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US20170024280A1 (en) 2017-01-26 US9760439B2 true US9760439B2 (en) 2017-09-12 Family ID=48695944 Family Applications (6) Application Number Title Priority Date Filing Date US13/727,581 Active 2032-08-29 US8914706B2 (en) 2011-12-30 2012-12-26 Using parity data for concurrent data authentication, correction, compression, and encryption US14/543,641 Active US9258014B2 (en) 2011-12-30 2014-11-17 Using parity data for concurrent data authentication, correction, compression, and encryption US15/018,782 Active US9760439B2 (en) 2011-12-30 2016-02-08 Using parity data for concurrent data authentication, correction, compression, and encryption US15/701,111 Active US10268544B2 (en) 2011-12-30 2017-09-11 Using parity data for concurrent data authentication, correction, compression, and encryption US16/277,869 Active US10664347B2 (en) 2011-12-30 2019-02-15 Using parity data for concurrent data authentication, correction, compression, and encryption US16/855,901 Active US11500723B2 (en) 2011-12-30 2020-04-22 Using parity data for concurrent data authentication, correction, compression, and encryption Family Applications Before (2) Application Number Title Priority Date Filing Date US13/727,581 Active 2032-08-29 US8914706B2 (en) 2011-12-30 2012-12-26 Using parity data for concurrent data authentication, correction, compression, and encryption US14/543,641 Active US9258014B2 (en) 2011-12-30 2014-11-17 Using parity data for concurrent data authentication, correction, compression, and encryption Family Applications After (3) Application Number Title Priority Date Filing Date US15/701,111 Active US10268544B2 (en) 2011-12-30 2017-09-11 Using parity data for concurrent data authentication, correction, compression, and encryption US16/277,869 Active US10664347B2 (en) 2011-12-30 2019-02-15 Using parity data for concurrent data authentication, correction, compression, and encryption US16/855,901 Active US11500723B2 (en) 2011-12-30 2020-04-22 Using parity data for concurrent data authentication, correction, compression, and encryption Country Status (1) Country Link US (6) US8914706B2 (en) Cited By (5) * Cited by examiner, † Cited by

To understand and use the data. Examples of passive metadata include data descriptions, tags, and annotations.In data governance, both active and passive metadata are important for ensuring data quality, managing data effectively, and providing meaningful insights to stakeholders. While active metadata is essential for maintaining the integrity of data within a system, passive metadata is necessary for making that data accessible and understandable to humans.Selecting the “right” platformData environments are complex and rely on multiple tools and platforms. A platform that generates active metadata is even more valuable if it can use the metadata to enhance and interact with other connected systems in a metadata exchange for platform-to-platform orchestration. BigID is a representative vendor in Gartner’s inaugural market guide for Active metadata management, citing that “Active metadata management is an emerging set of capabilities across multiple data management markets resulting from continuous metadata management innovation.” Gartner recommends that data management platforms use active metadata for interoperability with third-party systems.Three ways that Active Metadata Management Platforms will use metadata to interoperate are to:Export the active metadata to be used as insight or to create action in another connected tool or platform.Import “foreign” metadata to build further insight and optimize data strategies.Active metadata generated by a platform is used to prompt action or a workflow in a connected tool or platform.Enhance Active Metadata Management with BigIDActive metadata is foundational to a modern data governance practice. Savvy data teams know that metadata is essential to describe and manage data, and active metadata is the next evolution of metadata for data governance. Creating metadata that is actionable, and storing the metadata in a way that enables action, makes metadata even more powerful to benefit from emerging data governance capabilities.BigID Data Intelligence Platform applies ML to analyze data at scale and create active metadata. The platform generates metadata to add context including classifiers, attributes, and policies that are used to take action on data. Some actions, like applying policy information, are taken automatically. Other actions, like collaboration and approvals on glossary terms, trigger a workflow because they require human interaction. The active metadata created by BigID will maximize data value and minimize data risk for any organization wanting to gain context and add automation to manage a data environment.Sign-up below to see how BigID can enhance and increase the value of data with active metadata management. Get a 1:1 demo with our metadata management experts.

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According to Gartner, the future of active metadata is promising, as organizations increasingly rely on data to drive business outcomes. They predict that by 2025, 60% of organizations will actively incorporate metadata management into their overall data management initiatives, up from just 10% in 2017.Active metadata is becoming increasingly important in the modern data landscape, and is expected to play a critical role in enabling organizations to extract maximum value from their data assets.What is active metadata?What happens when you apply machine learning (ML) to metadata so that it can be used to make decisions and trigger actions? Applying ML to metadata transforms metadata into ‘active metadata’, meaning that the metadata is actionable.Metadata is the data that describes data. For a reminder of metadata types and descriptions, see Metadata Management 101: Know Your Data. Active metadata is ML-augmented metadata that can be used to take action or make decisions based on the metadata.Active metadata needs to be insightful to be useful for action and needs to be stored and made available in a way that enables operational use. Metadata management platforms apply ML to create insight about the metadata and take action on it. The action can occur by triggering a workflow or a platform may take some action automatically.Why is active metadata important?Active metadata management is crucial for effective data governance. Without it, data management processes can become inefficient and ineffective, leading to poor decision-making, increased risk, and wasted resources. Here are some reasons why active metadata management is important for data governance:Comprehensive and Up-to-Date View of Data: Active metadata management provides a comprehensive and up-to-date view of data, including its lineage, quality, and context. This can help organizations understand the origin and history of their data, and ensure that it is being used appropriately and in compliance with regulatory requirements.Real-Time Monitoring of Data Quality: Active metadata management enables real-time monitoring of data quality, using metrics such as completeness, accuracy, and consistency. This can help organizations identify and resolve data quality issues before they have a negative impact on business operations or decision-making.Enforcement of Data Governance Policies and Standards: Active metadata management can be used to enforce data governance policies and standards, such as access controls, data retention, and data classification. This can help organizations ensure that data is being used in compliance with regulatory requirements, and that sensitive data is being protected from unauthorized access or misuse.Improved Analytics and Decision-Making: Active metadata management can enhance analytics and decision-making by providing additional context and insights into the data. This can help organizations identify patterns, trends, and correlations in their data, and make more informed decisions based on that information.By continuously managing metadata, organizations can ensure that their data is accurate, reliable, and secure. This can improve decision-making, reduce risk, and increase the efficiency of active metadata management processes.Who uses it?Business analysts make business decisions by analyzing the physical data.Physical data ML analytic insight: Customer sales data shows that the demand for a product is increasing.Resulting business action: The company