Data Quality Audit

Defining what “good” data means is not a trivial exercise. The lack of a shared understanding across the business can result in costly data quality failures and create real friction between teams that produce, manage and consume data. Since the definition is different for different people, neither the most expensive enterprise tools nor the most sophisticated ML-driven approaches can solve the problem.

• What does “good” data mean to different stakeholders?
• Can you trust the information in your reports and on your dashboards?
• Which quality checks should you automate to prevent the most costly errors?

Get the answers you need now with Ultranauts. We’ll do the heavy lifting, quickly.

By working closely with various data stakeholders across an organization and tying each data element to critical business use cases, we’ll help you create a shared, objective, value-driven understanding of “good”. In addition to creating these Data Stories, we’ll validate Data Dictionaries, execute Deep Data Profiling, conduct a Governance Assessment, develop a Test Plan for Automation and establish a Maintainability Plan with internal data stakeholders.

Our cognitively diverse quality engineering teams, 75% of whom are autistic,
combine deep technical skills with an aptitude for rapidly absorbing complex domain
knowledge, so we can hit the ground running.