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One connected Data & Analytics practice spanning engineering, warehousing, scale, insight, and governance: built so the numbers your business runs on are accurate, current, and easy to act on.
Data & Analytics is one of the seven service domains behind every 3Shadz engagement: the specific engineering, modelling, and governance skills we use to turn scattered data into something your business can actually run on.
It brings together five connected capabilities: Data Engineering to move data reliably, Data Warehousing to model and store it, Big Data Engineering to handle it at scale, Business Intelligence to turn it into dashboards people use, and Data Governance to keep it accurate, secure, and accountable throughout.
You can start anywhere in that chain (a single pipeline, a warehouse migration, one dashboard, or a governance review) and expand as the platform proves its value.
Each capability stands on its own, and each one strengthens the next, from moving data reliably, to storing it well, to scaling it, to putting it in front of the people who need it, governed the whole way through.
Everything starts with data that arrives where it’s needed, in the shape it’s needed, on a schedule you can rely on. We design and build the pipelines that move data from your source systems into the platforms your business runs on.
Raw data becomes a modelled, queryable warehouse, structured around how your business actually asks questions, not how the source systems happen to store it.
When data volume, velocity, or variety outgrows a standard warehouse, we design distributed architectures that keep processing fast and costs predictable.
Data becomes decisions through dashboards and reports people actually open: built around the questions your teams ask every week, not a generic template.
Governance runs across every layer above it (who can see what, where a number came from, and whether it can be trusted), designed in from the start rather than retrofitted after an audit.
The same four-stage approach carries every engagement, whether it starts with a single pipeline or a full platform build.
Map your source systems, business questions, and the metrics that actually drive decisions before designing a single pipeline.
Build the pipelines and integrations that move data reliably from source to store, with quality checks built in, not bolted on.
Land data in a modelled, access-controlled warehouse, with lineage, ownership, and definitions documented as it’s built.
Ship dashboards people actually use, then monitor and refine pipelines as your data and questions evolve.
A data platform is only worth what people do with it. We optimize for dashboards your teams check every day, not ones built once for a demo and abandoned.
A lot of data projects stall because nobody trusts the numbers. Here’s what keeps ours trustworthy.
Every dashboard and report draws from the same governed warehouse, so numbers finally agree across teams.
Pipelines are built and tested like software, with monitoring and alerting from day one.
Access control, lineage, and data quality are part of the design, not a retrofit after an audit.
We choose the warehouse, processing engine, and BI tool that fit your stack, not the vendor we prefer.
Architectures are designed for tomorrow’s data volume, so you don’t re-platform every eighteen months.
The same team that builds your data platform also builds the software, cloud, and AI layers around it.
We choose the warehouse, processing engine, and visualization tool that fit your constraints and existing stack, not the other way around.
Cloud & Data Solutions, under Solutions, is the outcome-focused program, what a modern data and cloud platform can do for your business. Data & Analytics, under Services, is the specific discipline behind the data side of it: Data Engineering, Data Warehousing, Big Data Engineering, Business Intelligence, and Data Governance.
No. Most engagements start with one capability, often Data Engineering or Business Intelligence, and expand into warehousing, scale, or governance as the platform matures.
Yes. We are platform-agnostic and regularly work with Snowflake, BigQuery, Redshift, Azure Synapse, Databricks, and on-premises databases, building around what you already run wherever it makes sense.
All engagements are NDA-friendly. We apply role-based access control, data classification, and audit-ready governance from the start, and can design to specific frameworks such as GDPR, HIPAA, or SOC 2 where required.
A data audit or BI quick-start typically takes two to four weeks. A full warehouse build or governance program usually runs eight to sixteen weeks depending on the number of source systems involved.
Yes. Pipelines, warehouses, and dashboards are monitored and tuned after launch, so data quality and performance hold up as new sources and reporting needs are added.
Whether you need one pipeline, a governed warehouse, a dashboard people will actually use, or a full data platform, our Data & Analytics team can help you get there.
