Data Analytics
Analytics that answers business questions, not just dashboard questions. We build the pipelines, models, and reports that turn what you collect into decisions you can defend.
Data Analytics built for business.
Analytics that answers business questions, not dashboard questions. We start with the decision, then build the pipeline, the model, and the report around it.
We start with the decision, not the dataset. What does the team need to know before Monday, and what will they do with the answer? Everything downstream, the sources we integrate, the models we fit, the dashboard we build, points back to that. The result is a small number of reports that get opened every week rather than a large library of dashboards nobody remembers logging into.
If any of these sound like your business right now.
- Finance is copying rows out of Shopify, Stripe, or QuickBooks into a spreadsheet every Monday morning to build the weekly report.
- Leadership asks "why did last quarter miss the number" and each department gives a different answer with different data.
- Your marketing dashboard, your finance dashboard, and your operations dashboard all report different revenue figures for the same period.
- You have a data warehouse but nobody outside the technical team can get an answer out of it without asking someone to write a query.
- Reports get built with real effort, opened twice, and never opened again.
- You suspect the analytics tag on your site is broken or double-counting but have not opened GA4 in months to check.
How we run a data analytics engagement.
- 01
Decision-first discovery
Before touching a data source we scope the decisions the team needs to make on a recurring basis. What does leadership need to know before Monday, and what will they do with the answer? Everything downstream, from ingest to dashboard, gets built to serve that decision.
- 02
Source audit and integration
Inventory the data estate: what already exists, what is clean, what is duplicated, and what is missing. Integrate Shopify, HubSpot, Stripe, QuickBooks, Salesforce, GA4, and any custom sources through their APIs into a single warehouse-of-truth.
- 03
Pipeline, warehouse, and models
Batch or streaming ingest as the shape of the data dictates, transform with dbt or equivalent, and land it in a warehouse schema tuned for the questions leadership actually asks. Predictive or diagnostic models only where the payoff justifies the build.
- 04
Reports the team opens weekly
Three to five dashboards that answer the recurring decisions, plus a monthly insight brief written for the business owner rather than the analyst. Success is defined by the dashboards being opened, not by the count of dashboards built.
What we ask before we scope.
Every data analytics engagement starts with a discovery call, not a template quote. These are the questions we open with so both sides know whether the fit is real before anyone signs anything.
- Q01
What is the single recurring question in the business that takes more than 15 minutes to answer today?
- Q02
Which reports does leadership actually open every week, and which ones get built and forgotten?
- Q03
Where does customer, order, and revenue data live right now (Shopify, HubSpot, QuickBooks, Stripe, Salesforce, a custom app, or a mix)?
- Q04
Have you tried to build analytics in-house or with an agency before, and what specifically stalled?
- Q05
Do you handle personal, health, or financial data that carries a specific privacy regime we need to design against?
- Q06
What business decision would actually change tomorrow if you had a report that answered a specific question you cannot answer today?
Concrete outputs at the end of every project.
- Source-of-truth audit across the current data estate
- Ingest + transform pipeline (batch or streaming as fits)
- Warehouse schema tuned for the questions the business asks
- Dashboards for the 3-5 decisions that recur
- Predictive or diagnostic models where the payoff justifies them
- Monthly insight brief written for the business owner, not the analyst
How this fits with the rest of the studio.
Sapere Digital runs eight disciplines under one operating standard. Data Analytics lands harder when it is scoped alongside the practices it naturally reinforces.
Digital Marketing
Every campaign gets tied to real revenue in the warehouse rather than platform-reported impressions, and attribution finally spans the customer's actual buying window instead of stopping at the last-click.
SEO
Organic sessions get attributed back to leads and revenue in the same warehouse your paid channels report against, so channel-level performance stops living in three separate tool silos.
Artificial Intelligence
Retrieval systems and agents ground themselves in the same warehouse your analytics report from, so the answers people ask the AI and the answers the dashboards show line up to the same version of the truth.
Questions before you start.
How do we know we need data analytics?
The signal is usually a recurring question your team cannot answer in under 15 minutes. If finance is copying rows out of Shopify into Excel every Monday, or leadership is asking "why did last quarter miss" and getting different answers from each department, that is where analytics starts to pay back.
What data tools do you use?
We stay stack-agnostic. Common builds: Google Analytics 4 plus BigQuery for web and product, Snowflake or dbt for the warehouse, Metabase or Looker Studio for dashboards, Python or dbt for models. We meet you where your team already lives rather than forcing a rip-and-replace.
Do you handle data privacy and compliance?
Yes. Every pipeline is built with data minimisation and access controls. If you handle personal, health, or financial records we scope the controls around whichever regime applies (PIPEDA in Canada, HIPAA, SOX, or state-level US privacy laws). We do not build systems that create liability.
How long before we see business impact from analytics?
The first useful dashboard usually lands within four weeks: one report that answers one recurring question the team stopped bothering to ask. Deeper models (forecasting, cohort analysis, propensity) land in the second quarter. Impact compounds once the team makes decisions on the numbers rather than around them.
Can you integrate our existing tools instead of replacing them?
Yes, and that is usually the right call. We integrate with Shopify, HubSpot, Salesforce, Stripe, QuickBooks, and most SaaS platforms via their APIs. Replacing tools is a rebuild; connecting them is an integration. We only recommend replacement when the existing tool is genuinely blocking the answer.
Cybersecurity
Security work built on what you actually have running. Audit, access hardening, monitoring, and an incident-response plan the team could run tomorrow.
Digital Marketing
Performance, brand, and content marketing built to compound. We connect campaign craft to the metrics your business actually runs on.