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In addition to file servers and network drives, unstructured data comes from almost anywhere including logs, social media and real-time feeds.
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In order to move historical data into a centralized archive repository, we must first be able to connect with source data no matter where it resides.
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Moreover, both technical and business users will have easy self-service access to the data, without having to download and store individual copies of data.Įstablish a connector framework with source data systems A consistent policy-driven framework is applied to all enterprise data including file servers. Cloud data management enables enterprise archives and data lakes to connect with any data source and build metadata catalogues. The common data repository approach creates the opportunity to streamline data ingestion, create metadata catalogues, enforce data governance rules and control data access. By capturing both active and historical data across all data types (structured, semistructured and unstructured) in a single repository, cloud data management establishes centralized controls for all enterprise data. W3C architecture and standards enable anyone, anywhere, to process and share information with anyone else.Įstablishes a common data repository with cloud data managementĭata silos are difficult to control. Digital transformation requires real-time, event-driven data collection, and the W3C “One Web” vision defines an ideal architecture for any data to run on any W3C-compliant cloud.
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Modernize your data infrastructure with cloud native architectureĬloud-native software is an ideal platform for multi-cloud deployments because it is rooted in open-source standards established by the World Wide Web Consortium (W3C), an international community that develops open standards for the web. Furthermore, centralized cloud data management improves administrative control and mitigates the risk of consumer data privacy regulations like GDPR and CCPA. Through data retention policies, classification rules and compliance reporting, file archiving uses ILM to establish a robust compliance solution for unstructured enterprise data. File archiving uses Information Lifecycle management (ILM) to establish a data governance framework for compliance. Large scale unstructured data growth spreading fast across an organization also creates major compliance and consumer data privacy concerns.
By collecting all historical files and storing them in a central repository, data-driven organizations are properly staged for machine learning, artificial intelligence and advanced analytics. File archiving improves the performance of production file systems by moving older, inactive data to nearline, archive storage. Just like a database, file servers and network drives bog down from too much data. Researchers estimate that unstructured data is not only the biggest part of the data growth problem, it is growing at 55-65% per year.ĭata loses value fast soon after it is created, and data older than 18 months is rarely ever accessed. The rest is made up of unstructured files siloed on servers and network drives across the organization. But researchers estimate that only 20 percent of enterprise data resides in a database. Many organizations have successfully deployed database archiving to improve enterprise application performance and establish data compliance. With so much data available, the opportunity to mine the data for business value has never been greater, so it is really no surprise that organizations are embracing cloud data management to manage data growth. You may be surprised to learn How Much Data Is Created Every Day? But at the same time we are facing a data growth crisis so big that it is measured in zettabytes.
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Today we live in a data-driven world where every enterprise is focused on how to leverage their data better to improve business results.
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