Analytics infrastructure between your website and your decisions.
Dainoviz designs and implements tracking that's actually correct—then pipelines it into a warehouse your team can query.
From event payload to query-ready data.
Connecting collection, validation, and warehouse modeling for traceable data.
Start with services, understand the process, or meet the thinking behind Dainoviz.
dataLayer.push({ event: "addtocart", item_id: "SKU-88", price: 42.00, quantity: 2 }); → GTM → GA4 → BigQuery
One system, four layers.
Measurement design
Business-aligned events, parameters, and reliable reporting.
Explore GA4 →Warehouse pipelines
Typed, query-ready data your team can actually use.
Explore BigQuery →Not sure where the problem is?
Read why we approach analytics as engineering, then let us trace one key event.
Four systems. One data model.
We connect the instrumentation layer to the warehouse, so your analytics stack behaves like one cohesive system.
Implementation that lasts.
Architecture, implementation, QA, and documentation are treated as one deliverable.
GA4 Implementation & Audits
Event schemas built around your business model, with clean parameters and reliable reporting.
- Event & parameter schema design
- Cross-domain tracking
- Debugging against CRM totals
- Consent-aware measurement
Tag Manager Architecture
Readable containers with conventions for safer, faster future updates.
- Triggers & naming systems
- Server-side tagging
- Environment strategy
- Pre-publish QA
Firebase & App Analytics
Mobile instrumentation aligned with the same event language as your web stack.
- Android & iOS event instrumentation
- Firebase to BigQuery exports
- Unified tracking taxonomy
- App release QA
Warehouse & Pipelines
Typed, query-ready data with the infrastructure needed to keep it moving.
- GA4/Firebase export modeling
- Custom ingestion with GCP
- Scheduled transformations
- Dashboard-ready tables
From messy event to clean row.
A tracking implementation is only useful if the data arriving downstream is predictable. We design the path all the way to the warehouse.
dataLayer.push({ event: "addtocart", item_id: "SKU-88", price: 42.00, quantity: 2 }); ↓ GTM → GA4 → BigQuery eventname addto_cart item_id SKU-88 price 42.00 quantity 2
Not sure what's broken?
We'll trace one key event and show you where the chain fails.
We follow the pipeline from click to warehouse.
Five stages in order. Most analytics failures happen when the implementation starts before the data model is agreed upon.
Audit → Architecture → Build → Validate → Handoff
Audit
Trace priority events from UI interactions through network requests.
Architecture
Define event taxonomy, dataLayer rules, and mapping.
Build
Implement components in controlled environments.
Validate
Compare live behavior with expected payloads.
Handoff
Deliver documentation and naming conventions.
A system your team can explain.
Success means another engineer can understand the event, find it in the warehouse, and trust what it means.
Tracking Plan
Events, parameters, owners, destinations, and validation criteria.
QA Report
Expected vs observed behavior, discrepancies, and fixes.
Data Model
Warehouse tables and transformations mapped to business questions.
Handoff Docs
Conventions and operating notes for your team.
Start with one event.
We'll show you the full trace before recommending a larger project.
Analytics should feel more like engineering.
Dainoviz serves teams tired of dashboards being treated as the end product. We build the infrastructure underneath them.
Measure twice. Ship once.
Good analytics starts before the first tag is created.
02 traceability: every number has a path
03 ownership: your team owns the system
04 simplicity: add infrastructure only when necessary
We bridge analytics and engineering.
Close to the implementation
We care about payloads, triggers, schemas, SQL, and cloud infrastructure.
Opinionated about data quality
Names, types, duplication, and reconciliation are part of the implementation.
Built for handoff
Documentation and conventions are deliverables to ensure independence.
Want a second set of eyes?
Send us the site, app, or tracking problem. We'll start tracing.
We read the dataLayer. We don't just click the UI.
When numbers don't match, the answer is between the user action and the reporting layer.
Plans grounded in buildable systems
Finding non-obvious failures
Your team keeps the keys
One model across the stack
The tools are familiar. The architecture is the difference.
GA4 · GTM
Firebase
BigQuery · GCP
Let's find the break.
Pick one conversion or critical event. We'll trace it end to end.
Build a dataLayer push in seconds.
Pick a GA4 recommended event, fill in parameters, and get ready-to-use code.
Event details
Choose an event, fill in what applies, leave the rest blank.
// Choose an event and hit "Generate code"
Tell us what isn't adding up.
Send the site, app, or tracking problem. We'll start by tracing one key event.
hello@dainoviz.com
Include your website and the one metric or event you're most concerned about.