Alation

SEPTEMBER 28, 2022

What exactly is DataOps ? The term has been used a lot more of late, especially in the data analytics industry, as we’ve seen it expand over the past few years to keep pace with new regulations, like the GDPR and CCPA. In essence, DataOps is a practice that helps organizations manage and govern data more effectively.

DataOps 52

DataOps Data Pipeline Data Quality Analytics 52

Alation

JUNE 21, 2022

The audience grew to include data scientists (who were even more scarce and expensive) and their supporting resources (e.g., ML and DataOps teams). After that came data governance , privacy, and compliance staff. Power business users and other non-purely-analytic data citizens came after that.

DataOps 52

DataOps Data Scientist Data Quality Data Pipeline 52

IBM Journey to AI blog

SEPTEMBER 5, 2024

Access to high-quality data can help organizations start successful products, defend against digital attacks, understand failures and pivot toward success. Emerging technologies and trends, such as machine learning (ML), artificial intelligence (AI), automation and generative AI (gen AI), all rely on good data quality.

Data Governance 75

Data Governance Data Quality DataOps Artificial Intelligence 75

Precisely

DECEMBER 12, 2023

For some time now, data observabilit y has been an important factor in software engineering, but its application within the realm of data stewardship is a relatively new phenomenon. Data observability is a foundational element of data operations (DataOps). Data observability helps you manage data quality at scale.

Data Observability 52

Data Observability Data Quality Data Pipeline Machine Learning 52

AWS Machine Learning Blog

JULY 31, 2023

It also enables you to evaluate the models using advanced metrics as if you were a data scientist. We explain the metrics and show techniques to deal with data to obtain better model performance. For a column impact of 25%, Canvas weighs the prediction as 25% for the column and 75% for the other columns.

ML 90

ML ML Data Preparation Machine Learning 90

Precisely

JULY 15, 2024

Data domain teams have a better understanding of the data and their unique use cases, making them better positioned to enhance the value of their data and make it available for data teams. With this approach, demands on each team are more manageable, and analysts can quickly get the data they need.

Data Governance 72

Data Governance DataOps Data Silos Data Pipeline 72