The right Holistics alternative depends on who asks the data questions in your team. If analysts write the queries, a code-first tool keeps them fast; if everyone else needs answers, look at tools built on a semantic layer or a warehouse spreadsheet; and if you just want numbers on a screen, a KPI dashboard tool is simpler.
Who in your business asks the data questions? That single answer narrows this list faster than any feature grid. Holistics describes itself as AI-assisted self-service BI for data teams, so the people leaving it usually want either more control for analysts or less setup for everyone else. Below are eight business intelligence tools for both, in no particular order, grouped by the job each one does best.
Self-service sounds simple until someone asks a new question
Self-service is the promise on almost every BI homepage, and it's where switching goes wrong. A tool can be self-service for an analyst who writes SQL and still be a closed door for a sales manager who just wants to know why last week dipped. Before you compare a single feature, decide whose questions the new tool has to answer without help.
The other half of the job is what happens after the first dashboard. Dashboards get shared, copied, forgotten and quietly go stale. So test two things in any replacement: how people ask questions of the data, and how dashboards are shared and kept up to date once the person who built them moves on. Every tool here handles those two jobs differently, and that's where you'll feel the difference six months in.
Three ways these BI tools work
Code first, for analysts
Evidence, Mode and Redash assume someone on the team is happy writing queries. You get control and transparency, because every chart traces back to code you can read and review. The trade-off is that non-technical colleagues still lean on the analyst for anything new.
A model in the middle, for wider teams
Cube, Sigma and ThoughtSpot put a governed layer between your warehouse and the people asking questions, then add chat, spreadsheets or AI agents on top. They suit teams that want lots of people asking questions without each one building their own version of the truth. Expect more setup at the start, because someone has to define the metrics first.
Screens and spreadsheets, for everyday numbers
Klipfolio and Geckoboard are about showing the numbers people check every day, clearly and without fuss. They're closer to a reporting screen than a full analysis tool, which is exactly what plenty of small teams need.
Eight BI tools side by side
| Tool | Kind of BI tool | Good for |
|---|---|---|
| Cube | Semantic layer and AI analytics | One shared set of metrics for BI, embedding and AI |
| Evidence | BI as code, open source | Reports built from SQL and markdown |
| Geckoboard | Live KPI dashboards | Team numbers on office screens, Slack and phones |
| Klipfolio | Dashboard software | Dashboards for people who think in spreadsheets |
| Mode | Collaborative analytics | Analysts working in SQL, R and Python together |
| Redash | Query and visualise | Querying and charting many databases in one place |
| Sigma | Warehouse-native BI | Spreadsheet, chat or code on warehouse data |
| ThoughtSpot | Agentic analytics | AI-led questions and analytics embedded in other apps |
The eight, A to Z
Listed alphabetically and in no particular order. Each line reflects what the vendor says about its own product.
Cube
Best for: Teams that want one semantic layer under their BI, embedded analytics and AI answers, which the vendor pitches as answers staff and customers can trust.
Evidence
Best for: Analysts who'd rather write SQL and markdown than drag charts around; the vendor describes an open source, code-based way to build data products.
Geckoboard
Best for: Putting live team KPIs on office TVs, Slack, Teams, email or mobile, pulled from tools such as HubSpot, Zendesk and Salesforce, per the vendor.
Klipfolio
Best for: Spreadsheet-minded teams; the vendor says its dashboards fetch data automatically, can be shared and presented in many ways, and feel familiar to spreadsheet users.
Mode
Best for: Analysts mixing SQL, R, Python and visual analysis in one shared workspace, which the vendor frames as a collaborative data platform.
Redash
Best for: Connecting databases such as PostgreSQL, MySQL, BigQuery or MongoDB, then querying, charting and sharing the results, according to the vendor.
Sigma
Best for: Teams building on governed warehouse data through chat, a spreadsheet, SQL or Python; the vendor says it covers dashboards, apps and agents.
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ThoughtSpot
Best for: Data leaders who want AI agents, automated insights and analytics embedded where people already work, which the vendor describes as governed AI.
My advice before you rebuild a single dashboard
Don't migrate everything. Most BI accounts carry dozens of dashboards nobody has opened in months, and moving them just carries the clutter across. List the handful your team checks every week, rebuild those first, and let the rest go unless someone asks for them by name.
Then match the group to the people. If one analyst serves the whole business, a code-first tool keeps their work clean and reviewable. If you want sales, marketing and operations answering their own questions, a semantic layer or warehouse spreadsheet is worth the extra setup. If what you need is a few live numbers on a screen, don't sign up for a full analysis platform to get them. For a wider look at swapping business software, see my guide to Bitrix24 alternatives, and if your dashboards end up on shared screens, my web accessibility guide covers making them readable for everyone.
A fortnight-long dashboard trial
- Pick one real question your team asked recently and the dashboard that answered it.
- Choose two tools from different groups above, so you're comparing two different approaches.
- Connect the same data source to both and rebuild that one dashboard.
- Ask a colleague who doesn't write SQL to answer a follow-up question on their own.
- Change one number at the source and check how quickly each dashboard shows it.
- Keep the tool your colleague could use without calling you for help.
Do you make business intelligence software? Tool makers can ask to be considered for a featured place on this list.
Want your tool on this list? Featured spots and reviews here are paid partnerships. Here's how to partner with me.
Related: writing for us on python.
Frequently asked questions
What is a semantic layer in BI?
It's a shared set of definitions that sits between your data warehouse and your dashboards, so a metric like revenue or active customers means the same thing in every report and every AI answer.
Do I need to know SQL to use a BI tool?
Not always. Code-first tools expect it, while spreadsheet-style, chat-based and KPI dashboard tools are built so people who don't write SQL can still get answers. Someone usually still needs to connect the data at the start.
Is open source BI a good option for a small business?
It can be, if someone on the team is comfortable with code and hosting. You keep full control and can read every query, but you also take on the upkeep.
How do I stop dashboards going stale?
Give every dashboard an owner, review the list every quarter and archive anything nobody has opened. A tool that shows when a dashboard was last viewed makes this much easier.
Can AI answer questions about my business data?
Several BI tools now offer chat or AI agents on top of your data. The answers are only as good as the definitions underneath, so set up your metrics carefully before anyone relies on them.
How long does it take to switch BI tools?
It depends on how many dashboards you move. Rebuilding the few your team uses every week is usually quick; moving every old report is what drags a switch out.
