What exactly is data literacy, and how do we get there? In the context of an enterprise organization, data literacy takes on a plethora of meanings and prompts a string of questions. Do you know what your data is showing you? Are you asking the right questions when looking at a dashboard? Can you decipher the data visualizations and pull out the right insights, and do you truly understand what certain data trends mean and how you should act on them?

TechTarget defines data literacy as the ability to pinpoint useful information from data. This is not unlike the general definition of literacy, which is the ability to gather information from the written word. Deriving information from the core of how we communicate via the written word may actually be part of the answer to data literacy woes.

Depleting Analyst Resources

For most companies, the current state of business intelligence (BI) starts with the analyst. These members of the team play an essential role in funneling streams of data into a central location, cleaning that data for real business use and exploring areas of interest that they then translate into visualizations, dashboards and reports. At the end of the day, one of their main goals is making trends in this data easier to identify, understand and act upon.

Many times, however, dashboards are created by technically skilled data experts, but often for business users with less specialized data skill sets. To compound this misalignment, these users need to take their dashboard interpretations and make real-world, impactful business decisions. To be data literate requires a deeper understanding of the metrics your company tracks, which sources of the business impact each KPI and how they all work together to paint a more complete picture about the state of the organization.

Too often, expert analysts bridge this gap in understanding by explaining these nuances via phone call, meeting, presentation, etc. Top data talent ends up spending more time on explaining things rather than driving toward innovation. Even a data novice can see how inefficient investments in personnel and in BI software can become if such manual processes are invoked on a regular basis.

Augment The Viewer, Not Just The Analyst

Because data analysts and BI platforms are such valuable assets, a company should never settle for depleting or misusing them. Instead, efforts should be placed on multiplying their effectiveness with new implementation methods and innovative technology.

The efficiency of data experts can be improved so that higher-value work can become their core focus if given the right tools. One of the emerging frontiers of business intelligence is the platform integration with natural language generation (NLG) solutions. These solutions are what my company specializes in, and I have seen firsthand that NLG allows analysts to codify their expertise and automate the written reporting component of their responsibilities. For example, an analyst can transform their knowledge into conditional logic that writes an insightful narrative to help spread better data understanding — all using natural language generation software. As a result, more of their time can be spent on higher-return tasks rather than manually explaining dashboards for business stakeholders.

Make It Personal

A key obstacle for business stakeholders is that they don’t have time to sit through an explanation of technical data jargon, even if it’s painted as integral to the larger organization. Instead, a solution I suggest is to make it personal and pick a specific instance of that stakeholder’s daily responsibilities and show how a better understanding of their data improves their work life. Whether this better understanding ignites a minor improvement in their task efficiency or it fosters a much larger, improved sense of confidence in their decisions, picking a specific and integral part of that person’s job and demonstrating how data literacy results in tangible progress can make all the difference in fostering a wider movement to become more data literate in your company.

Work Smarter (Not Harder) To Make Everyone Data Literate

Successful data-driven organizations don’t simply want to know the answer to what is happening; rather, they aim to fully understand the “why this is happening” solution behind every major business question. Truly end-to-end solutions must account for communicating answers to data consumers who may not have the level of skill as the person who built the dashboard or analytics report. They also must give the individual data consumer and business stakeholder a feeling of improvement in their work, however small that improvement is at the beginning of their data literacy journey.

Not every person in your organization needs to be a degree-touting data scientist, but every person in your organization needs to be data literate. In order to achieve this, don’t reinvent the wheel: Give your analysts and data experts the tools they need to quickly explore and report on your business data. Creating an in-house data visualization or analytics platform may seem like a better option than making a large software purchase.

You can also reinforce that data-driven decision making is a core value in your organization. Ensure that everyone is utilizing your data analysis personnel and tools to drive their strategy and actions. Emphasize that all decisions should stem from data insights; this will help ensure that all organizational roles are using data regularly and becoming familiar with analysis that is relevant to their daily job functions.

Across the enterprise, there is a need for the robust yet flexible reporting and communication of insights from business data. It’s not enough to think just in terms of the way that data is collected, organized, modeled and analyzed — the modern enterprise also needs to fully account for the various levels of expertise of the data consumer by presenting key information through methods everyone can utilize and truly understand.

It is a continuous and widespread process, but improving data literacy can transform an organization from constant delays and retroactive thinking to leading the pack as an innovative, intelligent enterprise.