Business Intelligence Buyer's Guide

How Organizations Can Implement a Culture of Data Literacy

How Organizations Can Implement a Culture of Data Literacy

How Organizations Can Implement a Culture of Data Literacy

This is part of Solutions Review’s Premium Content Series, a collection of contributed columns written by industry experts in maturing software categories. In this submission, AtScale Co-Founder and CTO Dave Mariani offers his take on how you can implement a culture of data literacy in your organization.

SR Finds 106While the global analytics market is increasing, 60 percent of organizations cite company cultures that don’t “fully understand or value fact-based decision-making” as their biggest obstacle to successfully implementing analytics. This opposition to data literacy makes it difficult for organizations to invest in the analytics tools and personnel they need to gain actionable insights from their data and improve business outcomes. So how can leaders encourage a culture of data literacy in their businesses?

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Current Obstacles to Data Literacy

The amount of data businesses generate today is unprecedented, which is great for improving opportunities, but it also means there’s more information that employees have to sift through. Data governance is a big obstacle to data literacy because many organizations don’t know what data they have or where it’s stored. Businesses need a central repository for the data they collect. They also need to integrate this repository with their third-party applications to capture all relevant data.

Additionally, some employees may be opposed to gaining data literacy as a skill because they see analytics as something that falls outside of their job purview. Leadership has to tout the benefits of data literacy for everyone in the organization, starting with the ability to get answers to queries faster and make data-driven decisions for their department.

Even if employees are on board, many companies don’t have the analytics tools in place that make data literacy worthwhile. If employees don’t have access to analytics software or it’s too difficult for non-technical people to use, they’re going to get frustrated with their inability to access the data they need to make important decisions. While analytics software can be expensive, it typically has a good ROI when paired with a culture of data literacy.

Data Literacy Starts at the Top

In order for businesses to create a culture of data literacy, their executive team has to be on board. They have to not only be willing to invest in analytics tools and data literacy training for their employees, but they also have to become data literate themselves in order to set an example for their team. When managers are data literate, they put intrinsic pressure on individual contributors to also become data literate, so they can effectively contribute to the team.

Leaders also have to make sure their teams understand what’s expected of them when it comes to data literacy. Mariska Veenhof, analytics lead at, explains, “Implementing data governance includes determining the correct owners of certain data sources and making sure they understand the responsibilities that come with their data ownership role.” Giving employees data ownership roles can make them more invested in the data literacy process because they feel like leadership is investing in them.

AI Is a Crucial Part of Self-Service BI

When analytics is confined to technical teams, those employees can get overburdened quickly. “When an organization has weak analytics tools and/or weak data literacy, they come to rely on their data and analytics teams for low-level requests,” said Megan Brown, director of knowledge management and data literacy at Starbucks. “This can be trouble — it’s not possible for an analytics team to scale enough to make up for limited tools and data skills.”

Instead, organizations have to provide employees from all departments with the tools they need to self-serve for data-driven decisions. Providing a semantic layer gives business users the ability to extract insights using business-friendly language queries vs. SQL or a coding language.

Brown goes on to say, “Further, your business’s analytics professionals may be too removed from business decisions to put the insights into use — they depend on their business stakeholders to understand and apply the findings.” While having an analytics team is great, they likely won’t know how to use all of the insights they’re able to gather from the data. Data literacy enables users of all technical skill levels to ensure they’re making informed decisions for the business and that none of the BI team’s efforts go to waste.

Organizations Must Institute Training and Quality Checks

In order to institute data literacy across the organization, businesses must create educational programs that teach employees how to use data and analytics software in relation to their roles. They don’t need to know how data relates to every aspect of the business, but they do need to be able to gather insights about their respective departments. Employees need to know how to access and summarize the data using the analytics tools already in place.

In addition to educating employees, regular data quality checks ensure employees are pulling accurate information from the correct sources and analyzing it properly. It helps if there’s a single source of truth for an organization’s data. Managers should use mistakes as a learning opportunity to further coach employees and help them reach the ultimate goal of data literacy.

Successful Organizations Require Both Analytics and Data Literacy

While both analytics and data literacy is important on their own, together they provide businesses with the opportunities to make data-driven decisions. Creating a culture that prioritizes data literacy allows organizations to reduce the reliance on technical data teams for low-level data requests, while giving business users the tools they need to gain valuable insights about their business. And once employees are data literate, organizations can add artificial intelligence and machine learning algorithms to further improve business outcomes.

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