Precisely

JUNE 26, 2023

To name just a few of those trends and challenges: The COVID-19 pandemic disrupted consumer buying behavior, leading to a surge of 43% in e-commerce sales during 2020. This digital boom raised the stakes for cyber security and data privacy – and it’s easy to see why.

Data Quality 52

Data Quality Data Silos Analytics Analytics 52

AWS Machine Learning Blog

JANUARY 13, 2023

Analyzing real-world healthcare and life sciences (HCLS) data poses several practical challenges, such as distributed data silos, lack of sufficient data at any single site for rare events, regulatory guidelines that prohibit data sharing, infrastructure requirement, and cost incurred in creating a centralized data repository.

AWS 99

AWS Analytics Analytics Machine Learning 99

Alation

MAY 2, 2023

Further, companies in the hospitality industry collect and analyze personally identifiable information (PII) that require additional security and privacy protections, like the General Data Protection Regulation (GDPR). The platform analyzes data from a variety of sources, including customer preferences, travel patterns, and local events.

Analytics 40

Analytics Analytics Data Silos Data Governance 40

Alation

OCTOBER 12, 2021

In enterprises especially, which typically collect vast amounts of data, analysts often struggle to find, understand, and trust data for analytics reporting. Immense volume leads to data silos, and a holistic view of the business becomes more difficult to achieve. Why Do You Need an Enterprise Analytics Strategy?

Analytics 52

Analytics Analytics Data Governance DataOps 52

Alation

JULY 6, 2021

Deeper knowledge of how data is used powers deeper understanding of the data itself. Siloed Data. Silos exist in every enterprise, and they never fail to cause data governance challenges. Silos arise for a range of reasons. Why Do Data Silos Happen? The fast pace of data collection.

Data Governance 52

Data Governance Data Quality Data Silos Data Profiling 52

AWS Machine Learning Blog

JANUARY 13, 2023

Analyzing real-world healthcare and life sciences (HCLS) data poses several practical challenges, such as distributed data silos, lack of sufficient data at a single site for rare events, regulatory guidelines that prohibit data sharing, infrastructure requirement, and cost incurred in creating a centralized data repository.

AWS 96

AWS Analytics Analytics Machine Learning 96

Mlearning.ai

SEPTEMBER 4, 2023

The reason for using a mixture of domain-specific and public data is a model that excels in financial assessments while also performing well on standard benchmarks. 2020) and Chinchilla scaling laws with prior large language model and BloombergGPT parameter and data sizes. Figure 2: Jared Kaplan et al. 32] Alex Wang, et al.

AI 52

AI AI Azure Machine Learning 52