Data quality techniques, best practices and research

Data quality techniques and best practices News

June 10, 2021 10 Jun'21
Quest updates Erwin Data Modeling and Data Intelligence

Quest is looking to build out what it calls a data empowerment platform with the help of data modeling and other technology from its recently acquired Erwin business unit.
June 10, 2021 10 Jun'21
Prefect raises $32M for dataflow automation technology

Data startup Prefect is looking to grow its open source project and commercial business to help organizations manage automated data pipeline requirements.
June 03, 2021 03 Jun'21
Cribl aims to ease data observability with LogStream update

LogStream 3.0 brings new configuration capabilities to Cribl's pipeline technology that can help organizations optimize log and metrics data.
February 03, 2021 03 Feb'21
Collibra acquires predictive data quality vendor OwlDQ

With OwlDQ, Collibra is bringing data quality capabilities to its data intelligence platform to help users better understand and evaluate the impact of different sources of data.

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How to choose exactly the right data story for your audience

A data practitioner has two jobs: tell the right data story and in the right way to win over project stakeholders, data expert Larry Burns says in his latest book. Continue Reading
data scrubbing (data cleansing)

Data scrubbing, also referred to as data cleansing, is the process of amending or removing data in a database that is incorrect, incomplete, improperly formatted or duplicated. Continue Reading
raw data (source data or atomic data)

Raw data (sometimes called source data, atomic data or primary data) is data that has not been processed for use. Continue Reading

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What is data architecture? A data management blueprint

Data architecture is a discipline that documents an organization's data assets, maps how data flows through its systems and provides a blueprint for managing data. Continue Reading
How data governance and data quality work together

High-quality, reliable data is essential to the data governance process. Here are strategies to ensure data quality standards are ingrained in governance processes. Continue Reading
Data quality for big data: Why it's a must and how to improve it

As data volumes increase exponentially, methods to improve and ensure big data quality are critical in making accurate, effective and trusted business decisions. Continue Reading

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EY CTO outlines data governance challenges

Multinational professional services firm EY has taken a strategic view of how to manage and use data in a federated approach powered by a trusted data fabric. Continue Reading
The enterprise advantages of automated data collection

Many organizations still rely on manual data entry that wastes time and results in low-quality data. Here are the latest automated data collection techniques and their benefits. Continue Reading
How automated metadata management improves business insights

Automating metadata management can cut down time spent on tasks such as data tagging and cataloging. Explore how automated metadata management is improving data quality. Continue Reading

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Data governance and COVID-19 data security challenges

Maintaining data governance and data security best practices is essential now more than ever. But the increase in working from home can put a strain on those practices. Continue Reading
Common data lake challenges and how to overcome them

Managing the data contained in your enterprise data lake presents many challenges. From the amount of data to data inconsistencies, here are some solutions to common issues. Continue Reading
What data lake governance challenges do organizations face?

Consultant Anne Marie Smith details five challenges that an organization may face in applying data governance policies to data lakes and offers advice on how to overcome them. Continue Reading