Dashboards and reporting systems
Dashboards backed by clean data pipelines, clear metric definitions, and reporting workflows people can trust.
This is end-to-end reporting: the pipelines that move data into a warehouse, the models that turn it into metrics everyone agrees on, and the dashboards on top. The dashboard is the last five percent of the work and the only part anyone sees. It is for teams where someone still assembles a recurring report by hand, or where the existing dashboards are not trusted.
The problem
Dashboard projects usually fail underneath the dashboard. The chart is easy. Getting the number to be correct, refreshed, and the same number everyone else sees is the actual project.
- Someone spends Monday morning pulling CSVs and stitching them together in a spreadsheet.
- A BI tool was bought, connected directly to production, and now nobody trusts the numbers.
- Sales and finance report different revenue because each defined it separately in the tool.
- The dashboard silently shows stale data because an upstream load failed on Thursday.
- Only one person can change a chart, and they are busy.
What we build
Metric definitions first
What counts as revenue, an active customer, or a closed deal, agreed with the people who own those numbers and written down before anything is built.
Pipelines into a warehouse
Data moved out of source systems on a schedule, so reporting queries stop hitting the production database that runs your business.
A modeled layer
dbt models that turn raw vendor tables into the entities your business talks about, so every dashboard reads from the same definitions.
Freshness and quality tests
Checks that fail loudly when a load breaks, so a stale dashboard raises an alert instead of quietly misleading people.
Dashboards people will use
Built around the decisions the team actually makes, in whichever tool you already have, with each number traceable back to its model.
Scheduled delivery
Where the answer belongs in an inbox or a Slack channel rather than behind a login somebody has to remember to open.
What you get at handoff
- Pipelines and dbt models in your repository.
- A data dictionary in plain language for the tables people query.
- Dashboards in your own BI account, editable by your team.
- Alert routing you control, for freshness and test failures.
- A walkthrough covering how to add a metric without breaking the others.
Stack
Warehouses
Snowflake, BigQuery, Postgres, Redshift
Ingestion
Fivetran, Custom SDK connectors, Python jobs, APIs
Modeling
dbt, SQL, Incremental models
Orchestration
Airflow, Scheduled jobs
Dashboards
Looker Studio, Metabase, Power BI, Tableau, Hex
Related services
FAQ
Can you not just connect the BI tool to our database?
You can, and for a very small business it is sometimes the right call. It stops being right when reporting queries start slowing the database your business runs on, when you need history that the production system overwrites, or when metric definitions start living inside the BI tool where nobody can review them.
Which dashboard tool should we use?
Usually the one you already have. The tool matters far less than the modeled layer underneath it. If you have no tool yet, Metabase and Looker Studio both cover most small-business needs without a large licence commitment.
How do we stop two reports disagreeing?
By defining the metric once, in the modeled layer, and having every dashboard read from it. Disagreements almost always come from each analyst writing their own joins. That is a modeling problem, not a dashboard problem.
How current does the data need to be?
Less current than people assume, and asking is worth doing. Most operational reporting is fine on a daily refresh, which is dramatically cheaper than streaming. We ask what decision the number drives, then set the schedule to match.
What happens when a pipeline breaks?
A freshness test fails and someone gets alerted, which is the point. A dashboard quietly serving last Thursday numbers is worse than one that says the data is stale.
Ready to scope Dashboards?
Send the workflow, tool stack, or reporting problem. We will tell you what should be automated, what should stay manual, and what is worth building first.