erwin

DECEMBER 20, 2018

The driving factors behind data governance adoption vary. Whether implemented as preventative measures (risk management and regulation) or proactive endeavors (value creation and ROI), the benefits of a data governance initiative is becoming more apparent. Defining Data Governance. to Data Governance 2.0

Data Governance 40

Data Governance ROI Risk Management Metadata 40

erwin

MARCH 4, 2021

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.

Data Governance 122

Data Governance Digital Transformation Data-driven Cost-Benefit 122

Cloudera

AUGUST 4, 2021

And if data security tops IT concerns, data governance should be their second priority. Not only is it critical to protect data, but data governance is also the foundation for data-driven businesses and maximizing value from data analytics. But it’s still not easy.

Data Governance 86

Data Governance Metadata Data-driven Enterprise 86

O'Reilly on Data

MARCH 18, 2020

The new survey, which ran for a few weeks in December 2019, generated an enthusiastic 1,388 responses. This year, about 15% of respondent organizations are not doing anything with AI, down ~20% from our 2019 survey. It seems as if the experimental AI projects of 2019 have borne fruit. Respondent demographics. But what kind?

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311

Sisense

JULY 6, 2020

In today’s day and age, data can be found just about anywhere. The volume and types of data are growing by the day, making data processing and generation of data insights more and more complicated. Inaccurate data leads to generating unreliable insights which, in the long run, lead the business in the wrong direction.

Enterprise 98

Enterprise Management Data Architecture Strategy 98

Alation

FEBRUARY 8, 2022

In contrast to this common, centralized approach, a data mesh architecture calls for responsibilities to be distributed to the people closest to the data. Middlemen — data engineering or IT teams — can’t possibly possess all the expertise needed to serve up quality data to the growing range of data consumers who need it.

Data Governance 52

Data Governance Data-driven Metadata Enterprise 52