Data Analytics & Business Intelligence
Turn raw business data into decision-ready intelligence with data engineering, BI dashboards, reporting, visualization and predictive analytics.
Key Capabilities & Deliverables
- Snowflake, Databricks & BigQuery Lakehouses
- Real-time ETL / ELT Data Pipelines
- Executive Power BI & Tableau Dashboards
- Customer Analytics & Cohort Modeling
- Data Governance & Quality Management
Bring multi-source transaction logs and event streams into one modelled warehouse, so every executive dashboard reads from a single, tested definition of each metric.
Who This Is For
For businesses whose numbers live in several systems and get assembled by hand into a spreadsheet each week or month. The usual symptom is two people producing different figures for the same question, and nobody being sure which is right.
What You Get
We build the pipeline from source systems to decision: ingestion, a modelled warehouse layer with defined metrics, and dashboards on top. The definition of a metric lives in version-controlled transformation code, so "revenue" means one thing across every report rather than whatever each analyst filtered.
What We Deliver
- Data warehouse and lakehouse architecture
- A central store on Snowflake, BigQuery, Databricks or Postgres, structured in raw, modelled and reporting layers so transformations are traceable and reversible.
- ETL and ELT pipelines
- Scheduled and streaming ingestion from your operational databases, SaaS tools and event streams, with data quality tests that fail the run rather than quietly publishing bad numbers.
- Metric definitions in code
- Business logic expressed in dbt models under version control, so a metric change is reviewed and dated instead of edited inside a dashboard nobody audits.
- Executive and operational dashboards
- Power BI, Tableau, Metabase or Superset reporting built around the decisions people actually make, with drill-through to the underlying rows.
- Governance and lineage
- Access control, column-level lineage and freshness monitoring, so you can answer where a number came from and when it last updated.
How We Work
- Discover (1 week) — We map the business problem before writing code: who the users are, which systems already hold the data, what "done" has to look like, and what the real constraints are on budget, compliance and timeline.
- Define (1–2 weeks) — We agree the architecture, the integration points and the scope of the first release, then write it down. You get a build plan you can hold us to, not an open-ended retainer.
- Build (2–8 weeks) — Delivery runs in short increments against a working environment you can see. Automated tests and code review are part of the increment, not a phase bolted on at the end.
- Deliver (1–2 weeks) — We ship to production with monitoring and rollback in place, migrate data, and hand over documentation and access. Code lives in your repository throughout.
- Optimize (Ongoing) — After launch we watch the real numbers, fix what the telemetry exposes, and ship the next increment. The ABC Transport ERP, for example, still receives new modules every month.
How We Choose the Stack
Snowflake, BigQuery, Databricks and PostgreSQL for storage; dbt and Airflow for transformation and scheduling; Kafka and Flink where ingestion has to be real time; Power BI, Tableau, Metabase and Superset for reporting.
Related Work
- ABC Transport: fleet reporting on a single source of record — Fleet reports and the ledger read from the same trip entry as billing, which removed the three-way manual reconciliation that used to define month end.
Further Reading
- The Practical PostgreSQL Tuning Guide: Fixing Slow Queries, Connection Pools & Vacuum Bloat — Before you upgrade your AWS RDS instance or migrate to NoSQL, 95% of database bottlenecks come down to missing composite indexes, unconfigured connection pooling, and mismanaged autovacuum settings.
Data & Analytics FAQ
- Do we need a data warehouse, or will dashboards do?
- If your reporting draws on one system, connect a dashboard to it and skip the warehouse. The warehouse earns its cost once numbers have to be joined across systems, or once you need history that the source system overwrites. We will tell you which situation you are in before proposing infrastructure.
- Why do two of our reports disagree today?
- Almost always because the metric is defined twice, in two dashboards, with slightly different filters or date handling. The fix is to define it once in transformation code that both reports read from. Reconciling the existing disagreement is usually the first deliverable, because it is also the fastest way to map how your data really flows.
- How current can the numbers be?
- Daily batch covers most reporting and is the cheapest to run and reason about. Hourly or near-real-time is straightforward where the source systems support change capture. Genuine streaming is worth the added operational cost only when someone acts on the number within minutes — which is rarer than it is asked for.
- Who owns the code you write?
- You do. Work is committed to a repository you own and control from day one, along with the infrastructure definitions, environment configuration and deployment scripts needed to run it without us. There is no proprietary runtime or licence you have to keep paying for to use what we built.
- How do engagements usually start?
- With a scoping conversation, not a proposal template. We look at the systems and data you already have, agree what the first release needs to do, and come back with a written build plan covering scope, sequence and timeline. If that plan says the work is smaller than you expected, we will tell you.
- How do you work across time zones?
- Our team works from Delhi, and how much of the day we share depends on where you are. With the Gulf it is close to a full working day; with the UK and Europe it is a few hours each morning; with North America there is no natural overlap, so we work from written handoffs and one scheduled call. Each market page sets out the actual hours for that region.