CIO Business Intelligence

APRIL 30, 2024

Together the technologies aim to help business users and “novice” data analysts explore their data and gain insights without having to resort to data experts. This is really empowering everyone to be a data expert,” Maxon said. “It Shared Dimensions and Composable Data Sources.

Analytics 68

Analytics Metrics Visualization Dashboards 68

DataKitchen

FEBRUARY 2, 2023

Central IT Data Teams focus on standards, compliance, and cost reduction. ’ They are data enabling vs. value delivery. Their software purchase behavior will align with enabling standards for line-of-business data teams who use various tools that act on data. Recession: the party is over.

Metadata 130

Metadata Testing Risk Data Science 130

AWS Big Data

SEPTEMBER 29, 2023

In this post, we show how to process XML data using AWS Glue and Athena. This approach provides a user-friendly interface and is particularly suitable for individuals who prefer a graphical approach to managing their data. We use the AWS Glue crawler to extract XML file metadata. xml and technique2.xml. Choose Create.

Metadata 65

Metadata Visualization Data-driven Optimization 65

AWS Big Data

NOVEMBER 29, 2023

These announcements drive forward the AWS Zero-ETL vision to unify all your data, enabling you to better maximize the value of your data with comprehensive analytics and ML capabilities, and innovate faster with secure data collaboration within and across organizations.

Data Warehouse 103

Data Warehouse Data Lake Analytics Machine Learning 103

CIO Business Intelligence

MARCH 6, 2023

Advancements in analytics and AI as well as support for unstructured data in centralized data lakes are key benefits of doing business in the cloud, and Shutterstock is capitalizing on its cloud foundation, creating new revenue streams and business models using the cloud and data lakes as key components of its innovation platform.

Data Lake 59

Data Lake Cost-Benefit Recreation/Entertainment Experimentation 59

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. FMs are multimodal; they work with different data types such as text, video, audio, and images.

Data Lake 92

Data Lake Unstructured Data Management Modeling 92

Alation

FEBRUARY 20, 2020

After investing in self-service analytic tooling, organizations are now turning their attention to linking infrastructure and tooling to data-driven decisions. The Forrester Wave : Machine Learning Data Catalogs, Q2 2018. Another telltale sign of category creation is that it comes with a distinctive business model and profit model.” [7].

Machine Learning 52

Machine Learning Marketing Reporting ROI 52