Pickl AI

JULY 24, 2024

Summary: Data quality is a fundamental aspect of Machine Learning. Poor-quality data leads to biased and unreliable models, while high-quality data enables accurate predictions and insights. What is Data Quality in Machine Learning? What is Data Quality in Machine Learning?

Data Quality 52

Data Quality Machine Learning Machine Learning Clean Data 52

Precisely

MAY 16, 2024

To achieve trustworthy AI outcomes, you need to ground your approach in three crucial considerations related to data’s completeness, trustworthiness, and context. You need to break down data silos and integrate critical data from all relevant sources into Amazon Web Services (AWS).

AI 69

AI AI AWS Data Quality 69

Precisely

FEBRUARY 2, 2023

This requires access to data from across business systems when they need it. Data silos and slow batch delivery of data will not do. Stale data and inconsistencies can distort the perception of what is really happening in the business leading to uncertainty and delay.

Data Silos 52

Data Silos Data Pipeline Database Data Observability 52

Precisely

JULY 18, 2024

Ensures consistent, high-quality data is readily available to foster innovation and enable you to drive competitive advantage in your markets through advanced analytics and machine learning. You must be able to continuously catalog, profile, and identify the most frequently used data. Increase metadata maturity.

Data Lakes 52

Data Lakes Data Warehouse Data Governance Machine Learning 52

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 69

Data Quality DataOps Data Silos Artificial Intelligence 69

Precisely

OCTOBER 12, 2023

This includes understanding the impact of change within one data element on the various other data elements and compliance requirements throughout the organization. Creating data observability routines to inform key users of any changes or exceptions that crop up within the data, enabling a more proactive approach to compliance.

Data Quality 64

Data Quality Data Governance ML ML 64