Getting prepared for data reliability

You can improve the reliability of your data with an effective data quality solution. To get started, you’ll want to ask yourself and your colleagues a few fundamental questions.

Question 1

Are data quality and reliability “events” understood when they occur? How commonplace are they?

Understanding the frequency and nature of data quality issues is crucial, especially as AI investments expand and regulatory landscapes evolve. This insight helps determine the effectiveness of your current data management practices and highlights areas needing attention.

Question 2

What data quality dimensions or metrics show success for you?

Identify the key metrics that reflect data quality in your organization. This could include accuracy, completeness, consistency, timeliness and validity. Knowing these will help you set benchmarks and track improvements over time, ensuring your data meets the standards required for AI and compliance purposes.

View our recent Masterclass on the best practices for ensuring reliable data in the AI era

Question 3

Who across your organization must have visibility on the reliability of data?

It’s essential to identify the stakeholders who need access to data quality insights. This could include data engineers, analysts, business leaders and compliance officers. Ensuring that the right people have visibility into data reliability helps in making informed decisions and maintaining accountability, which is increasingly important in a regulated environment.

Question 4

Do those involved with business and rule validation have technical backgrounds?

Understanding the technical expertise of your team members involved in data governance is crucial. This will help in designing training programs and support systems to bridge any knowledge gaps and ensure effective rule validation, which is vital as AI integration becomes more complex.

Question 5

How manual is the process of generating data quality rules today?

Assess the current level of automation in your data quality management processes. Manual processes can be error-prone and time-consuming. Identifying opportunities for automation can enhance efficiency and accuracy, which is essential for keeping pace with the rapid changes in data volume and regulatory requirements.

These questions will guide you in evaluating your current data quality and observability readiness. And, they’ll help you pinpoint where you need focus and improvements, setting a solid foundation for robust data governance and AI implementation.

Why Collibra Data Quality

Monitor data quality issues across many sources with automated and targeted rule writing
Remediate faster with no-code, business ready DQ dimensions and self-service rules for accurate and consistent data products
Visualize data health and certify data for trusted business decisions