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Why All Of Your Employees Need To Understand Data

By Suhas Sreedhar

If the promise of big data could be summed up in one thought it would be this: Better information empowers better decision making. The past few years have seen big data rise to tremendous heights along with powerful, fast, reliable analytics that have been–and will continue to be–developed. So the first part of that promise, better information, is well on its way. What about the second part? With bigger and better data at our fingertips, how do we actually use them to make better decisions?

Decision-making in the workplace has always been a difficult skill to master. It requires making the most of the limited information you have–quickly but correctly understanding the data in the context of a problem.

What big data analytics helps most with is the “limited information” part. It gives you better insight into predicting what might happen based on what’s happened so far. Making decisions requires judging those data-based extrapolations. To do that, employees need to get comfortable with comprehending quantitative information.

Understanding data


Why Everyone Should Understand Data

According to Corporate Executive Board (CEB), an advisory company, information volume continues to grow by 60 percent a year. At the same time, 62 percent of workers lack the ability to assess that information and make decisions with it.

The CEB conducted a study in which it assessed workers on their ability to interpret relevant information. Based on the results, the CEB sorted the employees into three groups: “unquestioning empiricists,” who trusted analytics over their own judgements; “visceral decision makers,” who ignored data and stuck to their guts; and “informed skeptics,” who balanced the two. This last group showcased strong analytic skills and listening abilities, but were also able to gauge when it made sense to look beyond analytics and popular consensus.

People who would fall into the last group are in high demand in our big-data-driven business world. Unfortunately, there is currently not enough of them to go around.

According to McKinsey Global Institute, the United States faces a shortage of 140,000 to 190,000 workers who can analyze data. By 2018, it could face up to a 50 percent to 60 percent gap between demand and supply for data-savvy workers. If the supply doesn’t catch up, it’s possible that businesses won’t see significant return on the huge investment they are making in big data.

Creating A Data-Driven Office

But solving this problem by simply having more data experts in the workforce isn’t enough. Indeed, data is already ubiquitous–especially with the rising Internet of Things–and workers who are comfortable with data analysis need to become equally ubiquitous.

A massive push for data literacy is starting to happen. Many companies that have ventured into big data, like GE, are already touting the benefits of big data for everyday workers. Places like Harvard UniversityStanford University and Carnegie Mellon University now offer courses or master’s degrees with a focus on business intelligence and data analytics.

But how do companies go about increasing data literacy in the workplace? One way to get people thinking about data is to immerse them in it. A recent column in the Huffington Post highlights the need for companies to allow workers to experiment with data solutions to solve everyday problems. Companies should take care not to overwhelm users, added the article, but they should make data visible to everyone instead of just a selected few. Companies should also encourage employees to experiment and constantly improve their data analysis–and application–capabilities.

Google, which obviously has a data-driven culture, provides an example of how companies can use data to fix even the most non-technical problems: like Google employees consuming too many M&Ms.

By embracing data–in its myriad forms and vast scope–and exposing it to all aspects of an organization, more workers will become data-literate. This will enable companies to balance the need for data expertise with the people qualified to provide it.


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