Modern Stack, Not Legacy
Cloud warehouses (BigQuery, Snowflake, Redshift) + ELT (Fivetran, Airbyte) + dbt for transformation. Reproducible, version-controlled.
Data Analytics & Business Intelligence
Things at Web builds modern data stacks, BI dashboards, and analytics pipelines that put the right number in front of the right person at the right time. We connect your operational systems, model the data, and ship dashboards your team actually uses.
A modern analytics function is more than a BI tool. It is a stack of ingestion, transformation, modelling, and visualisation designed so business users get trustworthy answers without waiting for a data team ticket. Done well, analytics shortens the time from question to decision by an order of magnitude.
Actionable Insights
Data Analytics & Business Intelligence. Turn Data Into Decisions, Not Dashboards Ship.
Overview
Things at Web builds modern data stacks, BI dashboards, and analytics pipelines that put the right number in front of the right person at the right time. We connect your operational systems, model the data, and ship dashboards your team actually uses.
DATA INGESTAGGREGATING…
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Service Overview
What we actually deliver
A modern analytics function is more than a BI tool. It is a stack of ingestion, transformation, modelling, and visualisation designed so business users get trustworthy answers without waiting for a data team ticket. Done well, analytics shortens the time from question to decision by an order of magnitude.
We design analytics functions to shorten the time from question to decision ingestion, transformation, modelling, visualisation, and the governance that keeps definitions stable across teams. Done well, analytics shifts from reporting backwards to driving the next move.
Why choose us
Five reasons to choose us
Sources Unified, Definitions Agreed
We resolve definition conflicts in workshops before we build. No two teams arguing about which 'revenue' number is right.
Self-Service That Actually Works
Semantic layers so business users get trustworthy numbers without a data team ticket and without misuse.
Predictive Where It Earns
Forecasting, churn, propensity added where there is a real decision attached, not because the slide deck called for ML.
Quality Engineering
dbt tests at every transformation step plus source-layer contracts. Anomalies caught in the pipeline, not in the dashboard.
What We Build
Our capabilities
The core capabilities we deliver in every engagement all strict-fact, all in production today.
Modern Data Stack
BigQuery, Snowflake, or Redshift paired with Fivetran/Airbyte ingestion and dbt transformation version-controlled, reproducible.
BI Dashboards
Power BI, Tableau, Looker, or Metabase dashboards your team actually uses built from agreed definitions.
Data Warehouse Design
Source-to-mart modelling, dimensional schemas, slowly-changing dimensions, semantic layers.
ELT Pipeline Engineering
Cloud-native ingestion from SaaS tools, on-prem systems, and CDC sources with dbt for transformation.
Data Quality Engineering
Tests at every transformation step plus contracts at the source anomalies caught in pipeline, not in the dashboard.
The process
How we work
Our step-by-step engagement model ensures complete transparency, predictable iterations, and full code ownership on handover.
Discovery & scoping
We map your goals, audit anything that already exists, and agree on scope and success measures before any code is written.
Plan & team
You get a concrete plan with timeline and a named delivery team the same specialists who will actually build your product.
Build in iterations
Working software every iteration, with direct access to the engineers. No account-manager relay, no outsourcing chain.
Launch & ownership
Full ownership of code, hosting, and design files transfers to you on handover. Ongoing operations support is optional, never lock-in.
FAQ
Frequently Asked Questions
Answers to common questions about our process, deliverables, and how we work.
A modern data stack pairs cloud data warehouses (BigQuery, Snowflake, Redshift) with ELT ingestion (Fivetran, Airbyte), SQL transformation (dbt), and BI on top (Power BI, Tableau, Looker, Metabase). It replaces brittle ETL pipelines with reproducible, version-controlled data flows.
Most enterprises start with a warehouse for structured analytics. A lake (or lakehouse like Databricks) is added when you also need to store and process unstructured data text, images, logs and run ML on it. Start with the warehouse, add lakehouse capabilities only when use cases justify the complexity.
A first usable dashboard on top of a clean source can ship in 2–4 weeks. If the source needs cleaning, modelling, or integration, add another 4–8 weeks. The bottleneck is rarely the BI tool it is the data preparation and the agreement on definitions.
Three usual reasons: the metrics don't match how decisions actually get made, the data behind them is mistrusted, or the dashboard is too slow to load. We diagnose all three with a usage audit before rebuilding.
Tests at every transformation step (dbt tests, expectations) plus contracts at the source layer. Anomalies are caught at the pipeline, not by the executive viewing the dashboard. Data quality is engineering, not an afterthought.
Yes most modern SaaS tools have first-class connectors (Salesforce, HubSpot, Stripe, Shopify, Workday). On-prem systems are connected via CDC tooling, direct database replication, or API extracts. We design the ingestion strategy per source.
Get Started
Ready to talk Data Analytics & Business Intelligence?
Book a 30-minute discovery call. We'll walk through your needs, propose a concrete first wave, and tell you honestly whether it's the right fit.