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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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A step-by-step guide to setting up a data governance program

IBM Big Data Hub

In our last blog , we delved into the seven most prevalent data challenges that can be addressed with effective data governance. Today we will share our approach to developing a data governance program to drive data transformation and fuel a data-driven culture.

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SAP Datasphere Powers Business at the Speed of Data

Rocket-Powered Data Science

Datasphere accesses and integrates both SAP and non-SAP data sources into end-users’ data flows, including on-prem data warehouses, cloud data warehouses and lakehouses, relational databases, virtual data products, in-memory data, and applications that generate data (such as external API data loads).

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Self-service vs Centralized Data Management: How to Leverage Data Lineage to Empower and Control

Octopai

In the era of big data, organizations are grappling with the challenge of effectively managing and leveraging vast amounts of data. Two prominent approaches have emerged: self-service data management and centralized data management.

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8 data strategy mistakes to avoid

CIO Business Intelligence

“Organizations often get services and applications up and running without having put stewardship in place,” says Marc Johnson, CISO and senior advisor at Impact Advisors, a healthcare management consulting firm. They also need to establish clear privacy, regulatory compliance, and data governance policies.

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As insurers look to be more agile, data mesh strategies take centerstage

CIO Business Intelligence

With its emphasis on decentralized, domain-oriented data ownership and architecture, data mesh provides a potential answer for overmatched, out-manned businesses. Despite these benefits, the core problems that data centralization so often fails to address are the pragmatic realities of many enterprise data ecosystems.

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Data Preparation and Data Mapping: The Glue Between Data Management and Data Governance to Accelerate Insights and Reduce Risks

erwin

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. So questions linger about whether transformed data can be trusted.