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12 myths of data analytics debunked

Bob Violino | Nov. 28, 2017
From data concerns to staffing needs to technology combinations, data analytics misconceptions abound. Here’s a no-bull look at how to leverage data science to deliver bona fide business results.


Myth 6: Data science is a mysterious ‘black art’

The discipline of data science has received lots of attention in recent years and sometimes generates confusion as to exactly what it is. Basically, it involves the use of algorithms to find patterns in data.

“Data science seems mysterious because these algorithms are capable of analyzing more variables and larger data sets than the human mind can comprehend,” says Trevor Schulze, CIO at data storage company Micron.

“As computing power and memory have scaled in recent years, we are now able to quickly solve problems that would have been impractical to solve with any technique just 10 years ago,” Schulze says. “Data science is the natural evolution of statistical inference techniques that have been well understood for decades. There is no mystery to data science, once you understand the math.”


Myth 7: To do more data science, you need more data scientists

Data scientists are among the most in-demand of all technology professionals these days. Perhaps organizations can get by with fewer of these professionals if they redirect what they’re working on.

“A lot of data scientist time is spent on non-value-add activities like finding data sets, getting the data to a place where it can be worked on, and transforming and cleaning it,” Mason says. “Given how hard it is to hire data scientists, those low-value tasks aren't what you want them working on.

Uber's Michelangelo platform allows data scientists to focus on feature engineering, extraction, and analysis, rather than schlepping data around, and so allows them to be massively more productive,” Mason says.


Myth 8: Analytics takes too long

These days getting things done quickly — whether it’s rushing a product or service to market or responding to a customer inquiry in near real time — is a big competitive consideration for companies. Analytics sounds like something that takes a long time to perform, running counter to the goal of achieving speed and agility.

“The myth still exists that these types of projects take too long and are quite complex,” Saiz says. “At the end of the day it’s all about talent. With the right mix of skills and the application of agile methodologies, big questions can be answered in days or weeks, not months.”


Myth 9: Technology is the hard part

With the ever-increasing number of technologies available today, selecting the right combination of tools to deploy and integrate to get the desired results from the analytics team is no walk in the park, says James Burke, director of IT sourcing and digital advisory services at consulting firm ISG.

The real hard part, however, is “putting together the organizational structure and operating model to put all of what is required from a people, process, technology perspective together,” Burke says. “Also, how you do this inside of or ‘next to’ your existing organization is what seems to be most difficult for organizations.”


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