The International Data Corporation estimates that by 2020, the digital universe will encompass 44 trillion gigabytes, a tenfold increase from 2013. Translation? The amount of data collected has increased immensely and will only continue to grow. The enterprise ecosystem is not only collecting data in every area of an organization, but businesses are aggregating this information for better market understanding and process streamlining. We’re cemented in an era of data description and are moving into an era of prediction and prescription. Historical information about successes and failures are necessary for determining the direction of action, but real-time analysis and insight of data has become the competitive advantage of successful organizations.

The data analyst, or any of the many variations of the title given to this role, has become one of the most valuable people in providing this insight across the enterprise and fostering a data-driven organization. Analysts and data teams not only collect and investigate business data but often transform this information into visualizations and dashboards intended to make trends and insights easier to recognize and digest by non-data experts. These data visualization dashboards are often created by a group of data experts for other internal teams, external groups, or clients who may not be as knowledgeable about analyzing visualizations and extracting the correct takeaways. As a result, analysts end up explaining dashboards via in-person presentations, written reports, phone meetings, or video walkthroughs.

Data analysis is often siloed, confined to separate pockets of an organization. Not all departments have a team of expert data analysts on standby who can read and interpret massive data sets to then turn insights into understandable reports on command. Even if they do, writing manual reports, highlighting key insights, and providing recommendations for action steps is tedious, time-consuming, and tends to be retroactive in nature. Maxed out analysts paired with dashboard viewers who struggle to gain a complete understanding of what they’re seeing leads to a detrimental underutilization of data throughout an organization, leaving one of the most valuable resources in an enterprise virtually untapped.

Too often, information is collected, assessed, and then delivered to decision-makers only to lay dormant for too long, unused completely, or misinterpreted and acted upon incorrectly. Retroactive analytics reporting is misaligned with modern BI platforms and the top analytics tools, which champion the necessity for real-time insights for the full picture of business operations and accurate decision-making. The future of business relies on live data analysis and prescriptive insight that results in immediate action across all areas of an enterprise. This is referred to as the democratization of data.

How do we bridge the gap between the skilled understanding of the expert analyst and the non-data expert reluctant to jump on board the analytics train?

Pair the valuable data expertise of your human assets with the right technology to help them communicate and deliver insights to all areas of your organization. In other words, eliminate delay in knowledge sharing and data understanding by giving your analysts the tools they need to automate and scale their insight instantly.

When presented to the perfect audience, visualizations speak for themselves. However, leaving room for doubt and misinterpretation among a broader audience with varying levels of data expertise weakens the power of the visualization and harms the overall business because it opens the door for misinformed decisions. Analysts should be given all the tools to create evidence-based, positive change in an enterprise. Reducing time spent in the later stages of their work, translating complex data sets into simple, concise action steps, will empower them to focus more time and effort on higher-value tasks that further the health of the enterprise.

Natural Language Generation Steps in to Bridge the Gap

Advancements in technology like natural language generation (NLG) allow data teams and analysts to automate written analytics for their dashboards using natural language, going so far as to break down the same dashboard in written format by viewer role and function within the company. With NLG, analysts use their own expertise to set up conditional logic that creates written analysis and scales their knowledge up through the enterprise. NLG solutions enable viewers to get real-time summaries, that update as viewers explore their dashboard.

For those in an organization, like executives, who may never log into a data visualization platform, analysts can deliver written reports via email or other preferred messaging system using automation from NLG software. This ensures that executives and others receiving reports are getting the most up to date information to make better, smarter, faster decisions. The beauty of written analysis is that it can be delivered virtually anywhere, regardless of mobile, desktop, or other platform differences.

Presenting analysis in a written format can be a game-changer when bridging the gap between data experts and busy executives and any non-data expert seeking to make data-driven decisions. A picture is worth a thousand words—in front of the right audience. Organizations that will stay ahead of the competition are those that arm their analysts with the tools they need to present information and insight that speaks to the audience at hand.