Six Steps to Ensure Data Reliability in the Age of AI

The promise of AI hinges on one critical factor: data quality. Yet, a staggering statistic remains: over half of data professionals still distrust their data. This lack of trust translates directly to flawed strategies, wasted resources, and regulatory risks. Moreover, as AI rapidly transforms industries, relying on outdated manual data quality processes is a recipe for failure.

Collibra, an established partner in SAP’s open data ecosystem, recommends these six steps for organizations to ensure data reliability.

Step 1: Profile the Data

Organizations should lay the foundation for reliable data by discovering and classifying all their data sources, mainly focusing on identifying sensitive data types. This helps businesses build a robust data governance and compliance structure while enhancing data quality within SAP processes. It also improves reporting and analytics, mitigates risks, and reduces costs.

Step 2: Define Data Policies and Business Rules

According to Wavestone’s Data and AI Leadership Executive Survey, 37% of data leaders say their efforts to improve data quality have been successful. This was achieved by defining data policies and business rules that guide organizations in handling different data types. By taking this step, organizations ensure uniform data handling, reduce errors, enforce data integrity, and gain reliable data insights.

Step 3: Detect Anomalies

Collibra recommends that organizations establish a baseline for normal behavior within their data. They can use machine learning (ML) algorithms to monitor data continuously and detect deviations from this baseline. This is a powerful way of enhancing data quality as it establishes a baseline of standard data through historical data analysis and identifying key data attributes.

Step 4: Monitor for Impact

In this step, organizations should correlate anomalies with unintended changes and other events to identify the root cause and assess their potential impact. Anomaly detection algorithms can help identify unusual patterns or data points outside the expected range. At the same time, ML models can continuously monitor incoming data and compare it to the established baseline to strengthen data monitoring.

Step 5: Notify Key Experts

If an anomaly is detected, provide contextual alerts to relevant stakeholders, including data engineers, analysts, business leaders, and compliance officers, to initiate data remediation. Setting up automated alerts and notifications helps here, as the system can automatically notify the relevant stakeholders when an anomaly is detected, allowing for prompt investigation and resolution.

Step 6: Optimize Continuously

Monitoring data and detecting anomalies isn’t only about fixing immediate errors but continuously improving data, especially the one that fuels AI. An organization must evolve its data policies, rules, and reports from the insights it gains based on monitoring and anomaly detection to always ensure reliable and high-quality data.

In February 2023, SAP selected Collibra as its governance partner to help organizations tackle the challenges of ensuring reliable AI data. Under the partnership, Collibra and SAP prioritize unified and comprehensive governance for data and AI with integrated data quality, AI Governance, and end-to-end lineage capabilities for SAP and non-SAP data.

What This Means for SAPinsiders

Commit to continued innovation with governance for data and AI. Unifying data governance, data quality, and AI governance together drives reliable data products and AI, allowing teams to innovate confidently. For example, combining customer profile and product usage data creates insights into customer segment-product use, enabling AI to deliver targeted product recommendations.

Realize the power of the open data ecosystem. By collaborating across SAP, Databricks, and the broader data ecosystem, organizations that use the combined SAP and Collibra offerings can deliver flexibility while accelerating value creation. Moreover, by utilizing the integrated platform, SAP users gain complete visibility of data to deliver trusted data products to the right users across the organization.

Find the right partner to help implement data quality solutions. Collibra’s partnership with SAP Datasphere is evolving to address organizations’ complex data governance needs directly. Moreover, Collibra is collaborating to integrate SAP Business Data Cloud into their joint roadmap. This will allow SAP users access seamless, unified governance solutions designed to work harmoniously with the SAP environment.