A solution to complex data challenges

Traditional manual processes are no longer sufficient to manage the vast amounts of data and the rapid pace of change. This is where machine learning (ML) can play a transformative role. By leveraging ML, organizations can automate and enhance their data observability practices, ensuring reliable data quality, regulatory compliance and improved decision-making.

However, it’s important to take a structured approach when implementing machine learning to tackle data observability challenges.

Here are the six key steps to ensure your team’s success:

Ready to move forward on your journey to data reliability? In the pages to follow, we’ll expand on each of these steps, identify common obstacles and provide four questions you can ask to clear your path to data reliability.

1

Profile the data: Discover and classify your data sources, focusing on sensitive data types

2

Define data policies and business rules: Implement continuous testing and validation mechanisms to maintain data integrity

3

Detect anomalies: Use machine learning to monitor data and identify deviations from normal behavior

4

Monitor for impact: Correlate anomalies with changes and events to understand their impact

5

Notify key experts: Alert relevant stakeholders to initiate remediation processes

6

Optimize continuously: Evolve policies, rules and reports to enhance data quality goals