It’s no secret that reliable and trusted data is the lifeblood of business. Yet organizations consistently say they struggle with data quality. Only 37% of data and AI executives said they have been able to improve data quality.
Fragmented governance, quality and observability
Part of the problem is less than a third of organizations use a single, unified platform for governance, quality and observability. The majority, instead, have a data governance tool to define data quality standards, policies, business rules, metrics, regulatory requirements, and related stewardship processes and ownership. The management of technical data quality rules is handled in a completely different tool, often relying on a proprietary scripting language, or spreadsheets are used to track a patchwork of SQL and Python scripts. Finally, a third, separate tool monitors data for compliance with rules and metrics, identifying anomalies, causes, and impacts, and triggering alerts and notifications when problems arise.
Excessive technical workloads
The use of fragmented governance, quality and observability tools increases administration and troubleshooting complexity, and creates redundant and manual workloads that drive up costs.
For instance:
- Managing data connections, permissions and users separately in each tool
- Manually stitching together technical and business lineage information across tools
- Manually integrating task queues, workflows and notifications between tools
And custom integrations between the tools are often fragile and frequently break when the tools’ versions or APIs change.
Limited business visibility
Fragmented governance, quality, and observability tools make it difficult to cross reference causes and impacts with policy violations at every stage in the data flow so you can prioritize response based on business severity.
A few examples include
- Manually mapping data quality scores to data catalog assets and governance policies
- Manually correlating alerts to identify causes and impacts
- Manually identifying the people accountable for issue management and assigning them tasks
The lack of end to end visibility of data quality delays issue resolution, reduces business agility and increases business risk.
Collibra unifies data quality and observability with data and AI governance
You’ve done the hard part of cataloging your data and creating your data quality policies. Now take the next step and create the technical rules to monitor and enforce your policies. And map your data quality scores to your catalog assets and policies so you have visibility of both data quality and policy compliance.
The Collibra Platform is the industry’s only solution that unifies data quality and observability with data and AI governance.
- Increase data quality visibility: By automating profiling, classification and monitoring, as well as mapping of data quality scores to catalog assets and governance policies to increase consumer trust in data
- Reduce rule management workloads: By automating creation of technical data quality rules from governance policies using GenAI, and deployment of rules across data sources to increase steward productivity
- Accelerate identification of quality and policy issues: By automating identification of quality issues and policy violations, as well as issue causes and impacts to speed prioritization based on business impact
- Ensure proactive issue notification and management: By automating notification of stakeholders, task assignments, and workflows based on governance ownership and processes to prevent data issues from becoming business issues
The business benefits of unifying data quality and observability with data and AI governance
Whether you’re trying to use AI and analytics to better manage revenue, cost or risk, poor quality data impairs your ability to make decisions and take action with confidence. Research shows that organizations that use unified tools, processes, and operating models across data and AI governance outperform their competitors.
- Revenue management: Unified approaches to data governance and quality increase your ability to capitalize on new revenue opportunities and address challenges with revenue management. Organizations that take advantage of a unified platform on average realize a 21% increase in revenue growth
- Cost management: Unified approaches to data governance and quality increase your ability to capitalize on new opportunities to reduce expenditures and address challenges with cost management. Organizations that take advantage of a unified platform on average realize a 20% reduction in costs
- Risk management: Unified approaches to data governance and quality increase your ability to proactively identify and manage financial, operational and regulatory risk. Organizations that take advantage of a unified platform on average realize a 21% improvement in regulatory compliance
Summary
Data quality as an organizational practice requires data governance, quality, and observability capabilities to work together seamlessly. The Collibra Platform platform eliminates the need to manually integrate fragmented tools, which increases productivity in managing data quality and creates greater confidence in the use of data. Greater confidence in data and decisions in turn increases the business value delivered by AI and analytics.
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