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Quality and formatting may differ with more autonomous domain teams producing data assets, making interoperability difficult and dataquality guarantees elusive. Data discoverability and reusability. Data products must be properly designed and organized to be reused across the organization.
DataOps sprung up to connect data sources to data consumers. The data warehouse and analytical data stores moved to the cloud and disaggregated into the data mesh. So we have to be very careful about giving the domains the right and authority to fix dataquality. Tools became stacks.
Enterprise data analytics integrates data, business, and analytics disciplines, including: Data management. Data engineering. DataOps. … In the past, businesses would collect data, run analytics, and extract insights, which would inform strategy and decision-making. Evaluate and monitor dataquality.
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