Rita Sallam

JULY 22, 2016

By contrast, traditional BI platforms are designed to support modular development of IT-produced analytic content, specialized tools and skills, and significant upfront data modeling, coupled with a predefined metadata layer, is required to access their analytic capabilities. Enjoy your summer!! Regards, Rita Sallam.

Analytics 40

Analytics Business Intelligence Metadata Statistics 40

Domino Data Lab

JULY 22, 2019

By virtue of that, if you take those log files of customers interactions, you aggregate them, then you take that aggregated data, run machine learning models on them, you can produce data products that you feed back into your web apps, and then you get this kind of effect in business. That was the origin of big data.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

CIO Business Intelligence

MARCH 9, 2022

But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure.

Data Lake 115

Data Lake Unstructured Data Data Warehouse Data Quality 115

AWS Big Data

NOVEMBER 29, 2023

Generative SQL uses AI to analyze user intent, query patterns, and schema metadata to identify common SQL query patterns directly allowing you to get insights faster in a conversational format without extensive knowledge of your organization’s complex database metadata.

Data Warehouse 104

Data Warehouse Data Lake Analytics Machine Learning 104

AWS Big Data

OCTOBER 11, 2023

AWS Glue is a serverless data integration service that makes it easier to discover, prepare, move, and integrate data from multiple sources for analytics, ML, and application development. Then you can use Amazon Athena V3 to query the tables in the Data Catalog.

Visualization 100

Visualization Dashboards Data-driven Gap analysis 100

IBM Big Data Hub

AUGUST 4, 2023

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. It uses knowledge graphs, semantics and AI/ML technology to discover patterns in various types of metadata.

Data Architecture 62

Data Architecture Data Lake Machine Learning Data Governance 62

Sisense

MAY 18, 2020

In Data-Powered Businesses , we dive into the ways that companies of all kinds are digitally transforming to make smarter data-driven decisions, monetize their data, and create companies that will thrive in our current era of Big Data. AM: How is data collected to make these recommendations?

Analytics 98

Analytics Digital Transformation Data-driven Sales 98