/ Why companies must close the data literacy divide

Technology and people are frequently seen as the two keys to unlocking the power of data. As organizations have sought to hire more data specialists, they’ve also turned to self-service analytics capabilities to extend the influence of data across various departments and teams. Democratizing data throughout a company can help everyone from the CEO to frontline employees—to do their jobs faster, better and smarter. This year, the business intelligence and analytics market is forecasted by Gartner to grow to $18.3 billion propelled by this need for greater agility, accessibility and deeper insights.

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Most companies recognize data in the hands of a few data experts can be powerful, but data at the fingertips of many is what will be truly transformational.As organizations look to increase data access for their managers and employees, there’s an implied expectation that they will know what to do with the data once it’s shared with them. However, many companies are discovering simply making data more pervasive and accessible isn’t enough.

Imagine you assembled an extensive library comprised of the finest literary works in the world. How valuable would it be to someone who is illiterate? Until they can read and appreciate it, the library remains just a useless collection of inked paper. Similarly, all of the rich data visualizations and intelligence built into today’s self-service analytics tools can be negated by a simple deficiency in data literacy, which can be defined as the ability to understand, use and communicate data effectively. Increasingly, this data literacy divide will impede organizations of all shapes and sizes from reaping higher rewards from their data investments.

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Some may feel technology should be able to sufficiently address this issue, but it can’t foster data literacy on its own. While some advanced analytics solutions might be able to empower a new generation of citizen data scientists, these individuals will come from the subset of employees who are already adept with numbers. While it’s exciting to hear Gartner anticipates 40% of data science tasks will be automated by 2020, most of this automation will apply to the mundane, time-consuming task of wrangling data that currently bogs down data experts. Even prescriptive analytics will be unwise for people who are unable to spot-check whether the recommended actions are appropriate based on the underlying data.We may hope our education system will eventually fix this data literacy problem. While post-secondary institutions have slowly come to grasp the need for better analytical training, K-12 education continues to lag behind the data literacy needs of the modern workforce. In his 2009 TED Talk, mathematician Arthur Benjamin advocated it’s time for mathematics curriculum to shift from analog to digital, and for statistics and probability to replace calculus at the top of the mathematics pyramid. It’s difficult to say when this much-needed shift will occur.

**This article was originally published on Forbes.com on March 9, 2017.

Tags: Analytics, data