DataKitchen

NOVEMBER 29, 2021

In 2022, data organizations will institute robust automated processes around their AI systems to make them more accountable to stakeholders. Model developers will test for AI bias as part of their pre-deployment testing. Continuous testing, monitoring and observability will prevent biased models from deploying or continuing to operate.

Testing 245

Testing Data Lake Data Architecture Manufacturing 245

Cloudera

OCTOBER 19, 2023

While navigating so many simultaneous data-dependent transformations, they must balance the need to level up their data management practices—accelerating the rate at which they ingest, manage, prepare, and analyze data—with that of governing this data.

Data-driven 75

Data-driven Data Architecture Predictive Modeling Digital Transformation 75

CIO Business Intelligence

NOVEMBER 14, 2022

While many organizations still struggle to get started, the most innovative organizations are using modern analytics to improve business outcomes, deliver personalized experiences, monetize data as an asset, and prepare for the unexpected. Being locked into a data architecture that can’t evolve isn’t acceptable.”

Analytics 102

Analytics Machine Learning Testing Data Strategy 102

AWS Big Data

NOVEMBER 29, 2023

Since then, customer demands for better scale, higher throughput, and agility in handling a wide variety of changing, but increasingly business critical analytics and machine learning use cases has exploded, and we have been keeping pace.

Data Warehouse 103

Data Warehouse Data Lake Analytics Machine Learning 103

Ontotext

AUGUST 4, 2023

A data fabric utilizes an integrated data layer over existing, discoverable, and inferenced metadata assets to support the design, deployment, and utilization of data across enterprises, including hybrid and multi-cloud platforms. This aids in data discovery and exploration as it identifies patterns across all types of metadata.

Metadata 52

Metadata Data-driven Data Architecture Data Quality 52

AWS Big Data

MARCH 25, 2024

Foundation models (FMs) are large machine learning (ML) models trained on a broad spectrum of unlabeled and generalized datasets. This scale and general-purpose adaptability are what makes FMs different from traditional ML models. However, the value of such important data diminishes significantly over time.

Data Lake 92

Data Lake Unstructured Data Management Modeling 92