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Most best practices guides for self-service analytics are written for IT teams rolling out Tableau to their finance department. This isn't that.
This is for product teams building analytics into their SaaS product — especially two-sided marketplaces where your supply-side users (sellers, landlords, hosts, drivers) need to understand their own data. These users constantly ask your CS team for reports or are frustrated by the limited static dashboards your team have shipped. Self-service analytics solves this. But shipping it well is harder than it looks.
Here's the list of best practices we've learned building analytics products — and what we see teams get wrong.
TL;DR
- Self-service pays for itself when it replaces human labor, not when it adds flexibility.
- Static dashboards force teams to guess what users want — and they usually guess wrong.
- Design for drag-and-drop exploration, not SQL authoring.
- Instrument what users build, not just what they view.
- Recurring support questions are unmet analytics needs.
1. Pick a user segment that's drowning your CS team
Don't start with "let's add self-service analytics." Start with a problem.
For example, if you run a two-sided marketplace, your supply-side users are probably asking for reports constantly. Landlords want to see occupancy trends. Sellers want revenue breakdowns.
Your CS team is fielding these requests manually — exporting CSVs, building one-off spreadsheets, scheduling recurring reports. That's expensive, slow, and doesn't scale.
Self-service analytics pays for itself when it replaces this labor. The ROI is concrete: fewer support tickets, faster answers, happier users who feel in control of their data.
If your CS team is drowning in report requests, self-service could be your highest-leverage feature right now.
2. Stop guessing which metrics matter
Before building Embeddable, I co-founded Trevor.io, a BI tool. Early on, we built static dashboards for our users to show them how their team was using the platform — essentially audit logs visualized.
It was painful. We had to guess which data points were most important. Which time ranges? Which breakdowns? The dashboards were "one size fits all" by nature. Some users wanted weekly views; others wanted daily. Some cared about active users; others cared about query volume.
We got it wrong more often than we got it right.
That's where self-service shines. Instead of predicting what users want, you let each user surface what's relevant to their business. The landlord who cares about maintenance costs builds a different view than the one focused on vacancy rates — using the same underlying data.
Static dashboards force you to be clairvoyant. Self-service lets you be useful.
3. Pre-build starting points, not finished dashboards
Self-service doesn't mean dropping users into a blank canvas. That's overwhelming for most users and leads to empty states that kill adoption.
Give users a sensible default they can modify. "Your sales this month" is a starting point. From there, they can add filters, change date ranges, drill down into specific products, or pivot to a different view entirely.
Think of it like a document template. Nobody wants to stare at a blank page, but everybody wants to customize the result. Your job is to provide the template that gets them 70% of the way there.
The best self-serve embedded analytics tools make this easy: pre-built components that users can rearrange, filter, and extend without starting from scratch.
4. Design for drag-and-drop, not SQL
Self-service analytics should optimize for non-technical users, not power users.
Here's an unpopular opinion: don't build a SQL interface into your self-service analytics.
Power users who want to write custom queries already have a preferred tool. They use Mode, or Tableau, or they're comfortable in a notebook. They don't want to learn your query syntax.
For these users, the best thing you can do is make export seamless. CSV download. API access. Scheduled data dumps. Meet them where they already work.
Self-service analytics is for the other 90% of users — the ones who want answers without learning a query language. Drag-and-drop interfaces, natural filters, click-to-drill-down. If your user needs to understand SQL to get value, you've built for the wrong audience.
This is a deliberate tradeoff. You're optimizing for breadth of adoption over depth of capability.
5. Treat data freshness as a feature
Stale data destroys trust faster than missing data.
Users won't trust dashboards that feel stale.
If a seller made a sale an hour ago and your dashboard shows yesterday's numbers, they'll question everything. "Is this data even right?" becomes the default assumption. Trust erodes fast.
Near-real-time data (within minutes) should be your baseline for transactional metrics. Users don't need sub-second updates for everything, but they do need to see recent activity reflected.
This has infrastructure implications. You'll need to think about caching strategies, pre-aggregations, and query optimization. But the investment is worth it — data freshness is table stakes for self-service credibility.
6. Build guardrails that prevent nonsense, not exploration
Security matters more than anything. But over-locking your analytics UI kills the "self-service" part entirely.
The right approach: guardrails that prevent bad outcomes without restricting exploration.
Guardrails to enforce:
- Row-level security so users only see their own data
- Sensible aggregation defaults (don't let users accidentally query unbounded date ranges)
- Undo functionality so mistakes are recoverable
Controls that kill adoption:
- Approval workflows for building new views
- Restricted chart types "for consistency"
- Locked-down filters that prevent custom date ranges
The goal is to make it hard for users to see data they shouldn't, while making it easy for them to explore data they should. That's the balance.
For more on the technical side, see our guide on embedded analytics for multi-tenant architectures and how Embeddable ensures security for self-serve dashboards given a multi-tenant set up.
7. Instrument what users actually build
You're building self-service analytics. You should also have self-service analytics about your self-service analytics.
Track everything:
- Which charts get created most often
- Which filters get applied
- Which date ranges are popular
- Which exports happen (and to where)
- Which dashboards get shared vs. kept private
- Time spent on different views
This data is gold. Your users will teach you what matters — but only if you're listening.
We've seen teams discover that 80% of users only ever use 3 of their 15 pre-built charts. That's a signal: either the other 12 charts are unnecessary, or they're undiscoverable, or they're solving problems users don't have.
Instrument from day one. Retrofitting analytics tracking is painful.
If you can't answer "what do users actually build?" without asking an engineer, your self-service analytics isn't self-service yet.
8. Make export a first-class feature
Self-service doesn't mean "trapped in your UI."
Some users will always want to pull data into Excel for further analysis. Some will want scheduled reports emailed to stakeholders. Some will want API access for custom integrations.
These aren't edge cases to handle grudgingly. They're core workflows for power users.
Build export as a first-class feature:
- CSV/Excel download from any view
- Scheduled reports (daily/weekly/monthly) to email
- PDF export for sharing with non-users
- API access for programmatic retrieval
The more ways users can get data out, the more valuable your analytics become. You're not competing with their spreadsheet — you're feeding it.
9. Use support tickets as your roadmap
Every "how do I see X?" support ticket is a UX bug.
Every "this number looks wrong" ticket is a trust issue.
Every "can you pull this report for me?" ticket is a feature gap.
These aren't distractions from your product roadmap. They are your product roadmap — at least for the analytics feature set.
Create a tag in your support system for analytics-related requests. Review them monthly. Look for patterns. The requests that come up repeatedly are your highest-signal feature candidates.
This feedback loop is more valuable than user interviews or feature voting. It's what people actually need, in the moment they need it, expressed in their own words.
The payoff
Self-service analytics done well has compounding benefits.
Your CS team gets time back. Your users feel empowered. Usage data tells you what matters. Support tickets become feature specs. And the marketplace where users can answer their own questions is stickier than the one where they have to wait for help.
It's not easy to ship. The practices above come from building analytics products and seeing what works (and what doesn't) across hundreds of implementations.
To recap the 9 best practices:
- Pick a user segment that's drowning your CS team
- Stop guessing which metrics matter
- Pre-build starting points, not finished dashboards
- Design for drag-and-drop, not SQL
- Treat data freshness as a feature
- Build guardrails that prevent nonsense, not exploration
- Instrument what users actually build
- Make export a first-class feature
- Use support tickets as your roadmap
If you're exploring self-service for your product, see our deeper guides on what self-service analytics is and the best tools for embedded self-serve BI.
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