Transforming Data Quality for a Leading Streaming Platform

Transforming Data Quality for a Leading Streaming Platform

Customer Overview

A major media and entertainment industry player, this customer had an ever-expanding user base and a growing data ecosystem. Their platform provided on-demand streaming services to millions of users worldwide, leading to massive data inflows from diverse sources, including content management systems, user behavior analytics, and third-party integrations. As the company scaled, their data infrastructure needed to catch up, impacting operational efficiency and decision-making.

Challenges

  1. Overwhelming Amount of Incidents: The customer was facing a barrage of data quality incidents across multiple data sources daily, with no structured way to tackle or prioritize these issues.
  2. Latency in Notifications: Notifications were delayed, meaning data teams would often respond to issues long after they had already caused operational slowdowns or misinformed decisions.
  3. Alert Fatigue: The sheer volume of alerts overwhelmed the data teams, leading to fatigue and making it difficult to differentiate critical issues from less important ones.
  4. Multiple Data Sources: Inconsistent data flows from different sources resulted in mismatched data, which hindered analysis and reporting.
  5. Root Cause Analysis: Identifying the root causes of data issues proved manual and time-consuming, often resulting in prolonged downtime and misaligned reports.

Konstellation’s Solution

Konstellation deployed its one-stop shop of data quality tools to address these challenges, focusing on streamlining the customer’s incident management and resolution processes. The key solutions included:

  1. Standardization Across Data Sources: Konstellation first implemented its robust standardization engine, harmonizing data across all sources. By applying out-of-the-box quality checks, the system automatically detected anomalies, ensuring consistency across datasets without manual intervention.
  2. Table Reliability Score & Criticality Score: A custom-built scoring mechanism was introduced to evaluate the real-time reliability of data tables based on historical performance and detected anomalies. Tables were assigned a Table Reliability Score that provided an at-a-glance health check. At the same time, a Criticality Score highlights the importance of each incident based on its role in the business process, allowing the customer to prioritize high-value data.
  3. Anomaly Detection & Clustering Incidents: Using advanced anomaly detection, Konstellation automated the identification of outliers in real-time. The system then clustered these anomalies into meaningful incidents, significantly reducing the number of individual alerts and making it easier for the customer’s data team to manage. This reduced alert fatigue while increasing efficiency.
  4. Root Cause Identification & Prioritization: Konstellation’s solution offered deep-rooted impact analysis, grouping incidents by shared root causes. Instead of treating each alert as a separate issue, the system automatically analyzed the interrelations between data issues. By identifying the underlying causes, the platform prioritized incidents based on their potential business impact, ensuring that the most critical issues were addressed first.
  5. Automated, Out-of-the-Box Quality Checks: With Konstellation’s standard quality checks in place, the customer no longer needed to develop custom validation scripts for each data pipeline. The automated checks ran seamlessly and continuously, ensuring that data quality was monitored without the need for constant human oversight.

Results

  • 60% Reduction in Incidents: By clustering related anomalies into singular, actionable incidents, the total volume of alerts was reduced by 60%, freeing up the data team’s time and resources.
  • 75% Faster Response Times: Real-time alerting addressed notification latency, allowing teams to act on issues as they arose. The speed of root cause identification and incident resolution improved by 75%. The team used to take an average of 8 days to resolve an incident plus 14 days of backfill, which has now been reduced to 2 days due to quick identification.
  • Improved Decision-Making: With clean, reliable data, the customer could confidently report on user behavior and content performance, which informed strategic decisions.
  • Enhanced Team Productivity: By mitigating alert fatigue and prioritizing incidents based on their impact, the data team worked more efficiently and focused on value-added activities rather than manual troubleshooting. Working on the right thing during the planned time without being in a fire-fighter mode brought the data engineering team piece of mind. And as important as it is, reliability and productivity build trust in the data engineering team across the company.

Conclusion

Konstellation transformed the customer’s approach to data quality management, providing a scalable, automated, and prioritized incident management system. By implementing reliable quality checks, standardization, and insightful anomaly detection, Konstellation empowered customers to unlock their data's full potential, enhancing operational efficiency and decision-making.