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Data Governance Maturity and Tracking Progress

erwin

Data governance is best defined as the strategic, ongoing and collaborative processes involved in managing data’s access, availability, usability, quality and security in line with established internal policies and relevant data regulations. Data Governance Is Business Transformation. Predictability.

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Using Enterprise Architecture, Data Modeling & Data Governance for Rapid Crisis Response

erwin

It provides free access to videos, webinars, courseware, simulations, frameworks and expert strategic advice leveraging the erwin EDGE platform for rapid response transformation during the COVID-19 crisis. Here are a few examples specific to enterprise architecture and business process modeling, data modeling and data governance.

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The Key Role of the Data Governance Partner

TDAN

A question was raised in a recent webinar about the role of the Data Architect and Data Modelers in a Data Governance program. My webinar with Dataversity was focused on Data Governance Roles as the Backbone of Your Program.

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5 Data Governance Mistakes to Avoid

Alation

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Using bad data, or the incorrect data can generate devastating results. 5 common data governance mistakes 1.

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Top 10 Metadata Management Influencers, Sites, and Blogs You Must Follow in 2021

Octopai

Aptly named, metadata management is the process in which BI and Analytics teams manage metadata, which is the data that describes other data. In other words, data is the context and metadata is the content. Without metadata, BI teams are unable to understand the data’s full story.

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5 Data Governance Mistakes to Avoid

Alation

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Using bad data, or the incorrect data can generate devastating results. 5 common data governance mistakes 1.

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From Chaos to Control with Data Intelligence

erwin

The answer is radical transformation, made possible by an intelligent, data-driven approach to: New business models. One Customer’s Journey to Controlling Data Chaos. Being able to integrate all data touchpoints, including erwin DM for data modeling, Denodo for data visualization, and Jira for ticketing, has been key.

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