Recently, we announced the release of MicroStrategy 10.7. One exciting feature in our latest release is our integration with natural language generation (NLG) providers Narrative Science and Automated Insights. NLG technology lets MicroStrategy users tell better stories with their data by generating narratives that explain underlying trends and patterns in data through a natural language story. Incorporating NLG into a dashboard allows people with little technical background or data analysis experience to more easily understand the data behind any dashboard.

The integration is a huge step forward toward making our platform accessible to a wider range of users. For a partner perspective, we talked with Mary Grace Glascott, Director of Product Marketing at Narrative Science, about the potential for NLG, their integration with MicroStrategy, and her company’s mission to make this capability available to as many people as possible. Here’s what she had to say:

Why was it so important to add NLG to MicroStrategy 10.7?

"Narratives for MicroStrategy" automatically transforms visualizations and data into Intelligent Narratives, or stories that explain the insights within data, charts, and graphs. NLG extends the ability for all users, regardless of skill set, to get the most out of their data analysis. With NLG, they can focus on higher-value tasks because there’s no need to manually interpret charts and graphs.

With NLG, how much faster can a non-technical MicroStrategy user arrive at meaningful insights with their data?

Once the software is downloaded and installed, the technology works immediately. Prior to this extension, users would try to analyze something and figure out what it means, naturally holding their own bias during interpretation. With “Narratives for MicroStrategy,” any bias is automatically identified so users can focus on the important results.

Additionally, sharing dashboards across departments or roles is easier. Without NLG, analyzing the data and adding qualitative information to a dashboard might have taken up to an hour or two for an employee without a background in data analysis.

What are some other results of including NLG alongside data analysis?

It reduces opportunities for erroneous interpretation by less-technical users. That’s why we want to make sure all users can use these tools. By automating analysis and presenting findings in a way that anyone can understand and act on, we not only lower the barrier to entry for data analytics, but also ensure that insights match the dataset.

For more skilled users, it accelerates the ability to do data analysis. An expert analyst can immediately find the most important trends, anomalies, and insights in their data. Rather than having a single gatekeeper of BI, teams made up of diverse skill sets can better collaborate and share thoughts on a consistent data narrative.