top of page
Promethium Augmented Analytics

Data Analytics Use Cases

Address real-world use cases with powerful AI-driven augmented data management and analytics solution

Data Ops
Boost Speed and Accuracy 

The Problem With DataOps Today

Data pipelines are crucial for analytics, but can't keep up with the speed that the business needs to answer questions with data analysis and insights.

Icon Red Slow.png
Icon Red Complex.png
Icon Red Laborious.png

DataOps That Removes Bottlenecks

Efficient data pipelines remove the dependency on always moving and transforming data before analysis can begin. The performance improvements can be dramatic.

Pill Green Fast.png
Pill Green Simple.png
Pill Green Efficient.png
Try boosting 🚀 speed and accuracy for yourself
Data Disovery

Data Discovery
Data Catalog For Fast Analytics

The Problem With Data Discovery Today

Data Catalogs were born to solve for data governance and haven't evolved to make it fast and easy to find data for analytics. They still require data to be moved and to switch to other tools to serve up data for analysis.

Data Discovery That Speeds Up Analytics

Fast data discovery needs a data catalog that easily connects to all data sources, can catalog data automatically in minutes without moving data and makes finding the right data as fast and simple as a Google search.

NLP Question Data Match.png
Learn why a data catalog is not enough for fast 🚀 analytics
Self-Service Analytics

Data + Analytics + Business

The Problem With Data and Analytics Collaboration Today

Without a purpose built data analytics management solution requests get lost in email and collaboration can't happen in real time. Resulting in surprises, rework and dissatisfaction.

Collaboration That Drives Business Outcomes

When Data, Analytics and the Business can work together in real time trusted results are delivered faster and with less rework. Request tracking, chat, feedback and real-time data analytics development make collaboration easy.


Self-Service Analytics
Enable Everyone

The Problem With Self-Service Analytics Today

It's not really self service.  It only works for datasets that are already centralized and neatly modeled.  Plus it doesn't stop people wasting time creating analysis that already exists.

True Self-Service Analytics Enables Data Driven Business Outcomes

Self-Service Analytics is successful when data, knowledge, skill, complexity and effort barriers are eliminated so Business and function experts can use data analytics to answer questions when the answer is needed, instead of when technical resources are available.

Intuitive Search
Business Context
Don't let your business wait months for answers from data!
bottom of page