2019-11-28 00:00:00
5 min read
Data does not lie. But it doesn't give you the whole truth either -- that is if you don't know how to question it. That's why you need analysts who are trained to ask your data the right questions to help you infer solid, objective facts. Facts that can help you make sound decisions and improve your business performance.
However, it becomes impractical for your small team of analysts to go through the large volumes of data generated every day to provide your employees with actionable insights. It is also not feasible to train all your employees to become certified data analysts who can make sense of all the dashboards, spreadsheets, and reports for making fact-based decisions.
What you can do, however, is cultivate data literacy among all your employees to make them better explorers of data. But the road to achieving data literacy in your workforce can be long and hard, riddled with multiple challenges.
Understanding the roadblocks to data literacy
Gaining awareness of the biggest obstacles of data literacy and identifying the appropriate methods to overcome them are the foremost steps towards building a data-literate organization. Following are the most common obstacles to achieving data literacy that enterprises face:
1. Misinterpretation of data
Generally, the process of interpreting data requires the analyst to clean, query, analyze, and present the data in the form of simplified dashboards, visualizations, and reports. These reports, although highly detailed, cannot be objectively interpreted by the non-analyst employees.
Even data visualization in the form of graphs and charts can be interpreted differently by different people. As a result, depending on who is viewing the report, the inferences made from it can widely vary. This can potentially lead to suboptimal decisions that may lead to undesirable outcomes.
2. Data comprehension at different levels of hierarchy
Another challenge in achieving data literacy is providing data that is relevant to employees at different levels of the organization. To make the most out of data, enterprises must encourage data-based decision-making at every level of the enterprise. This means that not just strategic decisions but even those pertaining to day-to-day operations must be informed by data.
However, most employees in non-leadership roles are not equipped with the tools to understand insights. They can be intimidated by data-based problem-solving. Result? Employees are unable to improve their work methods and solve problems despite the availability of the requisite data.
3. Depleting analyst resources
If given enough time, the enterprise’s analysts can create custom reports for different functions. However, creating multiple reports on a daily basis is not the most productive use of an analyst’s valuable time. Every hour an analyst spends on creating reports and populating dashboards is an hour not spent on innovation that can add greater value to the business.
To overcome these problems, businesses can leverage Natural Language Generation (NLG), enabled by machine learning and artificial intelligence.
Overcoming the challenges with Natural Language Generation
An effective way to overcome these challenges of data literacy is making data tell a story that everyone can follow. This means building a simple narrative in plain English or any language that your employees can easily understand. And Natural Language Generation (NLG) technology, an emerging frontier in AI research, can help to achieve exactly that.
NLG gives machines the ability to convert structured statistical data from spreadsheets and other databases into plain text summaries. Using NLG-powered tools can enable enterprises to publish reports written in natural, day-to-day language instead of confusing tables and graphs. These reports and dashboards give employees the exact information and insights they need to make quick decisions.
Such reports can also be used in conjunction with statistical tables and visualizations to tell employees what each component of a table or visualization means. By using such reports, not only can employees take immediate action by gaining the necessary insights, but also develop data literacy skills in the long term.
The use of natural language generation technology with AI-based data analytics capabilities can ensure that each employee gets data in a form that is relevant to their function, designation, and data expertise. NLG-generated reports can talk to each employee in a language that they best understand. By using NLG to decode data, employees can become more capable and confident in utilizing data for day-to-day decision-making. In other words, they will have the tools that can help them become data literate.
Using NLG-based report generation tools to improve data literacy can unburden your analysts and spare them from having to repetitively create and update reports for different employees. Analysts can, thus, spend more time on solving big-picture problems and add greater value to the organization. In the long run, NLG can help enterprises to empower all their employees with data, ultimately resulting in continuous growth and improvement.
About Phrazor
Phrazor empowers business users to effortlessly access their data and derive insights in language via no-code querying
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