23 posts tagged with "enhancement"

In complex data models, users can create metric breakdowns that are technically valid but analytically wrong.

These queries run successfully and return plausible numbers, but the results are meaningless. This silent failure undermines trust and hurts self-service.

Now you can control which dimensions can be used with each metric using the filter_direction property on relationships.

Why this matters​

• Prevents silent data correctness issues before they reach end users
• Turns semantic correctness into a default behavior, not a best practice
• Keeps self-service powerful and safe, even in multi-fact (galaxy) schemas
• Reduces the need for manual user education or tribal knowledge

📚 Learn more​

• Controlling which dimensions can be used with a metric: Detailed walkthrough with examples
• Controlling filter and grouping paths: Reference documentation for filter_direction

Canvas dashboard's explicit_interactions list now states every active connection directly, so what's written is what's active, nothing is assumed.

Also, you can name target blocks directly across FilterInteraction, PopInteraction, and DateDrillInteraction without CustomMapping wrapper needed.

Before vs after​

Comparing the old and new syntax highlights how the explicit interaction model simplifies your dashboard configuration by making all active connections visible at a glance.

• ❌ Before (Old Syntax)

  // Old syntax - only the disabled links are visible, not the active connections
  interactions: [
    FilterInteraction {
      from: 'f1'
      to: [
        CustomMapping {
          block: ['v3', 'v4']
          disabled: true
        }
      ]
    }
  ]
  

• ✅ After (New Syntax)

  // New syntax - active connections stated directly
  explicit_interactions: [
    FilterInteraction {
      from: 'f1'
      to: ['v1', 'v2']          // Bare block names, no CustomMapping wrapper
      field: r(model.field)    // Set once on the entry, not per target
    }
  ]
  

Migration to new syntax​

We don't support mass conversion yet, but the transition is seamless:

• New dashboards automatically use the explicit_interactions: syntax.
• Existing dashboards convert when you next update their control or cross-filter setup.
• Legacy interactions: syntax remains functional, without breaking changes.

Timeline​

We expect to release this around mid-September 2026.

Admins can now configure MCP access separately from in-app AI access, per user or group.

Previously, one combined toggle granted a user both AI access inside Holistics and AI access via MCP together.

Now, granting AI access to a user or group lets you choose the scope:

• In-app only: use AI inside Holistics.
• In-app & MCP: also connect an AI client like Claude directly to Holistics via MCP.

This helps you grant MCP access only to users you trust to use an external AI client responsibly.

Learn more: Who Can Use AI

Field Filters can now be built directly from a metric, the same way you'd build one from a dimension or measure.

What's changed​

Previously, Field Filters only supported dimensions and measures as the source field - even though metrics are a first-class concept in the semantic layer alongside them. Now you can filter on a metric too, for example only showing rows where a computed metric like Churn Rate crosses a certain threshold.

Learn more: Field Filters

Selecting a date range previously meant either navigating the calendar manually or knowing the exact phrase to type — there was no quick path for common choices.

The date picker now has a quick preset panel for one-click access to common ranges (Today, Yesterday, Last 7 days, Last 30 days, Month to date, Year to date). If you prefer typing, shorter shorthands now work too: ytd, mtd, and wtd. Existing long-form phrases continue to work — this is fully additive.

Learn more: Relative dates

Browse and insert reusable components directly in the Reporting dashboard editor - no need to switch to Development.

Previously, inserting library components required switching to Development, which added friction and blocked Explorers entirely. Now you can insert existing components right where you work - components built by one teammate are instantly available to everyone.

Learn more: Reusable components

Previously, KPI styling had to be set widget by widget and had limited styling options.

🌟 What's new​

We're bringing KPI metric styling to the dashboard theme and supporting much richer styling options.

• Style once, apply everywhere: Configure in the dashboard theme, and changes will be applied to every KPI across the dashboard. Learn how to style with dashboard theme.

• More styling options: Customize alignment, label and value text, progress bars (text, indicator, track), and trend indicators per state (positive, negative, neutral). Learn how to customize your KPI metric.

The Explore interface has a new look and feel as part of our ongoing glow-up.

New chart selector - The chart picker now surfaces the most common chart types with a clearer icon set that makes it easier to distinguish between chart types at a glance. Less common charts are tucked behind a "more" option to keep the interface clean.

Configure button - The viz settings panel now opens and closes via a Configure button in the toolbar area.

Toolbar - The toolbar is where you can perform most Explore actions: ask AI, break down your data, drill by time, and more - all without opening the viz panel.

Run button in the toolbar - The Run button has a new home in the top-right toolbar alongside Analyze and Ask AI. This is the first step toward a workflow where you can control charts entirely from the field list and toolbar, without needing to open the viz panel.

Learn more: Explore is getting a glow-up

Select all fields in a model is now available in the field picker in Holistics.

Previously, selecting every field in a model meant checking each one individually — tedious for large models and a common friction point when exploring a dataset for the first time. Now you can select or deselect all fields in a model with a single click.

A checkbox appears next to each model name in the field picker. Clicking it selects all fields in that model. Click again to deselect.

The same behavior extends to folders and sub-folders, so you can bulk-select all fields within those groups at once.

The checkbox is available for Table visualization only.

AI that Asks Back makes the Ask AI experience more interactive and conversational.

Instead of ending after a single response, Ask AI can now guide the conversation forward by suggesting relevant next prompts and asking clarifying questions when needed. This makes it easier to refine your query, explore follow-up questions, and get better answers faster.

What's new:

• Suggested next prompts help you continue the conversation with one click.
• Clarifying questions help Ask AI better understand your intent before answering.
• A smoother workflow means less typing and less copy-pasting.