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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.

It’s also important to not assume that analytics tools will do all the work. “Technology alone never solves any business problem,” says Greg Layok, senior director of consulting firm West Monroe’s technology practice. “In the rush to create data lakes, organizations are instead ending up with swamps, or a morass of information that is too difficult for anyone to figure out.”

Technology doesn’t solve analytics problems, Layok says. “First, identify a business problem, then ask, ‘What data do I need to solve this?’” he says. “This will help you identify the gaps of data within your organization.”

 

Myth 10: Data analytics should be a separate department

In some organizations data analytics operates as a department on its own, and in others it is deeply embedded into a cross-functional team, says Steven Mast, president and chief innovation officer at Delvinia, a consulting and data collection company.

“However, with the explosion of data across all areas of business and the speed at which change is occurring, the department model does not work,” Mast says. “As organizations become more customer-centric, data-driven analytics specialists should be at the core of a business unit, not operating as a department that you call for support.”

Many of the complex problems organizations face today are within the business units, and many solutions to these problems are hidden in data, Mast says. “Data scientists and specialists, working closely with these business units, and using large data sets and artificial intelligence, will be key to incubating the next generation of products, services, and customer experiences.”

 

Myth 11: Analytics is just for PhDs

It’s great to have lots of well-educated people on the analytics team, but it’s not a requirement for success.

“Companies tend to think that without PhDs on board they won’t be able to perform best-in-class analytics,” Saiz says. “Modern analytics demand a blend of skills — those who are savvy in emerging technologies and open source software. Building ‘pods’ with different skills [including] big data architects, data engineers, data scientists, experts in data visualization, and more, is what makes the difference.”

 

Myth 12: AI will destroy jobs and wreck the economy

The introduction of new technology historically has disrupted many jobs and industries, and there are fears that artificial intellgience will eliminate the need for people to perform certain tasks.

“Artificial intelligence solutions are much better than people at solving certain kinds of problems,” Schulze says. “AI can read faster, remember more, and calculate complex mathematical relationships better than any person. However, AI is not capable of dealing with truly novel situations, which is where humans excel.”

To be sure, Schulze says, certain jobs have already been lost or diminished by the growth of AI, and others will follow. “Still, our human capacity to understand and work around completely unforeseen circumstances will not be replaced by any currently understood AI technology,” he says. “For the foreseeable future, the most effective approach to AI will be to augment human abilities with AI systems that perform some of the ‘heavy lifting’ where algorithms outperform people. Though many jobs will change as a result of AI, people will continue to be a critical part of this business ecosystem.”

 

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