Data Engineering & Analytics

Informatica is still the right answer for enterprise MDM, data governance, and the regulated reporting workloads where its lineage and rule-engine capability are hard to replicate. The modern stack (Snowflake or Databricks for compute, dbt for transformation, Fivetran for ingestion, a BI layer on top) is a strong fit for analytics-first estates without heavy MDM requirements. Most enterprise estates we work in need both for different workloads; the architecture question is which is the system of record for which domain.

Narrowly, on purpose. The most common MDM failure is to start with "unify all customer data" and discover, two years in, that the political work was the real programme. We start by picking a bounded domain (one critical business entity), shipping a working golden record for that entity in three to six months, and demonstrating the operating model. Expansion follows demonstrated success. The technology is rarely the bottleneck.

Data governance is an operating model, not a tooling decision. Tools matter — we deliver on Informatica Axon, Collibra, and the cloud-native equivalents — but the governance only works when the data stewardship roles are defined, accountable, and tied to real business outcomes. We design the operating model alongside the tooling, and we will not start a tooling rollout if the role definitions are absent.

Yes. We have shipped regulator-facing reporting platforms for energy authorities and federal ministries — fact extraction, aggregation, rule application, scenario modelling, audit trail. The architecture differs from analytical BI: reproducibility, lineage, and signed-off versions matter more than dashboard polish. We design for that distinction from the start.

It depends what you mean. If you mean "can your data foundation feed RAG and ML pipelines" — yes, that is the default deliverable shape. If you mean "will the dashboards become AI-native" — that is our AI Insight product roadmap conversation, and it is genuinely different work. We separate the data engineering investment from the AI-application investment and price them on their own merits.

Power BI for Microsoft-aligned estates (which most regulated and government clients are). Tableau where the analyst tooling preference is established. Looker for SQL-first organisations on the modern data stack. We deliver on all three; we do not have a strong opinion absent the surrounding estate.