Dataversity

NOVEMBER 15, 2021

DataOps is something that has been building up at the edges of enterprise data strategies for a couple of years now, steadily gaining followers and creeping up the agenda of data professionals. The number of data requests from the business keeps growing […].

DataOps 110

DataOps Analytics Analytics Data Quality 110

Alation

FEBRUARY 3, 2022

The goal of DataOps is to create predictable delivery and change management of data and all data-related artifacts. DataOps practices help organizations overcome challenges caused by fragmented teams and processes and delays in delivering data in consumable forms. So how does data governance relate to DataOps?

Data Governance 98

Data Governance DataOps Data Quality Machine Learning 98

Alation

JUNE 21, 2022

The product concept back then went something like: In a world where enterprises have numerous sources of data, let’s make a thing that helps people find the best data asset to answer their question based on what other users were using. And to determine “best,” we’d ingest log files and leverage machine learning.

DataOps 52

DataOps Data Scientist Data Quality Data Pipeline 52

Precisely

FEBRUARY 10, 2023

Read Here are the top data trends our experts see for 2023 and beyond. DataOps Delivers Continuous Improvement and Value In IDC’s spotlight report, Improving Data Integrity and Trust through Transparency and Enrichment , Research Director Stewart Bond highlights the advent of DataOps as a distinct discipline.

DataOps 52

DataOps Data Observability ML ML 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

FEBRUARY 9, 2024

In 2024 organizations will increasingly turn to third-party data and spatial insights to augment their training and reference data for the most nuanced, coherent, and contextually relevant AI output. When it comes to AI outputs, results will only be as strong as the data that’s feeding them.

Data Quality 68

Data Quality DataOps Data Silos Artificial Intelligence 68

AWS Machine Learning Blog

JULY 31, 2023

Although machine learning (ML) can provide valuable insights, ML experts were needed to build customer churn prediction models until the introduction of Amazon SageMaker Canvas. Refer to Predict customer churn with no-code machine learning using Amazon SageMaker Canvas for a full description.

ML 98

ML ML Data Preparation Machine Learning 98