The days of not collecting data as a company are over — long over. The International Data Corporation (IDC) estimates that by 2020, the digital universe will encompass 44 trillion gigabytes, a tenfold increase from 2013. 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’ve already hit the era of “every business decision must be driven by data.” Departments that previously operated on premonition, status quo or frameworks of the past must now justify their forward-looking strategy with supporting metrics. Historical information about successes and failures is necessary for determining the direction of action, but analysis and insight of data as it happens have become the competitive advantage of successful organizations.
The data analyst, or any of the many variations of that title given to this role, has become one of the most valuable positions in providing this insight across the enterprise. This role not only collects and investigates business data but often transforms this information into visualizations and dashboards that are intended to make trends in results easier to spot and digest. Often, these dashboards are constructed by a group of data experts for viewing by groups who may not be as knowledgeable about drilling down into visualizations and extracting the correct takeaways that influence their business decisions. Unfortunately, this ends up forcing these analysts to explain dashboards (which in an ideal world, wouldn’t need any explanation) via written reports, phone meetings or video walkthroughs.
Additionally, data analysis is often confined to separate pockets of an organization. Not all departments have a team of expert data analysts on standby who can decipher massive data sets and then turn these insights into understandable reports on command. Even if they do, writing manual reports highlighting key insights and providing recommendations for action steps is tedious and can easily become retroactive in nature. With a perfect storm of maxed out analysts and a lack of clear direction and understanding from dashboard consumers, underutilization of an organization’s data leaves the most valuable resource an enterprise has virtually untapped.
Too often, information is collected, assessed and then delivered to decision makers, only to lay dormant for too long, unused completely or even worse — misinterpreted and acted upon incorrectly. Retroactive analytics reporting is misaligned with modern business intelligence (BI) platforms and the top analytics tools, which champion the necessity for real-time insight on business operations for accurate decision-making. The future of business will rely on real-time data analysis and prescriptive insight that results in immediate action across all areas of an enterprise. So how exactly do we close the gap between the skills and data understanding of the expert analyst and the pressed-for-time, strategy-focused executive? Pair the valuable data expertise of your human assets with the right technology that helps them convey 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 throughout the enterprise.
Make additions to the data visualization and dashboard platforms your organization already licenses. When presented to the perfect audience, visualizations may speak for themselves. However, leaving room for doubt and misinterpretation among a broader audience with varying levels of data expertise diminishes the analyst’s hard work and harms the overall business. Analysts should be given all the tools to create evidence-based positive change in an enterprise. A way for them to focus more time and effort on the heavy-lifting analysis component of their job is to reduce time spent on the later stages of their work: translating the complex into simple, concise action steps for non-data experts.
Advancements in areas like natural language generation (NLG) allow analysts to automate analysis presented in natural language for their dashboards, going so far as to break down the same dashboard in written format by viewer role. With NLG, analysts can use the data across a dashboard and their own expertise to set up conditional logic that creates written analysis. It’s a process similar to the way analysts transform data into the visualizations within a dashboard. NLG solutions can also enable real-time summaries that update as viewers explore and drill down in a dashboard without constant manual intervention. So what about the executives who have and will never log into one of these data visualization platforms? Meet them where they live by delivering written reports via email or other preferred messaging system. That’s the beauty of written analysis: It can be delivered virtually anywhere, regardless of mobile, desktop or other platform operating system differences.
In bridging the gap between data experts and busy, decision making executives, presenting analysis in a written format can be a game-changer. 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 speak to the audience at hand.