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The Symbiotic Relationship Between Data Governance and AI

David Menninger's Analyst Perspectives

Data governance has always been a critical part of the data and analytics landscape. However, for many years, it was seen as a preventive function to limit access to data and ensure compliance with security and data privacy requirements. Data governance is integral to an overall data intelligence strategy.

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5 Ways Data Modeling Is Critical to Data Governance

erwin

Then there’s unstructured data with no contextual framework to govern data flows across the enterprise not to mention time-consuming manual data preparation and limited views of data lineage. Today’s data modeling is not your father’s data modeling software.

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A Data Governance Self-Assessment Test

TDAN

The purpose of this article is to provide a model to conduct a self-assessment of your organization’s data environment when preparing to build your Data Governance program. Take the […].

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Domo Addresses Data Products and Agentic AI

David Menninger's Analyst Perspectives

Additionally, as I recently explained , the companys platform addresses a broad range of capabilities that includes data governance and security, data integration and application development, as well as the automation and incorporation of artificial intelligence (AI) and machine learning (ML) models into BI and analytics.

Metrics 130
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The DataOps Vendor Landscape, 2021

DataKitchen

Testing and Data Observability. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Genie — Distributed big data orchestration service by Netflix.

Testing 304
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The future of data: A 5-pillar approach to modern data management

CIO Business Intelligence

Digital transformation started creating a digital presence of everything we do in our lives, and artificial intelligence (AI) and machine learning (ML) advancements in the past decade dramatically altered the data landscape. Historically, this pillar was part of analytics and reporting, and it remains so in many cases.

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Specialized tools for machine learning development and model governance are becoming essential

O'Reilly on Data

Model packaging: companies are using MLflow to incorporate custom logic and dependencies as part of a model’s package abstraction before deploying it to their production environment (example: a recommendation system might be programmed to not display certain images to minors). Model governance.