Metadata management performs a critical role within the modern data management stack. It helps blur data silos, and empowers data and analytics teams to better understand the context and quality of data. This, in turn, builds trust in data and the decision-making to follow. However, as data volumes continue to grow, manual approaches to metadata management are sub-optimal and can result in missed opportunities. Suppose that a new data asset becomes available but remains hidden from your data consumers because of improper or inadequate tagging. How do you keep pace with growing data volumes and increased demand from data consumers and deliver real-time data governance for trusted outcomes?
It is imperative to evolve metadata management approaches to keep pace with the proliferation of enterprise data. This puts into perspective the role of active metadata management. According to Gartner, active metadata management includes a set of capabilities that enable continuous access and processing of metadata.
What is Active Metadata management?
Active metadata management uses Machine Learning to automate metadata processing and use the outcomes of that metadata analysis to help drive decisions through recommendations, alerts and more. In short, active metadata management makes data more actionable in real-time. It includes a set of capabilities that facilitate automated data discovery, improve confidence in data, and enable data protection and data governance at scale.
Common use cases for active metadata management
Improve data discovery
Research shows that up to 68% of data is not analyzed in most organizations. Knowing what data assets are available across the enterprise is key to improving data utilization. You can enable advanced data discovery with AI-driven recommendation engines that analyze active metadata and recommend new assets to data consumers based on their usage patterns.
Provide early indicators of data quality
Poor data quality is a barrier faced by organizations aspiring to be data-driven. Most data quality management approaches are reactive, triggered only when consumers complain to data teams about the integrity of datasets. Active metadata management can help with proactive data quality management. Data observability capabilities help augment trustworthy data and detect anomalies in data pipelines, allowing IT teams to quickly surface and resolve issues before they impact the business.
Regulatory and compliance
The risks of non-compliance – legal penalties, loss of reputation and customer trust – are too big to be ignored. According to the Gartner Hype Cycle for Data Privacy 2021, more than 80% of companies worldwide will face at least one privacy-focused data protection regulation by 2023. Rather than responding to each challenge individually, a proactive approach to data privacy, protection and risk management is an opportunity for organizations to build customer trust. With active metadata management, organizations can enforce data policies automatically and implement data protection rules at scale for better compliance with new data regulations.
3 benefits of an active metadata management solution
A data fabric solution connects the right data, at the right time, to the right people, from anywhere it’s needed. One of the key aspects of the IBM data fabric solution is the active metadata capabilities delivered by IBM Watson Knowledge Catalog for Cloud Pak for Data. This data catalog empowers data producers and consumers to understand, trust and protect data, and to use it confidently throughout its lifecycle.
Know your data
Ensuring that data is enriched with all the relevant context is critical for advanced data discovery and improved trust in data. Watson Knowledge Catalog helps data consumers find and understand data by offering a strong metadata foundation consisting of business terms, data classifications, and reference data backed by AI/ML-driven automation. With intelligent recommendations from IBM Watson and peers, users are empowered to find relevant assets from across the enterprise at scale. Furthermore, automated metadata enrichment built into Watson Knowledge Catalog uses machine learning to automatically assign business terms to data assets at scale. This helps users find data faster, decide if data is appropriate and can be trusted and how to work with data.
Trust your data
Complex data landscapes and resulting data silos place a time-consuming burden on data teams to govern data spread across distributed data environments and deliver trusted data. To improve trust in data, Watson Knowledge Catalog performs data quality analysis to assign quality scores to data assets based on dimensions like data class and type violations, duplicate values, missing values, and suspect values. Custom data quality rules can then be defined to improve curation activities. Furthermore, IBM’s partnership with MANTA brings automated data lineage capabilities to trace and analyze how data is moved and consumed across all your applications and data sources. This complements IBM’s acquisition of Databand.ai and its data observability solutions to facilitate trustworthy data by actively using historical trends and statistics to detect data anomalies in data pipelines so that IT teams can quickly surface issues before they impact the business.
Protect your data
IBM supports advanced data privacy management capabilities for dynamic enforcement of your data protection policies globally. Create data protection rules to help control access to data assets no matter where they reside, mask data at the column level and filter data rows based on row attributes. IBM can help protect sensitive and critical data through de-identification of personal information and confidential information.
Want to try out the active metadata features that allow IBM to deliver integrated quality and governance capabilities? Check out the free trial.
Access the report to read why IBM is recognized as a Leader in the 2022 Gartner® Magic Quadrant™ for Data Quality Solutions.
Source: ibm.com
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