Business Intelligence Services on Google Cloud
Business Intelligence that people actually use
Most organisations have considerably more dashboards than they have decisions being made from them. We build the pipelines, the metric definitions and the reporting that people open on a Monday morning, because it tells them something they can act on.
Talk to an expertSee what we deliver
- 20+ years on Google Cloud
- Google Cloud Premier Partner
- 50+ certifications
- ISO 27001:2022
Why dashboards go unopened
Almost nobody has a reporting shortage. What tends to be missing is the confidence that two people looking at the same number are looking at the same thing, and that is what quietly stops a dashboard being used.
The systems speak different languages
Marketing talks about opportunities, IT talks about tickets, and finance uses its own definitions again. When those systems are brought together in BI, the same business question can produce different answers simply because the underlying data was never defined in the same way.
Reporting is somebody's second job
A recurring report is still assembled manually, often under time pressure at month end, by the same analyst who was hired to spend time on analysis rather than exports and formatting.
It answers a question nobody asked
The dashboard was built around a priority from months ago, but the business has moved on. It still runs and still gets maintained, even though hardly anyone uses it anymore.
From reporting to decisions
Better charts seldom move this on their own, but a single place where each metric is defined usually does, particularly when access is quick enough that people browse instead of requesting and governed enough that the answer holds up when it is questioned.
Before
- The same metric defined differently in each report
- Days a month spent assembling the same export by hand
- Dashboards slow enough that people stop opening them
- Access handled by emailing a file to whoever asked
- Answers arriving after the moment they were needed
After
- One semantic layer where each metric is defined once
- Recurring reporting automated, so analysts do analysis
- Dashboards fast enough that browsing them feels worth it
- Role-based access applied at the semantic layer, not per file
- Operational dashboards that update as events happen
Built on Google Cloud for trusted, scalable BI
A strong BI setup starts with consistent definitions. Metrics should be defined once, governed centrally, and used across dashboards and reports, so teams are working from the same logic rather than creating their own version of the truth.
BigQuery provides the scalable data foundation underneath, keeping reporting fast and accessible even as data volumes grow. That makes it easier for people to explore information when they need it, instead of having to request another report.
20+
Years building on Google Cloud, since before most of it had a name
50+
Google Cloud certifications across the team
Premier
Google Cloud Premier Partner, with specialisations in data and infrastructure
What we put in place
Four layers, and they are worth doing roughly in this order. Building dashboards on top of undefined metrics is the most common way a reporting project produces something nobody believes.
The data underneath
Reliable dashboards cannot rest on fragmented data, so this comes first: stable pipelines and a warehouse structure that will still make sense when the third source is added.
Data integration
A unified dataset drawn from the systems the business actually runs on, rather than the ones that were easiest to connect.
Warehouse architecture
BigQuery structured so that a new source or a new question does not require rebuilding what is already there.
Pipelines & scheduling
Loads that run on a schedule, fail loudly when they fail, and can be re-run without a manual repair.
Data quality
Checks on the numbers before they reach a dashboard, because a wrong figure costs more trust than a missing one.
Semantic layer & governance
Where a metric gets its definition, its owner and its access rules. This is the layer that decides whether two people in a meeting are arguing about the business or about the report.
Metric definitions in one place
A structured set of metrics defined in code and governed centrally, so the number means the same thing wherever it appears.
Read the case study →
Ownership & quality rules
Clear ownership of who manages each dataset and how its quality is checked, written down rather than assumed.
Role-based access
Access applied at the semantic layer, so one report can serve several clinics, brands or partners without leaking between them.
Dashboards & self-service
Making the result something people can use without asking anyone: browse, filter, subscribe, and be told when something moves rather than having to go and look.
Operational dashboards
Reporting that updates as events happen, which matters most in logistics and eCommerce where minutes carry cost.
Embedded & partner reporting
Interactive dashboards delivered to customers and partners in place of static PDFs assembled by hand.
Read the case study →
Self-service enablement
Getting people to the point where they can answer their own question, so insight stops being a queue.
Alerting
Thresholds that notify the person who can act, rather than a daily digest everyone filters into a folder.
Performance & analytics
Once the numbers are trusted, the interesting work starts: tracking what matters, explaining what moved, and getting ahead of what is likely next.
KPI & performance management
Tracking and optimising against KPIs, with enough clarity about ownership that accountability follows the number.
Diagnostic analysis
Not only what happened but why, which is usually the question the dashboard prompts and rarely the one it answers.
Predictive & prescriptive
What is likely to happen next and what to do about it, where the data supports going that far.
See the service →
What changed for two clients
One in health technology, one in publishing. Different problems, the same underlying fix.
EW2Health · health technology
Health data healthcare workers can act on
EW2Health needed real-time health data that clinicians could read quickly, with secure access separated across multiple clinics. We built pipelines from Amazon Aurora into BigQuery, a LookML semantic layer of roughly 20 to 30 structured metrics, and dashboards with alerting on top. Persistent derived tables took load times down to a tenth of what they were.
Read the case study →
Sijthoff Media · publishing
Partner reporting that builds itself
Sijthoff Media reaches 3.5 million professionals a month, and was spending up to two working days a month assembling individual partner reports by hand. A three-week proof of concept moved GA4 data into BigQuery and into Looker's embedded platform; partners now open interactive dashboards instead of receiving static PDFs.
Read the case study →
Before you commission another dashboard
We already have dashboards. Why would we start again?
Usually you would not. The more common route is to leave the dashboards where they are and put a semantic layer underneath them, so the definitions stop drifting. Rebuilding the front end is the visible part of the work and rarely the part that fixes the problem.
Who ends up maintaining this?
Your team, in your own environment, with the metric definitions in a repository they control. Plenty of clients keep us involved for new work, though the intent is that ordinary changes do not need us.
Can we give partners or customers their own reporting?
Yes, and it tends to be one of the higher-return pieces of work because it removes recurring manual effort on your side while giving the other party something better than they had. Access separation is handled at the semantic layer rather than by maintaining parallel reports.
Who is allowed to see which numbers?
Access is decided at the semantic layer, alongside ownership and the quality checks, rather than by maintaining a separate report per audience. We are ISO 27001:2022 certified, and for regulated clients we work to whatever additional framework applies.
Next step
Want reporting people open on a Monday?
If your dashboards exist but are not being used, or the same metric keeps coming out differently in different places, we are happy to look at where that is coming from.