Alation

JUNE 21, 2022

Over time, we called the “thing” a data catalog , blending the Google-style, AI/ML-based relevancy with more Yahoo-style manual curation and wikis. Thus was born the data catalog. In our early days, “people” largely meant data analysts and business analysts. ML and DataOps teams). data pipelines) to support.

DataOps 52

DataOps Data Scientist Data Quality Data Pipeline 52

Precisely

AUGUST 3, 2023

They reported facing challenges to the success of their data programs — including cost (50%), lack of effective data management tools (45%), poor data literacy/program adoption (41%), and skills shortages (36%) as well as poor data quality (36%).

DataOps 98

DataOps Data Quality Analytics Analytics 98

Precisely

FEBRUARY 10, 2023

Advanced analytics and AI/ML continue to be hot data trends in 2023. According to a recent IDC study, “executives openly articulate the need for their organizations to be more data-driven, to be ‘data companies,’ and to increase their enterprise intelligence.”

DataOps 52

DataOps Data Observability ML ML 52

Precisely

FEBRUARY 9, 2024

When it comes to AI outputs, results will only be as strong as the data that’s feeding them. Trusting your data is the cornerstone of successful AI and ML (machine learning) initiatives, and data integrity is the key that unlocks the fullest potential. That approach assumes that good data quality will be self-sustaining.

Data Quality 69

Data Quality DataOps Data Silos Artificial Intelligence 69

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

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. Additional key topics Advanced metrics are not the only important tools available to you for evaluating and improving ML model performance.

ML 98

ML ML Data Preparation Machine Learning 98

Precisely

SEPTEMBER 18, 2023

Trusted data is crucial, and data observability makes it possible. Data observability is a key element of data operations (DataOps). The best data observability tools incorporate artificial intelligence (AI) to identify and prioritize potential issues. Why is data observability so important?

Data Observability 52

Data Observability Data Quality Data Pipeline DataOps 52