Data Analytics Services — from raw data to decisions.

A focused data foundation — warehouse setup, 3–5 ELT pipelines, dbt models, and a core BI dashboard — takes 6–10 weeks. A full modern data stack with multiple sources, a complete dbt project, semantic layer, and multi-team dashboards takes 12–20 weeks. Adding predictive analytics models extends the timeline by 4–8 weeks. The most common schedule risk is data quality — poorly structured source data multiplies transformation time by 2–3×. We run a data audit in week one that surfaces quality issues before they affect the build timeline.

Snowflake is right for multi-cloud organisations and teams that need flexible data sharing across business units. BigQuery is right for teams standardised on Google Cloud — serverless, with zero cluster management and tight ML integration. Amazon Redshift is right for teams heavily invested in AWS. Databricks is the right choice when significant ML workloads run alongside analytics and you need a unified lakehouse. Cost varies by query pattern: Snowflake and Redshift use compute clusters (cost scales with time), BigQuery uses serverless per-query pricing (cost scales with data scanned). We recommend based on your existing cloud commitment and query patterns — not a vendor preference.

A KPI (Key Performance Indicator) is a quantitative metric that measures performance against a specific business objective — revenue growth rate, customer churn rate, gross margin, CAC:LTV ratio. Most businesses track too many KPIs, which dilutes attention. 8–12 KPIs per business level is the optimal range — enough to cover the key dimensions of performance (revenue, cost, customer, operations) without overwhelming decision-makers with noise. We define these with business stakeholders before writing a line of SQL, because a KPI defined incorrectly in the warehouse propagates the wrong number across every dashboard built on top of it.

Yes. Connecting data from multiple source systems into a centralised warehouse is the core of data engineering. Fivetran has 400+ pre-built connectors covering Salesforce, Stripe, HubSpot, Google Analytics, Shopify, and most SaaS tools — setup takes hours rather than weeks of custom code. For internal databases (PostgreSQL, MySQL), we use native connectors or log-based CDC (Change Data Capture) replication. Once all sources land in the warehouse Bronze layer, dbt joins and transforms them into unified models — a single customer record combining CRM, billing, and product usage data from separate source systems.

Self-service analytics lets business users — product managers, marketers, finance — explore data and build their own views without writing SQL or waiting for analyst bandwidth. You need it if your analysts spend more than 30% of their time answering ad-hoc data questions from other teams. Tools like Power BI, Looker, and Metabase provide drag-and-drop exploration on top of governed warehouse data. The governance piece is critical: without a semantic layer defining KPIs centrally, self-service creates competing numbers. We build self-service on top of dbt mart models and a semantic layer, so users explore freely within guardrails that enforce consistent metric definitions.

Four mechanisms ensure data quality: dbt automated tests on every model, pipeline monitoring with Airflow alerts, data freshness checks, and anomaly detection on key metric values. dbt tests run on every model execution — not null (required fields present), unique (no duplicate primary keys), referential integrity (foreign keys exist), and accepted values (categorical fields only contain valid values). A test failure in a staging model prevents the downstream mart model from running, preventing a bad number from reaching a dashboard. Airflow or Prefect sends Slack alerts when any pipeline fails, freshness checks flag stale tables, and anomaly detection fires when a KPI deviates more than 2 standard deviations from its 30-day moving average.

Yes. Data analytics directly supports GEO (Generative Engine Optimisation) and AEO (Answer Engine Optimisation) by measuring which content gets cited by AI engines, tracking traffic from AI-referred sessions, and identifying the query patterns where your content wins or loses citations. We build dashboards that connect Google Search Console data, GA4 organic traffic, and AI referral source tracking — letting your content team see which pages get cited by ChatGPT, Perplexity, and Google AI Overviews. That data drives content decisions: which questions to answer more specifically, which pages need entity-density improvements, and where competitors are winning AI citations you should claim.