Cross-industry engineering experience across regulated and high-growth sectors.
All Industries
“Gartner found 60% of data and analytics leaders blame culture, not funding, when governance fails.” — Gartner, 2026
“More than 70% of organizations have a CDO, yet unclear authority still limits the role.” — EDM Association
Ownership and controls for lending, deposit, and trading data, linked to our banking technology work.
BCBS 239 and SOX lineage evidence, delivered alongside regtech engineering for regulatory reports.
Policy, claims, and underwriting data governed for audits, supported by insurance technology expertise.
Asset inventories and access controls ahead of HIPAA Security Rule changes, backed by healthcare software engineering.
Consent records and CCPA risk assessments tied to the customer data each one covers.
DORA register data checked for completeness before supervisors find the gaps.
USA
PL
UA
MX
TRA common grouping is ownership and decision rights, data quality, metadata and lineage, security and access, and regulatory compliance. DAMA-DMBOK and the EDM Association’s DCAM describe each area in more depth and give you a way to score progress.
Most enterprises combine a data catalog, a business glossary, lineage capture, quality checks, and access policy tooling, often through platform-native options such as Microsoft Purview or Databricks Unity Catalog. A tool creates no governance until owners have the decision rights and the time to act on what it shows.
An assessment and one governed domain come first, and a rollout that spans several domains takes considerably longer because adoption, not tooling, sets the pace. Timing for each phase is fixed in the statement of work after the assessment.
Cost depends on the number of domains, the state of your catalog, lineage depth, regulatory scope, and whether operations continue after launch. Engagements start with a fixed-fee assessment that scopes and prices each following phase, whether it runs through team extension, managed delivery, or a nearshore and offshore delivery center.
No, the work runs on platforms you already license, on open-source tools such as OpenLineage, or on a mix of both. Native features in Databricks, Snowflake, or Microsoft Purview often cover more of the catalog than teams expect, which keeps license spend for gaps that matter.
We review idle compute, node sizing, and repeated processing, then match capacity and loading patterns to the workload. Incremental loads, partition pruning, and job-scoped compute can reduce unnecessary processing as data volumes grow.
Data governance decides who owns data, what it means, and who may use it, while data quality sets correctness targets and data observability detects when data breaks in production. Governance sets the rules, and the other two measure and enforce them on live data every day.
The best fit depends on whether you need a framework, an implementation team, or ongoing operations. Ask any firm to name the engineers who will do the work, show a sample deliverable, and state in writing what your team owns when the engagement ends.
A data governance consulting firm assesses maturity, designs the operating model and decision rights, implements the catalog and quality controls, and prepares evidence for regulators. Implementation-focused data governance consulting firms such as Zoolatech also leave those controls running in your systems, with your team trained to operate them.
Look for firms that can implement lineage from source systems to risk reports, because supervisors still single out lineage as a weak point years after the original compliance deadline. Zoolatech builds that lineage, and the ownership behind it, on the platforms banks already run, so evidence comes from production jobs rather than manual spreadsheets.
