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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

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 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.

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6 DataOps Best Practices to Increase Your Data Analytics Output AND Your Data Quality

Octopai

DataOps is an approach to best practices for data management that increases the quantity of data analytics products a data team can develop and deploy in a given time while drastically improving the level of data quality. Automated workflows for data product creation, testing and deployment.

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Erwin Data Intelligence: A Data Partner’s Perspective

erwin

While the essence of success in data governance is people and not technology, having the right tools at your fingertips is crucial. Technology is an enabler, and for data governance this is essentially having an excellent metadata management tool. Next to data governance, data architecture is really embedded in our DNA.

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Case study: Policy Enforcement Automation With Semantics

Ontotext

These models are as important to companies as their frontline products and determine how data is managed, consumed, combined, joined, and analyzed. Data-centric approach In the data-centric approach, metadata serves as a layer of interoperability between the data sources.

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Five benefits of a data catalog

IBM Big Data Hub

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

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The BI Data Management Toolset: What You Shouldn’t Be Without

Octopai

(We’ve had clients who started using our automated data lineage solution, and suddenly everyone in their organization was “trusting their data and believing in their data.”). The visibility provided by data lineage also enables you to streamline your data asset management by breaking down data silos and eliminating redundancies.

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From Data Silos to Data Fabric with Knowledge Graphs

Ontotext

The Data Fabric paradigm combines design principles and methodologies for building efficient, flexible and reliable data management ecosystems. Knowledge Graphs are the Warp and Weft of a Data Fabric. To implement any Data Fabric approach, it is essential to be able to understand the context of data.