What can GoodData do? We grouped its capabilities; each group is based on the vendor’s current documentation.
GoodData runs queries where your data lives, so you do not have to move it into another product.
The logical data model and the metrics are the shared source for dashboards, reports and AI answers.
type: metric
id: downtime_hours
title: Downtime (hours)
maql: SELECT SUM({fact/downtime_minutes}) / 60
format: "#,##0.0"Business users can filter, drill down and build their own views without writing SQL.
Define models, metrics and dashboards as code and bring software discipline to analytics.
Bring analytics into the application your users already work in, or use the semantic layer from any tool through the API.
One model, many customers or plants: a workspace hierarchy passes the definitions from a parent workspace down to child workspaces.
AI experiences run on the same governed metrics and context, and you can trace which data and rules shaped an answer.
Cloud operated by the vendor, or the same codebase on your own infrastructure.
Intelligence Hub delivers conditioned, modeled OT data to Snowflake or Databricks, and GoodData builds metrics such as OEE and downtime from it.
With a workspace hierarchy, one plant model serves the same dashboards for every plant with plant-specific data filters.
Equipment makers and service providers offer embedded analytics that shows each customer only their own data.
A semantic layer defines the business meaning of data (entities, relationships and metrics) in one place, so every dashboard and AI answer is fed from the same definition and numbers stay consistent. See our article on the semantic layer for details.
No. GoodData connects to where the data already lives and runs queries there. For plant data the typical path is to land conditioned data from HighByte in a warehouse or lake and connect GoodData to it.
Both are possible. GoodData Cloud is SaaS operated by the vendor; self-hosting runs the same codebase on your infrastructure with Helm, Docker and Kubernetes. Data residency and regulatory needs decide.
Yes. In a workspace hierarchy child workspaces inherit the parent’s model and data sources, and workspace data filters limit the data each one sees. Users can add their own insights without affecting others.
Yes. Analytics as code lets you define models, metrics and dashboards as JSON or YAML and version them in Git, applied through the CLI or API.
You can bring your own LLM and use context management to decide which data, knowledge, tools and policies each AI experience may use. The sources and rules behind an answer are traceable.
Tell us where you are and what you want to achieve, and we will outline the right scope and pilot plan. Our team replies within 24 hours.