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Why Patterns?
How generative analytics is different
There are some things dashboards work great for, like tracking KPIs that don't change frequently, but for tasks that require you to explore and interrogate data, dashboards are quite limiting.
Patterns gives autonomy to everyone at your company to analyze data. It up-levels everyones capabilities turning traditional data Builders into Instructors and everyday Viewers into Analysts.
We do this by leveraging generative AI Analysts that model your data and generate analyses via natural language, allowing viewers to access and analyze data independently and unlocking higher leverage work for builders.
Business intelligence tools have long had the concept of a Builder, the individual on the data team responsible for loading data and building dashboards, and a Viewer, the stakeholder that uses the data.
A typical dashboard offers a view into the underlying data with filters to cut and sift through the data, adding a few more views. If you need anything else, you must make a request to the data team to add a new data source, chart, or filter. This is very limiting in practice when the data you need is dynamic, and when the questions you ask might always be changing.
In Patterns, instead of building a single view into your data, an Instructor would train an AI Analyst on how to query the data. Define what tables contain what data, how metrics are calculated, and clarify any business specific logic that might be useful when generating an analysis.
Instead of building every single dashboard view by hand, you simply tell your AI Analyst how your data is structured, unlocking every possible view of your data on demand.
As a result, everyone can now conduct analytics independently. All you need to do is make a request, your AI Analyst will confirm and clarify it's action plan, then execute the plan self-correcting for errors and checking edge-cases along the way. Truly self-serve analytics!
Last modified 8d ago