AWS now supports natural language forecasting queries in QuickSight Q

Amazon Web Services (AWS) has announced some notable new natural language querying capabilities that enable non-analysts to forecast future business outcomes.

By way of a quick recap, AWS first introduced its QuickSight business intelligence service back in 2015, allowing customers to visualize their data through dashboards and reports without requiring the technical prowess of a developer. Fast-forward to 2020, and AWS introduced natural language queries to the mix via QuickSight Q, enabling users to find answers simply by typing plain-English questions into a search bar, such as “show me this month’s sales by segment.”

Todays’ announcement builds on that, by serving answers to future-gazing questions.

ML-powered forecasting with Q, as AWS CEO Adam Selipsky referred to the new feature on stage at AWS re:Invent today, allows users to view business performance forecasts without involving any analysts or data scientists. All they have to do is enter “forecast” or “show me a forecast” into the search bar, followed by up to three metrics (e.g. “sales”), and Q will deliver the appropriate visualizations for that query.

On top of that, users can include filters, for example if they want to see sales for a specific region over a specific timeframe.

QuickSight Q: Forecasts with filters

Additionally, QuickSight Q will also now support natural language “why” questions, allowing non-analysts to drill down into the data and figure out what factors led to a particular outcome.

For example, if a graph shows that enrolment in a course dropped, the user might also want to know why it dropped. So they would start such a query with the word “why,” and then follow it up with a metric and a timeframe. In this case, the question could be: “Why did enrollment drop in 2021“?

QuickSight Q: “Why” questions

Both forecasting and “why” questions are available to users from today.

Read more about AWS re:Invent 2022 on TechCrunch

AWS now supports natural language forecasting queries in QuickSight Q by Paul Sawers originally published on TechCrunch



from TechCrunch https://ift.tt/fmdZENL
via IFTTT

Comments

Post a Comment

Popular posts from this blog

Tesla’s Dojo, a timeline

Elon Musk doesn’t want Tesla to be just an automaker. He wants Tesla to be an AI company, one that’s figured out how to make cars drive themselves.  Crucial to that mission is Dojo, Tesla’s custom-built supercomputer designed to train its Full Self-Driving (FSD) neural networks. FSD isn’t actually fully self-driving; it can perform some […] © 2024 TechCrunch. All rights reserved. For personal use only. from TechCrunch https://ift.tt/9uhRcnY via IFTTT
Read more

Autonomation

Image
“Jidoka” is a new one to me. TRI (Toyota Research Institute) CEO Gill Pratt described the concept as “Automation with a Human Touch.” The anglicized version of the notion is “Autonomation” — both are modified forms of “ automation,” in their respective languages. The word was originally applied to Toyota’s Production System, highlighting the need for human participation in the process. Quoting from Toyota here: Employing Jidoka principles throughout the production process is a vital element of the Toyota Production System, forcing imperfections to be immediately addressed by self-inspecting workers and thereby reducing the amount of work added to a defective product. Some automated machines can also function in the detection process, allowing human operatives to only be engaged when alerted to a problem. It’s a nice sentiment — a kind of harmony between robots and humans that ultimately plays to their respective strengths. There is, of course, a very real question of how fundamen...
Read more

Time for moderation?

Flexport CEO Ryan Petersen made it clear that while he was keen on buying Convoy because of the "incredible tech stack" it built, he wasn't buying its strategy. © 2023 TechCrunch. All rights reserved. For personal use only. from TechCrunch https://ift.tt/oB8wO3Q via IFTTT
Read more