Data Analytics
Data that tells you what to do — not just what happened.
Most analytics projects fail at the strategy layer — teams collect everything and analyse nothing.
Analytics strategy, stack design, and implementation for enterprises that have data but aren't using it. We start with the decisions that matter most to your business and design backwards from there. 18 years of building the systems that generate data means we know where the value is buried.
Analytics impact across AurvikAI clients
transactions analysed monthly
dashboards in active daily use
daily route optimisations
avg. decision speed improvement
Analytics capabilities we deliver
From executive dashboards to embedded operational analytics.
Decision analytics
We identify the 3–5 decisions your business makes regularly where better data changes the outcome, then build the analytics layer that delivers the right insight at the right moment to the right person.
faster decision cycles
Analytics architecture
The right stack for your actual needs.
Data warehouse, lakehouse, or mart — the right architecture depends on your query patterns, data volume, freshness requirements, and team capability. We recommend the simplest stack that meets your requirements, not the most technically sophisticated option.
of analytics use cases served by modern cloud warehouse + dbt + Looker/Metabase
AurvikAI architecture assessments
Cloud data warehouse
BigQuery, Snowflake, or Redshift — selected based on your cloud provider, budget, and query patterns.
Transformation layer
dbt for version-controlled, tested, documented data transformations that your team can maintain.
Ingestion pipeline
Fivetran or Airbyte for managed ingestion from SaaS tools, databases, and APIs.
Visualisation
Looker, Metabase, or Tableau — selected for your user base, not for the vendor's marketing.
How we build analytics that people use
Adoption is the only analytics metric that matters.
Start with the decisions, not the data.
Identifying the 3–5 recurring decisions where better data would change the outcome.
Mapping what data is needed, what exists, and what gaps need filling.
Defining who will use each analytics product — their questions, cadence, and technical literacy.
Designed for adoption
Built for the executive who reads dashboards on a phone.
Every dashboard and report is designed for the specific person who will use it — their questions, their decision cadence, their technical literacy. We prototype with real users before building, and measure adoption after launch. A dashboard that 10 people open every morning is more valuable than one that 100 people have bookmarked but never visit.
Adoption is the metric we care about.
daily active usage across deployments
rounds of user testing per project
Common questions about data analytics
From stack selection to real-time requirements.
Cloud data warehouse (BigQuery, Snowflake, or Redshift), dbt for transformation, Fivetran or Airbyte for ingestion, and Looker or Metabase for visualisation. This handles 99% of analytics use cases, is operated by a small team, and scales from startup to enterprise.
INSIGHTS
Thinking worth reading
Ready to build analytics your team actually uses?
Let's start with the decisions that matter most to your business — and design analytics backwards from there.