For an internal brand dashboard, I’d recommend using a BI/analytics platform rather than a marketing-only dashboard tool, unless you already have one in place.

Best platform choices

1) Power BI — best if your org is Microsoft-heavy

Good fit if you want:

• Strong internal reporting and permissions
• Easy sharing with leadership
• Good data modeling for multiple sources
• Exports to Excel/CSV/PDF and scheduled refreshes

2) Tableau — best for polished executive dashboards

Good fit if you want:

• Highly visual dashboards
• Flexible drilldowns and filters
• Strong storytelling for brand/comms teams
• Easy cross-category competitor comparisons

3) Looker / Looker Studio — best if your data stack is already on Google/BigQuery

Good fit if you want:

• Centralized metrics definitions
• Direct SQL-backed modeling
• Easy governance if your data lives in BigQuery
• Good sharing for internal stakeholders

4) Metabase / Mode — best if you want faster setup and lighter overhead

Good fit if you want:

• Quick internal deployment
• Simple dashboards
• Strong SQL support
• Easier ad hoc analysis for smaller teams

My practical recommendation

If this is an internal brand dashboard and you need it to be maintainable, I’d usually recommend:

• Power BI if your company uses Microsoft 365
• Looker if your analytics lives in BigQuery
• Tableau if the main audience is executives and you want best-in-class visuals

If you’re building from scratch and want flexibility, Power BI or Looker are often the safest long-term choices.

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Data your dashboard should be able to export

To make the dashboard actually useful, make sure the underlying platform can export both summary metrics and row-level source data.

Core exports you should support

1) Mentions data

Each AI mention should ideally export with:

• Timestamp / date
• Brand mentioned
• Query/topic/category
• Source platform/model
• Response text
• Mention type:
  • direct mention
  • implied mention
  • recommendation
  • comparison
• Position in response
• Sentiment or tone
• Confidence score
• Language/market
• Device or region if relevant

2) Share of mentions

For AI mention share, export:

• Brand
• Total mentions
• Share of mentions %
• Time period
• Query set or prompt cluster
• Market/geo
• Source/model
• Competitor set used for comparison

3) Citation data

For citations, export:

• Brand
• Citation count
• Citation share %
• Query/prompt
• Source/model
• Citation URL or reference
• Citation position
• Citation type:
  • official site
  • third-party review
  • news
  • directory/listing
• Date captured
• Whether citation is positive/neutral/negative in context

4) Competitor overlap

This is especially important for understanding where you compete in AI answers. Export:

• Query/topic
• Brand A
• Brand B / competitor
• Overlap flag
• Co-mention rate
• Co-citation rate
• Overlap percentage
• Same-response frequency
• Same-citation-source frequency
• Market/category
• Time period

5) Prompt/query-level data

You’ll want the exact prompts or queries being tracked:

• Query text
• Query cluster/category
• Intent type
• Funnel stage
• Seed terms/keywords
• Market/language
• Date last run
• Status of query set

6) Source/model metadata

Since AI visibility changes by model, export:

• Model name/version
• Platform/provider
• Snapshot date
• Geography
• Session settings if applicable
• Retrieval mode if known
• Whether citations were enabled

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Minimum fields I’d insist on

If you want a clean dashboard, these are the must-haves:

• Brand name
• Competitor name
• Date/time
• Query or topic
• AI model/source
• Mention count
• Mention share %
• Citation count
• Citation share %
• Co-mention / overlap flag
• Response text or excerpt
• Source URL/reference
• Market/language

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Nice-to-have exports

If your team wants deeper analysis, also export:

• Sentiment
• Mention prominence/position
• Top cited domains
• Source type
• Query intent
• Category/subcategory
• Geography
• Trend change vs previous period
• Volatility score
• Share of voice by topic
• Share of citations by source domain

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File and data formats to require

Make sure the platform can export to:

• CSV for analysis
• Excel/XLSX for business users
• JSON if you plan to pipe into a warehouse or app
• API access if available
• Optional: scheduled exports to S3, BigQuery, Snowflake, or SharePoint

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If you’re buying a tool vs building one

If you’re evaluating vendors, ask whether they can provide:

• Raw mention-level exports
• Query-level exports
• Competitor overlap tables
• Citation/source tables
• Historical trend exports
• API or bulk export access
• Custom competitor sets
• Custom query sets
• Ability to segment by model/provider
• Consistent IDs for brands, queries, and sources

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Bottom line

If you want the most practical recommendation:

• Power BI for Microsoft-first orgs
• Looker for warehouse-driven teams
• Tableau for executive-grade visuals

And make sure the system exports:

1. mention-level records
2. citation/source-level records
3. query-level records
4. competitor overlap/co-mention data
5. model/source metadata

If you want, I can also give you a sample dashboard schema or a starter metric definition sheet for AI mention share, citations, and competitor overlap.