GoodData Analytics

The analytics platform where metrics are defined once and reused by dashboards, embedded apps and AI assistants.

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metrics/downtime_hours.yamlDefinition
type: metric
id: downtime_hours
maql: SELECT SUM({fact/downtime_minutes}) / 60

The same definition, three surfaces

Dashboard
Embedded in your app
AI assistant

Illustrative example

Category
Headless BI, embedded analytics, AI
Deployment
GoodData Cloud or self-hosted (Kubernetes)
Data sources
Snowflake, Databricks, BigQuery, Redshift, PostgreSQL and more
Developer tools
React SDK, Python SDK, REST API, Git

Every team has its own “downtime” number

GoodData connects to the warehouse or database where your data already lives and adds the layer business tools often lack: a governed semantic model. Dashboards, analytics embedded in your own product and AI assistants all read the same definitions, so a number means the same thing wherever it appears. Models, metrics and dashboards can also be defined as code and kept in Git.

When downtime, OEE or scrap rate is calculated by a separate query in every dashboard, the numbers drift apart and trust erodes. The meeting ends up arguing about which number is right rather than about what the data says.

A semantic layer defines entities and metrics once. Every chart, report and AI answer is generated from those definitions; a change is made in one place and shows up everywhere.

Without a semantic layer

  • Every dashboard carries its own SQL and filters
  • The same metric returns different results in different places
  • An AI assistant guesses table and column names
  • Dashboards are copied by hand for every customer or plant

With GoodData

  • Metrics are defined once on the logical data model
  • All dashboards and reports use the same definition
  • Assistants answer through governed metrics and context
  • A workspace hierarchy passes the model down to child workspaces

How it works

From raw source to ready-to-use data in five steps.

  1. Connect

    Connect to where your data lives: Snowflake, Databricks, BigQuery, Redshift, PostgreSQL and others. For custom sources you can write your own connection with FlexConnect.

  2. Model

    On the logical data model, turn tables into datasets and columns into attributes and facts, and define the relationships. Joins are resolved from this model.

  3. Define metrics

    Define business metrics such as downtime, yield or unit cost once. Metrics are governed, reusable and return the same result for every consumer.

  4. Build and share

    Build insights and dashboards with filters and drill-down. If you prefer, write everything as YAML or JSON and version it in Git.

  5. Embed and ask

    Embed analytics into your application with the React SDK and APIs, give each tenant its own view through a workspace hierarchy, and let AI assistants answer through the same metrics.

Data sources

  • Data warehouse
  • Data lake
  • Databases
  • Custom (FlexConnect)

GoodData

  • Logical data model
  • Metrics (semantic layer)
  • Dashboards and analytics as code

Consumers

  • Your application (SDK)
  • AI assistant
  • APIs and BI tools
  • Tenant workspaces

What it offers

The capability groups; the details are on the features page.

  • Connect to the data you already have

    GoodData runs queries where your data lives, so you do not have to move it into another product.

  • Semantic layer

    The logical data model and the metrics are the shared source for dashboards, reports and AI answers.

  • Interactive dashboards and exploration

    Business users can filter, drill down and build their own views without writing SQL.

  • Analytics as code

    Define models, metrics and dashboards as code and bring software discipline to analytics.

  • Embedded analytics and headless BI

    Bring analytics into the application your users already work in, or use the semantic layer from any tool through the API.

  • Multi-tenancy

    One model, many customers or plants: a workspace hierarchy passes the definitions from a parent workspace down to child workspaces.

  • AI and agents

    AI experiences run on the same governed metrics and context, and you can trace which data and rules shaped an answer.

  • Security and deployment

    Cloud operated by the vendor, or the same codebase on your own infrastructure.

All features

Together with plant data

Feed it with HighByte

Intelligence Hub delivers conditioned, modeled OT data to Snowflake or Databricks, and GoodData builds metrics such as OEE and downtime from it.

Plant and line views

With a workspace hierarchy, one plant model serves the same dashboards for every plant with plant-specific data filters.

Offer analytics to your customers

Equipment makers and service providers offer embedded analytics that shows each customer only their own data.

Where it helps

Typical situations; all of them are on the use cases page.

Plant-to-boardroom KPIs

OEE, downtime and scrap are calculated differently by production, maintenance and finance.

Data in
MES and historian data (in the warehouse)
Outcome
The meeting discusses what to do, not which number is right.

Multi-site comparison

Several plants make the same product, but each one’s report is built separately.

Data in
Per-plant data
Outcome
Adding a plant is as simple as adding a child workspace.

Analytics embedded in your product

An equipment maker wants to show customers machine performance inside its own portal.

Data in
Machine data (in the cloud)
Outcome
Customers see their own data without leaving the application.

All use cases

How ASP Dijital delivers

We do not just resell the product: we deploy it and hand it over to your team.

  1. Discover

    In a workshop we clarify the current state, sources, goals and constraints, and choose the first use case together.

  2. Pilot

    We set up a GoodData pilot with a limited scope and written success criteria, and judge the result against them.

  3. Roll out

    We standardise the models, policies and templates from the pilot, integrate with surrounding systems and run a security review.

  4. Enable and support

    We train your team, hand over documentation and support you after go-live.

Frequently asked questions

What is a semantic layer and why do I need one?

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.

Do I have to move my data into GoodData?

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.

GoodData Cloud or my own infrastructure?

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.

Does it support many customers or many plants?

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.

Can I keep analytics in Git?

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.

All questions

Let’s take the first step with GoodData together

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.

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