Data Governance Data Quality Data Transformation Modeling 
datapine
SEPTEMBER 29, 2022
1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data.
IBM Big Data Hub
FEBRUARY 9, 2023
In our last blog , we delved into the seven most prevalent data challenges that can be addressed with effective data governance. Today we will share our approach to developing a data governance program to drive data transformation and fuel a data-driven culture.
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CIO Business Intelligence
JANUARY 24, 2024
They also need to establish clear privacy, regulatory compliance, and data governance policies. Many industries and regions have strict regulations governing data privacy and security,” Miller says. This type of environment can also be deeply rewarding for data and analytics professionals.”
erwin
JANUARY 11, 2019
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. So questions linger about whether transformed data can be trusted.
CIO Business Intelligence
FEBRUARY 16, 2023
“All they would have to do is just build their model and run with it,” he says. But to augment its various businesses with ML and AI, Iyengar’s team first had to break down data silos within the organization and transform the company’s data operations. For now, it operates under a centralized “hub and spokes” model.
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. Where is it?
AWS Big Data
APRIL 19, 2023
Background The success of a data-driven organization recognizes data as a key enabler to increase and sustain innovation. The goal of a data product is to solve the long-standing issue of data silos and data quality. It follows what is called a distributed system architecture.
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