Data and AI

Data and AI programmes succeed when business glossaries, technical metadata, and access policies align rather than drifting apart. Hosn Al Tomoh implements catalogues and lineage so stewards know who approved a dataset for which use cases, including machine learning training, retrieval indexes for LLMs, and customer-facing analytics products. That alignment prevents the common failure mode where compliance teams discover downstream uses long after data products were approved for narrower purposes by domain owners.

Quality checks, anomaly detection, and reconciliation jobs are scheduled with explicit ownership and escalation paths. Finance and operations trust dashboards when exceptions route to named teams who acknowledge, investigate, and resolve within agreed timeframes. Silent failures in pipelines are the enemy of trust, so Hosn Al Tomoh invests early in observability and alerting patterns that surface issues before stakeholders encounter them through downstream reports or dashboards during critical business reviews.

Lakehouse and warehouse implementations favour open formats, clear access patterns, and pragmatic compute choices over vendor zealotry. Delta, Iceberg, and Parquet choices depend on your workload mix, latency tolerance, and ecosystem compatibility. Hosn Al Tomoh implements these foundations with migration paths in mind. Data architectures outlive platforms, and the wrong format choice today becomes an expensive rewrite when analytics, machine learning, and operational systems each need different access patterns later.

For AI, we emphasize evaluation harnesses, human oversight for high-risk decisions, and FinOps for training and inference. Those elements answer the questions regulators and boards now ask routinely, especially in financial services, healthcare, and public sector contexts. Model cards, data cards, evaluation reports, and drift monitoring dashboards sit alongside model endpoints so governance reviewers find the artefacts they need without asking engineering teams to produce them repeatedly for different audiences.

MLOps and LLM operations depend on repeatable training, evaluation, and deployment patterns. Feature stores, prompt management, and versioned artefacts let teams iterate quickly without losing track of what ran in production. Shadow deployments, canary releases, and rollback procedures apply to AI workloads just as they do to software services. That reduces surprises when models start serving real traffic and their outputs have financial or operational consequences.

Data products are built with ownership in mind rather than as byproducts of analytics projects. Domain teams own the data they produce, central platform teams own shared infrastructure, and governance spans both. That separation of concerns is what makes data mesh workable when appropriate and keeps centralised lakehouse approaches performant when that fit is better. Hosn Al Tomoh recommends the pattern that matches your organisation rather than prescribing one ideology to every client engagement.