DataStories is mentioned in Gartner’s Market Guide for Augmented Analytics 2022

Gartner Market Guide for Augmented Analytics 2022

Datastories is proud to be mentioned once again by Gartner in their new augmented analytics market guide (see also the mention of 2019, the same year as we were named a cool vendor in Augmented Analytics ). We are happy to see that Gartner analysts continue seeing us as representative vendor in their new and updated Market Guide for Augmented Analytics given their knowledge of the market and requests of their clients.

DataStories,posted on 9th January 2023

This year’s guide is adjusting the market definition calling augmented analytics:

Capabilities that automate data preparation, insight generation and multipersona collaboration put analytics and business intelligence and data science and machine learning platforms on a collision course.
Gartner Market Guide for Augmented Analytics page 1

Gartner analysts are noticing the following trends in our market:

  1. Collision of BI, analytics and data science with multipersona collaboration,

  2. Verticalization of tools to deliver specific contextualised insights and domain-centric knowledge.

  3. Continuing trend of large vendors acquiring startups to get the first mover advantage.- - The acquisition trend has also touched us when we joined Partners in Performance in July 2021 to strengthen advanced analytics offering in heavy-asset industries.

Multipersona users colliding

Not only advanced analytics and business intelligence are colliding, but target markets for users are colliding as well. We observe the same trend in industrial applications, where corporate analytics programs require seamless collaboration of OT and IT.

Multipersona collaboration has always been critical for DataStories to support - data engineers, data scientists, business analysts and automation engineers must be able to effectively collaborate within projects, while speaking different languages. To enable this we introduced seamless connectivity between our DataStories SDK (written in Python, the language of data scientists and engineers), and DataStories platform (browser-based tool clear to operators and engineers).

Vendors verticalising

The past year’s history of DataStories also shows focused verticalization. An influx of new clients from operations in asset intensive industries especially in metals, mining, food, recycling, pulp&paper and energy has made us streamline and optimise our product towards insight implementation, model deployment and performance monitoring in very specific operation environments.

Story-telling vs automated insights

While this years market guide is mentioning us as an example of automated story-telling, this past year we’ve been mostly releasing features for automated insights. (footnote: The marketguide only allows a vendor to be mentioned in one category - and we are happy to be in story-telling.)

Data storytelling combines interactive data visualization with narrative techniques to deliver insights in compelling, easily assimilated forms. Automated data storytelling is an augmented analytics capability that generates news-style data stories
Gartner Market Guide for Augmented Analytics page 3A
- this has been our USP from the founding. DataStories provides automated and interactive data story reports describing complex relationships between critical process KPIs and hundreds to thousands process variables. Our stories were automating a standard data science project journey from assessing the quality of data in analytics based table to correlation analysis, variable selection, model development, model validation and what-if analysis & simulations, and autogenerated conclusions in an easy to understand narrative. The focus on operations has led us to further develop model operationalisation and model management, so that our clients can make the models work for them every minute of every hour.

What our clients are buying today are products with models operationalised, supported by DataStories SDK. We call them Virtual Experts. They are Augmented Advisors that monitor continuous process outcomes and advise how to improve them. We are deploying models from generated data stories into real-time Virtual Advisors, which alarm when context changes and recommend adjustments to process parameters to improve the outcomes (like product quality, production throughput, waste).

Our approach summarized:

  1. Automated data stories (including automated model generation and what-if exploration)
  1. Deploying stories for Automated Insights and optimisation recommendations (we reverse-engineer models in real time for any given context, and prescribe recommended changes to controllable parameters to improve the KPIs).

Want to learn more about our approach?

Schedule a demo and discover our cool augmented analytics products