The Best Place to Start with Natural Language Operational Reporting

December 28, 2015 Katy De Leon

Natural Language Operational Reporting Image

Oftentimes, companies approach us with many ideas of where they “could" apply natural language operational reporting. As we all know, large enterprises have an abundance of reporting needs. But, how do you determine the best place to start?

Here are the top 3 things to consider when making your decision.

1. Select an Existing Report

The easiest place to start is with any type of report that is currently being produced. Say a performance report that explains how your sales or marketing campaigns are doing.

This ensures that the communication objectives and audiences of the report are already clearly defined, and it will be easy for an Advanced NLG system to replicate -- whether it’s a report for one person everyday, or a report for hundreds of people each month.

2. Identify Known and Accessible Data Sources

In order for Advanced NLG systems to write, they need data. Is your data readily available and extractable from a database? Is your data format consistent, well documented, and defined?

Our platform, Quill, can work with any structured data format, including SQL tables, CSV, and JSON. And, the more sample data sets you can provide, the better. This enables the system to understand and check the quality of your data, which will vastly accelerate the implementation of your natural language operational reporting project.

3. Determine Where You Need to Scale

In organizations big and small, analysts and operations managers spend countless hours producing reports. They must sift through information gathered from vast amounts of data, and clearly communicate the results in a way that’s easily consumable. And, the ad hoc requests keep flying in. Automation is the perfect solution to the ever-growing need for natural language operational reporting.

Advanced NLG systems that use automation immediately improve the data gathering, analytic and writing workflow by automatically writing high-quality reports -- in seconds -- to multiple audiences, freeing up employees to focus on more strategic, value-added activities.

Intelligent systems, like Quill, make it possible to codify what humans do into a machine. Why stick with manual report production when intelligent systems excel at automation?

Adding Advanced NLG capabilities will make it easier and faster for your organization to make sense of all of the data being collected and ensure a valuable return on your data investment.


https://www.narrativescience.com/automated-analyst?utm_source=uberflip&utm_medium=blogpost&utm_campaign=Automated_Analyst

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