Increasing Data Literacy to Drive Better Decision-making

PinIt

The goal of data literacy is to build an environment where every employee has the resources and confidence to perform their own analytics or data science work.

Gartner defines data literacy as “the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied, and the ability to describe the use case, application and resulting value.” Unfortunately, only 24 percent of the workforce is confident in their ability to read, work with, analyze, and argue with data, according to the Data Literacy Project.

At its core, data literacy in a business context means equipping employees with the knowledge of how to use data to power strategic insights. Most organizations have the data in their possession but lack the technical skills across the organization to sufficiently analyze and interpret it – otherwise known as the “skills gap.” But why should companies care about improving data literacy?

The business value of data literacy

When done effectively, closing this skills gap leads to better data-driven decision-making throughout the organization. In the ideal scenario where 100 percent of an organization’s workforce is confident in their ability to interpret and analyze data, new strategic insights gleaned across every team can yield competitive advantages for the business. For example, product and design teams can translate customer behavioral data to develop the next in-demand feature and be first to the market with it. Similar examples of the potential value of data insights exist across nearly every department, yet often data science teams, analysts, or business intelligence (BI) power users are the gatekeepers of these insights. The lack of widespread data literacy prevents many companies from acting on data in a timely manner to see business outcomes come to life.

Meanwhile, data scientists and engineers continue to be in high demand and are stretched incredibly thin. Instead of solely assigning data analytics tasks to these teams, the goal of data literacy is to build an environment where every employee has the resources and confidence to perform their own analytics or data science work.

See also: Actionable Insights at Scale Needs New Data Approach

Where to start

Currently, only 34% of organizations provide data literacy training. Driving a data-first philosophy needs to start at the top with executive buy-in and leaders promoting the business benefits of data literacy. It’s critical for the C-suite to understand the impact data can have. In fact, a study from the Data Literacy Index found that organizations with higher corporate data literacy scores yielded up to $540 million in higher enterprise value compared to those with low scores.

To design a data literacy program that sticks, leaders must empower employees to work with data, regardless of their role or level of expertise. Like with any skill, employees will come to the table with varying levels of confidence and historical knowledge. It’s helpful to start this process by conducting an assessment to evaluate these baselines or have employees self-identify areas of strength or weakness with data. This information will serve as the foundation for the overall program and make sure employees have the proper educational resources and training tailored to their needs.

Getting it off the ground

From there, developing an effective and robust data literacy program takes time and should be unique to every organization’s needs. Getting the inaugural program off the ground is often the most time-consuming part. For some organizations, it might be helpful to tap the data science team or Center of Excellence, if one exists, to act as champions for this project who can speak to the strategic benefits that data could bring to individual teams.

Additionally, having data scientists and engineers in a designated “data science champion” or “analytics champion” position can help evolve how other departments are viewed across the organization. Instead of considering engineers or analysts as a helpdesk-style IT resource, they’ll be seen as teachers and enablers for the rest of the workforce.

Organizations don’t have to start from scratch to find a comprehensive program for all skill levels. There is a wealth of data literacy courses available online that can be customized depending on the desired outcomes and level of investment.

Data literacy needs data democratization

An important part of any data literacy program is removing the barriers to data that exist in the first place. If data is not able to get into the hands of the business users who want it, there is no point in teaching them how to read, work with and analyze data. To enable true data democratization, organizations need to reevaluate their data management architectures. Behind every data-driven company is a robust cloud data infrastructure that can hold wide and vast amounts of data, enabling every user to search and access the type of insights that empower business decisions. Without a reliable foundation, there might be some questions about the quality of the data and whether they can even trust the analytics in the first place.

To be truly data-driven, organizations need to view data as a consistent, strategic advantage. In the long run, organizations that empower users with the right tools and skills to analyze data at the moments they need it most will reap the benefits that come with a data-literate workforce. As organizations continue to aggregate more data than ever before, having teams that can turn these resources into business insights will be an incredibly powerful value driver.

Dave Armlin

About Dave Armlin

Dave Armlin is VP Solution Architecture and Customer Success at ChaosSearch.

Leave a Reply

Your email address will not be published.