Data & Analytics

Data strategy & architecture

Target-state architectures, platform selection and roadmaps for warehouses, lakehouses and data mesh approaches.

Overview

A data strategy should explain how data will help the business make better decisions, not just which platform to buy. We start with the outcomes leaders care about, the questions they cannot answer today and the AI and analytics use cases on the roadmap, then work back to the data, architecture and organization needed to support them.

On the architecture side, we assess your current estate and define a pragmatic target state: how data is ingested, stored, modeled, governed and served to reports, applications and AI. We compare warehouse, lakehouse and data mesh patterns honestly, and support platform selection with criteria grounded in your workloads, skills, existing investments and cost.

The output is a roadmap sequenced by business value, with each increment delivering something usable. We also define the operating model behind it, including domain ownership, platform team responsibilities and the standards that keep a growing data estate coherent.

What you receive

01Current-state assessment of data assets, flows and pain points
02Target-state architecture with design principles and standards
03Platform evaluation and selection recommendation
04Value-sequenced roadmap with cost and resourcing view
05Data operating model and ownership structure

Technologies we work with

Snowflake
Databricks
Microsoft Fabric
Google BigQuery
Amazon Redshift
Apache Iceberg
dbt

We are technology-agnostic: tools are chosen to fit your requirements and existing investments.

Questions we often hear

Should we choose a warehouse, a lakehouse or a data mesh?

They solve different problems and are often combined. We recommend an approach based on your data types, workloads, team structure and maturity, and we avoid adding organizational complexity, such as a full data mesh, before it is needed.

How long does a data strategy take?

A focused strategy and target architecture typically takes six to ten weeks. If a platform decision is urgent, we can run a shorter evaluation in parallel.