Given the exponential increase in data, as well as the tools needed to store and analyze it, it seems like it must be getting easier for companies to understand and communicate the insights from available data sets. However, this is not always the case. Even with massive amounts of data being collected, companies still struggle with what to do with all of it, devoting significant resources to sift through and be able to extract valuable insights that are easily communicable.
This common problem was discussed in a recent webinar with two of our partners, Amazon Web Services (AWS), and Deloitte. As a partner with AWS through the AWS Competency Program and Deloitte through the Deloitte Catalyst Program, we discussed how leading enterprises are tapping into artificial intelligence, and natural language generation (NLG) specifically, to bring innovative, creative, and unique solutions to this data communication problem.
Combining efforts to put this webinar together were Kris Skrinak, Artificial Intelligence and Machine Learning Segment Lead for AWS; Ryan Kurt, VP of Partnerships at Narrative Science; and Sheetal Parikh, Senior Manager of Innovation at Deloitte. Skrinak, Kurt, and Parikh walked participants through the different uses of machine learning in AWS, an overview of how NLG solves the data communication problem, and the strategic ways in which Deloitte partners with Narrative Science to simplify data reporting and deliver value for its customers. You can view the webinar here, and you can keep reading below for the highlights from this session.
NLG and its Application in Data Communication
As strategic thought leaders in all three of their sectors, AWS, Deloitte, and Narrative Science have something in common: they want to partner with companies that share their passion for creating innovative solutions for problems that businesses often know that they have but don’t know how to solve. All three organizations are dedicated to solving this data communication issue, and each provides valuable aspects of the process.
Speaking to this issue first was AWS representative, Skrinak. He began with a quick overview of the many ways in which AWS is leading the charge for Machine Learning (ML) and software cloud interfaces. By enabling organizations to be much more agile in processes around data storage and analysis, AWS has drastically reduced barriers to get businesses up and running with AI solutions. These endeavors have led AWS to create an ML Competency program, which was intended to designate partners who have demonstrated technical proficiency and customer success in AI and ML. After extensive vetting of Narrative Science’s software, customer stories, and support materials, Narrative Science achieved AWS ML Competency status. In fact, according to Skrinak, Narrative Science was a “first pick when the ML Competency was announced.”
Excited about this opportunity, our head of partnerships, Ryan Kurt, gave a deeper dive into AI applications like our NLG platform, Quill. Quill is solving major pain points in data communication including time and resource allocation, human-prone inaccuracies, and employee attrition for large-data analysts (the turnover rate is increasing and ranked second among software turnover rates).
As Kurt explained, Quill drastically minimizes the amount of time that it takes to analyze and interpret data; with Quill, as long as a user provides intent and structured data, the software can create a readable, communicable, humanized story that enables companies to act more effectively and efficiently.
To emphasize the value in using Quill to improve data communication efficiency, Kurt turned it over to Deloitte’s Parikh. As a longtime partner with Narrative Science, Parikh briefly walked participants through the nearly 20 use cases that Narrative Science has created for Deloitte and its clients. Through Deloitte’s varied applications of NLG across multiple industries and business lines, it is proving that through automation and domain expertise, data communication does not have to be the tedious, time-consuming, and often frustrating job that it is now.