the Art

The driving idea behind analytics has been to take intuition out of the equation, replacing it with pure data-driven analysis. With IOT and the explosion of data in recent years, it’s virtually impossible to comprehend and analyze all of the data that’s currently generated.

And business processes have become so complex and dynamic that — not only are there several variables in any decision-making process — but the variables also morph and change at lightning speed. As a result, outsmarting the competition requires faster decision-making.

As a result, without a sound appreciation for data analytics as a science, analytics projects are bound to fail. It’s a simple certainty.

So it’s certainly not a surprise that analytics project teams are usually comprised mostly of a host of technical architects and specialists. They work with cutting-edge technologies to churn out loads of data and produce results that are extremely sleek and cool, through impressive yet easy-to-use visualizations.

But what is the true end-result of the work of these analytics teams? Do their efforts help business at all times? Does the work always provide actionable insights?

Unfortunately, the answer to these questions is NO!

The Art of Science

Why does the science of analytics so frequently fail to deliver the insights and guidance expected of it? In truth, there are a number of reasons6 that analytics often disappoints.

But perhaps one often overlooked reason for the failure is that analytics really isn’t purely a science at all; it also requires a component of artistry. And the reason that analytics is part art is because it demands that a dollop of intuition be mixed into the mass of numbers and facts. Analytics without intuition is like leaving the yeast out of a batch of bread – the results simply fall flat.

John Naisbitt (Former executive with IBM and Eastman Kodak; American writer in the area of futures studies; author of several international best sellers like Megatrends and Re-inventing the Corporation.) put it perfectly – “Intuition becomes increasingly valuable in the new information society precisely because there is so much data.”

Like any other art, analytics does require the intangible and somewhat unexplainable factor of intuition to make it successful. The subjectivity of analytics comes through intuition, inquisitiveness, creativity, and the imagination of the people involved – not just through hard numbers and facts.

Intuit the correct questions to ask, and data analytics will likely provide the answers. But if you don’t know what to ask, analytics can’t help, just as a map can’t help without a destination in mind.

Analytics requires thinking out of the box to come up with solutions that may not have been thought of before – or to put it differently: thinking like an artist. The science in analytics is essentially just an enabler, not a solution. It provides a vehicle for translating intuition into results.

A Recipe for Success

In sum, I believe that data analytics requires a healthy mix of science and art to be successful. Without the science, it’s not likely that organizations can derive any meaningful information from their sea of data. But art is a smaller, though crucial make-or-break ingredient for the successful application of analytics within an organization.

By the way, one of the greatest minds of modern times quite agrees with me. Albert Einstein, the great physicist, felt that his own insights did not come ultimately from logic or mathematics, but from imagination and intuition: “When I examine myself and my methods of thought, I come close to the conclusion that the gift of imagination has meant more to me than any talent for absorbing absolute knowledge.”

Elaborating, he added, “All great achievements of science must start from intuitive knowledge. I believe in intuition and inspiration…. At times I feel certain I am right while not knowing the reason.” Thus, his famous statement that, for creative work in science, “Imagination is more important than knowledge” (Calaprice, 2000, 22, 287, 10).

Could there be a stronger advocate to support my views?

Works Cited

1. Wikipedia "Analytics" Retrieved from

2. Wikipedia "Statistics" Retrieved from

3. Wikipedia "Computer Programming" Retrieved from

4. Wikipedia "Operations Research" Retrieved from

5. Wikipedia "Data and Information Visualization" Retreived from

6. Forbes (2015, Dec 12) "Top 5 Reasons Why Analytics Projects Fail"
Retrieved from