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Within the modern active metadata management stack, metadata management serves an important role. By helping data and analytics teams to better understand the context and quality of data, it helps in the combining of data silos. In turn, this increases confidence in the data and the ensuing decision-making. Manual approaches to metadata management are less than ideal and can lead to missed opportunities as data quantities continue to increase. Consider a situation in which a new data asset becomes available but is kept from your data consumers due to incorrect or insufficient tagging. How can you provide real-time data governance for consistent data while keeping up with increasing data volumes and consumer demand?
To keep up with the growth of enterprise data, it is essential to adapt metadata management strategies. This clarifies the role of active metadata management. Active metadata management, according to Gartner, entails a group of abilities that allow constant access to and processing of metadata.
Active metadata management automates metadata processing using machine learning, and then uses the outcomes of that analysis to inform decisions through suggestions, warnings, and other means. In other words, an active metadata system improves the actionability of data in the present. It has some features that enable automated data discovery, boost data confidence, and enable scaleable data security and governance.
According to research, most organizations don’t analyze up to 68% of their data. Improving data consumption requires an understanding of the data assets that are available across the organization. AI-driven recommendation engines that examine active metadata and suggest new assets to data users based on their usage habits can enable advanced data discovery.
A challenge to becoming data-driven that organizations must overcome is poor data quality. The majority of data quality management strategies are reactive, only being activated when users express issues with data teams about the accuracy of datasets. Proactive data quality management can improve from active metadata management. Data observability capabilities support solid data by improving it and identifying anomalies in data pipelines, enabling IT teams to quickly identify and fix issues before they have an impact on the company.
Legal consequences, reputation loss, and loss of customer trust are too great risks to take on. More than 80% of businesses globally will have to deal with at least one privacy-focused data protection policy by 2023, says the Gartner Hype Cycle for Data Privacy 2021. Instead of addressing each issue individually, organizations may increase customer trust by taking a proactive approach to data privacy, protection, and risk management. Organizations may better comply with new data standards by enforcing data policies automatically and putting data protection principles into action at scale with active metadata management.
A data fabric solution connects the appropriate data to the appropriate people at the appropriate time and from any location as needed. The IBM Watson Knowledge Catalog for Cloud Pak for Data’s active metadata capabilities are one of the key features of the IBM data fabric system. This data catalog gives data creators and consumers the tools they need to confidently use data throughout its lifecycle while analyzing, trusting, and protecting it.
For advanced data discovery and increased trust in data, it is important to make sure that the data is enriched with all the relevant context. Watson Knowledge Catalog provides a solid metadata foundation made up of business keywords, data classifications, and reference data, supported by AI/ML-driven automation, to assist data consumers in finding and understanding data. Users are given the power to identify important assets from throughout the company at scale with the help of intelligent recommendations from IBM Watson and peers. Additionally, Watson Knowledge Catalog’s automated metadata enrichment uses machine learning to automatically assign business keywords to data assets at scale. This makes it easier for customers to find material quickly, decide whether it is acceptable and trustworthy, and decide how to use it.
Data teams must spend a lot of time managing data spread across distributed data environments and providing trusted data due to complex data landscapes and the consequent data silos. Watson Knowledge Catalog does data quality analysis to award quality scores to data assets based on factors such as data class and type violations, duplicate values, missing values, and suspect values to increase the trust in data. Then, specific custom data quality rules can be created to enhance curation processes. The partnership between IBM and MANTA also adds automatic data lineage capabilities, allowing you to track and examine how data is used and consumed across all of your applications and data sources. By actively using past trends and statistics to detect data anomalies in data pipelines, this complements IBM’s acquisition of Databand.ai and its data observability solutions to enable trustworthy data so that IT teams can quickly identify problems before they have an impact on the business.
IBM provides amazing data privacy management tools for the global, dynamic application of your data protection policies. Make data protection rules to restrict access to data assets regardless of where they are located, mask data at the column level, and filter data rows according to row attributes. Through the de-identification of private and confidential information, IBM can aid in the protection of sensitive and important data.
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