Automation Intelligence: The End of Passive BI and the Start of Real-Time Insight Orchestration

For years, the business intelligence industry has focused on building better dashboards. Cleaner UIs, faster queries, more visual options. But for most users, the workflow hasn’t changed: log in, look around, try to figure out what matters — and then do something about it.

That model doesn’t work anymore.

In modern organizations, information isn’t the problem. Distribution is. Timing is. Relevance is. Insight that arrives late, out of context, or buried inside a portal is insight that won’t get used.

It’s time for analytics to stop waiting to be discovered and start participating.

We call this shift automation intelligence: a native orchestration layer inside the analytics platform that connects metrics to action, and delivers insight when and where it matters most.

Most BI Tools Automate Delivery — Not Decisions

Plenty of platforms offer automation features: scheduled exports, basic alerts, subscription reports. These solve for convenience. But they don’t solve for action.

True automation intelligence goes further. It brings together real triggers, curated logic, and multi-channel delivery, so insight is generated and distributed at the right moment, to the right person, with the right context.

Here’s what that distinction looks like:

Automation isn’t about sending more emails. It’s about reducing the time from signal to action — and doing so at scale.

What Automation Intelligence Requires

To make this real, automation needs to be built directly into the analytics stack. At GoodData, we’ve structured it around three core functions:

1. Trigger

Automations begin when something changes, not when someone checks.

• Metric thresholds
• Period-over-period comparisons
• Data refreshes
• External system events via API or webhook

Every trigger is scoped by user permissions, workspace context, and filter logic, so outputs are always relevant and secure.

2. Execution

Once triggered, business logic runs to define what should be delivered.

• Metric evaluations
• Comparisons or anomaly checks
• Filter and segment application
• Output formatting and preparation

This replaces the need for users to interpret; the insight is pre-evaluated and ready to act on, using metrics defined in the semantic layer to ensure consistent, meaningful context across the organization.

3. Delivery

Outputs go where work happens.

• Slack, Teams, email
• Embedded dashboards
• Cloud storage (e.g., S3, GCS)
• Webhooks or downstream tools
• Internal notification centers

Delivery respects roles, filters, and frequency controls — reducing noise and surfacing only what’s important.

Built to Scale, Built to Govern

GoodData’s automation framework is already in production across embedded analytics platforms, customer-facing products, and enterprise reporting environments.

With GoodData, we’ve transformed our embedded analytics experience for our customers, giving them tailored, actionable insights into sales performance and customer engagement. Automation features like scheduled exports help ensure our users get the information they need, when they need it, which is a big upgrade to our analytics suite. — Outfield

Capabilities Available Today

• Scheduled and event-driven exports (PDF, XLSX, PNG, CSV)
• Metric-based and comparative alerts
• Alert-per-attribute (e.g., by region, product, account)
• Delivery via email, webhooks (for Slack, Jira, Salesforce, and more), S3, and embedded dashboards
• Full filter/context awareness via UDF/WDF
• Workspace-based isolation and permissions

Coming Soon

• Threshold suggestions based on metric history
• Narrative summaries for alert conditions
• Forecast-based early warnings
• Anomaly detection as trigger input

Because automation is native to the platform, it’s tightly governed, fully programmable, and designed for multi-tenant environments.

Moving Past the Dashboard Era

Dashboards play a key role in data workflows, but they assume the user knows when and where to look. Automation intelligence flips that model: the system takes responsibility for detecting, evaluating, and delivering what matters.

This isn’t about AI for AI’s sake. It’s about reliability, timing, and distribution — the real bottlenecks in how data is used today.

If you're embedding analytics into products, supporting internal teams, or scaling data delivery across business units, automation intelligence isn’t a nice-to-have. It’s the difference between being informed and being able to act.

Insight should not depend on someone logging in. It should move on its own.

That’s the shift. And that’s what we’ve built into the core of GoodData. If you’re ready to operationalize analytics and deliver value the moment it matters, schedule a demo and talk to our team.