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The What & Why of Data Governance

erwin

Modern data governance is a strategic, ongoing and collaborative practice that enables organizations to discover and track their data, understand what it means within a business context, and maximize its security, quality and value. The What: Data Governance Defined. Data governance has no standard definition.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes. No, its ultimate goal is to increase return on investment (ROI) for those business segments that depend upon data.

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8 data strategy mistakes to avoid

CIO Business Intelligence

Undervaluing unstructured data Much of the data organizations accumulate is unstructured, whether it’s text, video, audio, social media, images, or other formats. These information resources can hold enormous value for enterprises , enabling them to gain new insights about customers and market trends.

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Top 6 Benefits of Automating End-to-End Data Lineage

erwin

Replace manual and recurring tasks for fast, reliable data lineage and overall data governance. It’s paramount that organizations understand the benefits of automating end-to-end data lineage. The importance of end-to-end data lineage is widely understood and ignoring it is risky business.

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How to modernize data lakes with a data lakehouse architecture

IBM Big Data Hub

In the case of Hadoop, one of the more popular data lakes, the promise of implementing such a repository using open-source software and having it all run on commodity hardware meant you could store a lot of data on these systems at a very low cost. But it never co-existed amicably within existing data lake environments.

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How smava makes loans transparent and affordable using Amazon Redshift Serverless

AWS Big Data

smava believes in and takes advantage of data-driven decisions in order to become the market leader. The Data Platform team is responsible for supporting data-driven decisions at smava by providing data products across all departments and branches of the company.

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Tackling AI’s data challenges with IBM databases on AWS

IBM Big Data Hub

This involves unifying and sharing a single copy of data and metadata across IBM® watsonx.data ™, IBM® Db2 ®, IBM® Db2® Warehouse and IBM® Netezza ®, using native integrations and supporting open formats, all without the need for migration or recataloging.