Data you can trust

Data Pipelines & Governance

Data pipelines move data from your systems to where it is used, cleaning and checking it along the way. Meanify builds data pipelines and sets up data management and governance (quality checks, lineage, access control and retention rules), so your data is reliable for reporting and AI and compliant with the GDPR.

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Who it's for

  • Companies whose reports don't match between departments
  • Organisations preparing their data for AI projects
  • Regulated sectors that need traceability and access control
  • Teams maintaining fragile scripts and spreadsheets

Problems we solve

  • Data spread across many systems and spreadsheets
  • Nobody trusts the numbers
  • Pipelines that break without anyone noticing
  • No clear rules on who can access which data, or how long it is kept

What's included

  • Data pipelines

    Batch and streaming ingestion, transformation and loading.

  • Data quality checks

    Automated tests that catch errors before they reach your reports.

  • Data modelling

    Warehouse or lakehouse models designed for analytics and AI.

  • Governance

    Catalogue, lineage, ownership, access control and retention policies.

  • GDPR and EU AI Act support

    Documentation and controls for personal data and AI use.

How it works

  1. Audit

    We review your data sources, flows and pain points.

    You confirm priorities and owners.

  2. Design

    We design the target architecture and the governance rules.

    You approve the architecture and rules.

  3. Build

    We build pipelines with tests and monitoring from day one.

    Every pipeline change is reviewed.

  4. Hand over

    We document everything and train your team.

    Your team signs off the handover.

Technologies we use

  • SQL and Python
  • dbt
  • Apache Airflow
  • Apache Spark
  • PostgreSQL
  • Cloud data warehouses
  • Data catalogue and lineage tools

Frequently asked questions

What is data governance?

Data governance is the set of rules and responsibilities that decide who owns each dataset, who can access it, how its quality is checked and how long it is kept. It makes data trustworthy and helps meet legal requirements such as the GDPR.

Do we need clean data before starting an AI project?

You need data that is good enough for the task. We often start with a short data audit, fix the issues that matter for the project and add quality checks so problems are caught early.

Can you work with our existing data platform?

Yes. We build on the databases, warehouses and cloud services you already use whenever possible.

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