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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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Empowering data mesh: The tools to deliver BI excellence

erwin

In this blog, we’ll delve into the critical role of governance and data modeling tools in supporting a seamless data mesh implementation and explore how erwin tools can be used in that role. erwin also provides data governance, metadata management and data lineage software called erwin Data Intelligence by Quest.

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How Your Finance Team Can Lead Your Enterprise Data Transformation

Alation

Building a Data Culture Within a Finance Department. Our finance users tell us that their first exposure to the Alation Data Catalog often comes soon after the launch of organization-wide data transformation efforts. After all, finance is one of the greatest consumers of data within a business.

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

CIO Business Intelligence

Harmonizing these data sets with centralized enterprise data faces increasing challenges as shifts to data definitions, schema, and architecture require constant central data team efforts. These domain data leaders often cite the diminishing returns and significant effort of central data team engagement.

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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. And there’s control of that landscape to facilitate insight and collaboration and limit risk.

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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

Reporting being part of an effective DQM, we will also go through some data quality metrics examples you can use to assess your efforts in the matter. But first, let’s define what data quality actually is. What is the definition of data quality? 2 – Data profiling. date, month, and year).

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How to modernize data lakes with a data lakehouse architecture

IBM Big Data Hub

This was, without a question, a significant departure from traditional analytic environments, which often meant vendor-lock in and the inability to work with data at scale. Another unexpected challenge was the introduction of Spark as a processing framework for big data. Comprehensive data security and data governance (i.e.