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Dataventra

Optimisation, Migration & Quality

Data quality management

Bad data is cheapest to fix before it reaches a report. We put rules, checks and reconciliation in the pipeline itself, and make data-quality results visible to the people who own the data.

Discuss your environment
SQL ServerPower BI

When teams bring us in

Signs you need Data Quality support

  • Business users regularly find errors before the data team does
  • Totals in reports don't match the source system
  • Duplicate or missing records appear after loads
  • There is no agreed owner for fixing bad data

What's included

Our Data Quality work

  • 01

    Quality rules

    Completeness, validity, uniqueness and consistency checks defined with the business.

  • 02

    Pipeline checks

    Checks run as part of each load, with clear pass, warn and fail outcomes.

  • 03

    Source reconciliation

    Automated comparison of warehouse totals against source systems.

  • 04

    Reporting & ownership

    Data-quality dashboards and routing so issues reach the team that can fix them.

FAQ

Data Quality — common questions

Where should data-quality checks run?
As close to the load as possible — in staging and before data is published to reporting — so problems are caught before anyone relies on them.
Do you need a separate data-quality tool?
Not necessarily. Many checks can be implemented in SQL Server alongside your pipelines, with results reported in Power BI.
Who decides what 'good' data looks like?
The business owners of the data. We help define measurable rules with them and implement those rules technically.

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Talk to us about Data Quality.

Tell us what you're running and what's getting in the way. We'll suggest a sensible first step.

Start a conversationjil.gajera@dataventra.in