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The Conceptual Data Model’s Unwavering Role in Data Governance 

In this contributed article, editorial consultant Jelani Harper highlights how in an age in which data governance has become all but synonymous with data privacy and data protection, there are numerous aspects of data management that are regarded much differently than how they traditionally were. Data modeling, for example, is frequently considered a dimension of data engineering or data science.

The Metamorphosis of Data Governance: What it Means Today

In this contributed article, editorial consultant Jelani Harper highlights how despite changing definitions, a more profound—and deceptively subtle—transformation has almost silently wrought data governance into something markedly different than what it was as little as even five years ago.

Monitaur Launches GovernML to Guide and Assure Entire AI Life Cycle 

Monitaur, an AI governance software company, announced the general availability of GovernML, the latest addition to its ML Assurance platform, designed for enterprises committed to responsible AI. Offered as a web-based, SaaS application, GovernML enables enterprises to establish and maintain a system of record of model governance policies, ethical practices, and model risk across their entire AI portfolio. 

How Digital Governance is Evolving – And Why Leaders Should Care

In this contributed article, James Farhat, CEO of ACTS. discusses how digital governance is evolving – and why leaders should care.
“Digital governance” is a relatively new term. To understand it, it’s helpful to recap what we mean by “digital transformation” – which is essentially leveraging technology to improve processes and the way people work.

anch.AI, former AI Sustainability Center, Secures $2.1M in Seed Funding to Launch Ethical AI Governance Platform

Against the rising tide of regulation, anch.AI has released the first horizontally integrated ethical AI governance platform, a one-stop shop for businesses to accelerate responsible AI adoption across their organization. The B2B SaaS startup emerged from the AI Sustainability Center, a Swedish think tank, and has secured $2.1M in seed funding to further develop and launch their pioneering risk assessment platform.

2022 Trends in Data Governance: Operational Capabilities

In this contributed article, editorial consultant Jelani Harper discusses how for organizations everywhere—regardless of industry, size, or area of focus—data governance has reached a critical inflection point. It has long been established as a vital area of risk management predicated on achieving regulatory compliance, maintaining data privacy, and ensuring ongoing sustainability of data as an enterprise asset.

How Governing Observability Data is Critical to ESG Success

In this contributed article, Nick Heudecker, Senior Director of Market Strategy at Cribl, discusses how observability data comprises the logs, events, metrics, and traces that make things like security, performance management, and monitoring possible. While often overlooked, governing these data sources is critical in today’s enterprises. The current state of observability data management is, at best, fragmented and ad hoc. By adopting an observability pipeline as a key component in your observability infrastructure, you can centralize your governance efforts while remaining agile in the face of constant change.

One Giant Leap Forward for Data Governance

In this contributed article, editorial consultant Jelani Harper discusses how the predominance of cloud computing, the advent of edge computing, the prevalence of the Internet of Things, and the pervasiveness of cognitive computing have had a profound impact on the data sphere—and on data governance.

How ML Powers Data Access Governance with Immuta & Databricks

If data isn’t accessible for real-time analytics, is it still valuable? Immuta’s native Databricks integration avoids this dilemma by using ML to streamline data access governance, and deliver analytics-ready data quickly and securely. For Databricks users leveraging Immuta, ML drives sensitive data discovery, dynamic access control, and consistent policy enforcement.