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.
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.
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.
Collaboration
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.
Request
Tracking
Chat
Feedback
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.