Data as an operating capability, not a project.
We design and build the data foundations, analytics and decision systems that let the business run on evidence — reliably, at pace, and at scale.
Executive summary
Data is now the substrate every other capability depends on. AI models, digital products, operational decisions and executive reporting all trace back to the same question: is the underlying data trustworthy, accessible and current.
Laminin's Data & Intelligence practice covers the full arc — strategy, platform, engineering, analytics, decision systems and governance. We treat data as a product, not a project, and design for the operational reality of how the organization actually uses it.
Our work anchors many of our other engagements: no serious AI or digital-product program lands without the data layer that supports it.
The problems we address
A decade of tools, still no single source of truth
Multiple warehouses, dashboards that disagree, and reconciliations that consume more effort than the analysis.
Analytics that answers questions no one asked
Rich reporting, thin decision-making. The gap between insight and action never closes.
AI ambitions blocked by data reality
The strategy assumes clean, integrated, well-governed data — the estate does not have it.
Data platforms accumulating cost without accumulating value
Compute and storage growing faster than usage; ROI increasingly hard to defend.
Governance treated as documentation, not engineering
Policies exist, controls do not. Regulatory exposure is real and rising.
Analytics talent held back by the platform
Strong analysts and scientists spending most of their time on plumbing rather than on the questions that matter.
What Laminin does
Data strategy and operating model
Where data creates value, how it is owned, funded, produced and consumed — and what changes to make that real.
Modern data platforms
Cloud data platforms, lakehouse architectures, streaming and event-driven data, and the FinOps discipline to keep them economical.
Data engineering and product mesh
Reliable pipelines, data contracts, quality tests and the product-oriented model that makes data usable at scale.
Analytics and BI at executive quality
Reporting and analysis that shortens the distance between question and decision, with modern semantic layers.
Machine learning and decision intelligence
Predictive systems and decision workflows embedded in operations — ranking, forecasting, optimization, targeting.
Data governance operationalized
Ownership, lineage, quality, privacy and regulatory controls translated into engineering practice, not policy binders.
Ten data disciplines
Composed to fit the estate and the decisions that depend on it.
Data Strategy
Where data creates value and how the organization gets there.
Data Engineering
Pipelines, contracts, quality and the operational spine of the data layer.
Data Platforms
Cloud data platforms, lakehouse, streaming and event-driven architectures.
Analytics
Deep analytical work that translates data into strategic insight.
Business Intelligence
Executive reporting, semantic layers, and self-serve models that hold up under load.
Decision Intelligence
Structured decision-making combining analytics, models and judgment.
Machine Learning
Predictive, ranking and optimization systems in production, not notebooks.
Data Governance
Ownership, lineage, quality, privacy and controls, operationalized.
Data Visualization
Editorial-quality visualization that carries the argument, not the chart junk.
Predictive Analytics
Forecasting, risk modelling and scenario analysis at the decision boundary.
Four phases from strategy to running platform
Diagnose
Assess estate, use cases and constraints — where the value and the friction really live.
Architect
Target platform, data model, ownership and governance designed for the use cases that matter.
Build
Engineer pipelines, products and consumption layers with quality tests, observability and data contracts.
Operate
Run and evolve the platform — FinOps, reliability, governance, and continuous adoption.
The data stack we work in
Selected per engagement — we are cloud and vendor pragmatic.
- WarehousesSnowflake, BigQuery, Databricks, Redshift and equivalent lakehouse architectures — chosen by workload, cost and team fit.
- Transformationdbt, SQLMesh and code-first transformation with tested, versioned models and semantic layers.
- StreamingKafka, Kinesis and Pub/Sub for event-driven pipelines where latency and freshness matter.
- OrchestrationAirflow, Dagster and Prefect — with lineage, retries and observability built in.
- BILooker, Power BI, Tableau, Metabase and Superset — with a shared semantic layer, not tool-specific silos.
- MLOpsFeature stores, experiment tracking, model registries, and monitoring for drift, quality and cost.
- GovernanceData contracts, catalogs, lineage, quality frameworks and access controls aligned to privacy and sector regulation.
Related industries, work and insights
Relevant industries
Related work
Questions data leaders ask us
Do you take a position on the modern data stack?
Pragmatic. We favor code-first transformation, tested pipelines, a shared semantic layer and clear ownership. The specific tools are chosen per engagement — never a house stack pushed on the client.
How do you approach governance without slowing everyone down?
Governance is embedded — data contracts, quality tests and access controls in the pipeline, not in a document. The goal is faster confident use of data, not new committees.
Where does this capability meet the AI capability?
Directly. Most AI programs eventually stall on data. We often run the data workstream alongside the AI workstream so they land together.
Do you operate platforms after they are built?
Yes — managed-operate arrangements, or short handover programs into a client team we have built alongside.
Have a data question you can't put on a dashboard?
Tell us what decision the data has to support. We'll design the platform around that, not the other way around.