Data Management Consulting Services

Iseyon builds the data foundation that analytics, AI and regulatory reporting depend on: named ownership, agreed definitions, tested pipelines, and controls that hold up in an audit. Most engagements start from the same place. Data lives in several systems of record, the same customer or product exists under several identifiers, and two reports on the same subject disagree. The work is getting from that state to a governed environment your own team runs.

An engagement moves in phases. Discovery comes first: we inventory the systems of record, trace how each critical field is produced, and write down where numbers diverge and why. That gives a shared picture of the current state and a ranked list of what is worth fixing first. Design follows: target architecture, the ownership model, the quality rules, and the standards each domain is held to. Build happens in slices, one domain at a time, so something is in production and trusted before the next domain starts. Handover is planned from the first week, because the point is that your team can extend the model without us.

Data Strategy & Governance Framework

Iseyon writes the data strategy against decisions the business is trying to make, then builds governance that supports those decisions rather than a policy binder nobody opens. That means a named owner per domain, a written definition for every metric that reaches a board deck, an approval path for changes to those definitions, and a standing forum where disagreements get settled instead of routed around. The deliverables are working artifacts: catalog entries for critical assets, quality rules expressed as tests that run on every load, and an escalation path that says who fixes what when a test fails.

Master Data Management (MDM)

Iseyon designs and implements master data solutions that give critical entities such as customers, products and suppliers one trusted record. Design starts with survivorship rules agreed with the business: which source wins for which attribute, and who arbitrates when none of them is right. Then comes match and merge logic, a stewardship queue for the records automation cannot decide, and publication of the golden record back into the systems that act on it. The pattern that recurs across engagements is that matching is the easy part. Getting two departments to accept one definition of a customer is the work.

Data Quality & Standardization

Iseyon profiles the data before anyone agrees a target, because the conversation changes once the actual state of each critical field is visible. Profiling, cleansing, validation and standardization then move into the pipeline itself rather than living in a one-off cleanup script: each rule becomes a test, each test has an owner, and failures stop bad records instead of surfacing weeks later inside a report. Your team takes the rule set, the tests and the exception workflow at handover.

Data Architecture & Integration

Iseyon designs architectures that survive the addition of the next source system. That means clear contracts between the raw, conformed and serving layers, an ingestion pattern chosen per source rather than one tool forced onto everything, and an agreed answer for schema changes before the first pipeline ships. Where APIs and streaming are the right fit we use them, and where a nightly batch is sufficient we say so.

Metadata Management & Lineage

Iseyon implements metadata and lineage so that "where did this number come from" is answerable without an engineer. Definitions, ownership, refresh timing and upstream dependencies are captured against the assets people actually use. That turns an audit question into a lookup, and it makes the blast radius of a proposed change visible before the change ships.

Data Security & Compliance

Iseyon builds access control on the identity provider you already run, so permissions follow joiners, movers and leavers without a side process. Sensitive fields are masked or tokenized by classification, encryption covers data at rest and in transit, and access logging answers who read what and when. Compliance obligations are mapped to specific controls, and each control is documented in a form an auditor can follow.

Data Lifecycle Management

Iseyon defines retention, archival and disposal per class of data rather than per system, so the same rule applies wherever a record lands. Cold data moves to cheaper storage on a schedule, deletion is provable, and the basis for each retention period is written next to the policy that enforces it.

Cloud & Hybrid Data Management

Iseyon runs one governance model across cloud, hybrid and on-premise estates: the same definitions, the same access model, the same quality tests, wherever the data physically sits. In migrations the legacy path and the new path run in parallel until outputs reconcile, so cutover happens on evidence rather than on a date.

Modernization & Optimization

Iseyon replaces legacy data systems in slices, starting with the pipelines whose failure wakes somebody up. Each slice gets automated tests and reconciliation against the system it replaces, and the old job is retired deliberately once the new one has held. Along the way we remove the jobs, tables and reports nobody uses, which is usually the fastest cost reduction available on a legacy estate.

Recurring Problems and the Patterns We Apply