Remove Analytics Remove Big Data Remove Metadata Remove Structured Data
article thumbnail

The Missing Link in Enterprise Data Governance: Metadata

Octopai

In order to figure out why the numbers in the two reports didn’t match, Steve needed to understand everything about the data that made up those reports – when the report was created, who created it, any changes made to it, which system it was created in, etc. Enterprise data governance. Metadata in data governance.

article thumbnail

Three Emerging Analytics Products Derived from Value-driven Data Innovation and Insights Discovery in the Enterprise

Rocket-Powered Data Science

I recently saw an informal online survey that asked users which types of data (tabular, text, images, or “other”) are being used in their organization’s analytics applications. The results showed that (among those surveyed) approximately 90% of enterprise analytics applications are being built on tabular data.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

What is a data scientist? A key data analytics role and a lucrative career

CIO Business Intelligence

What is a data scientist? Data scientists are analytical data experts who use data science to discover insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. Semi-structured data falls between the two.

article thumbnail

Data governance in the age of generative AI

AWS Big Data

First, many LLM use cases rely on enterprise knowledge that needs to be drawn from unstructured data such as documents, transcripts, and images, in addition to structured data from data warehouses. Data enrichment In addition, additional metadata may need to be extracted from the objects.

article thumbnail

AI recommendations for descriptions in Amazon DataZone for enhanced business data cataloging and discovery is now generally available

AWS Big Data

Amazon DataZone enables you to discover, access, share, and govern data at scale across organizational boundaries, reducing the undifferentiated heavy lifting of making data and analytics tools accessible to everyone in the organization. Amazon DataZone uses the available metadata on the asset to generate the descriptions.

article thumbnail

Gain insights from historical location data using Amazon Location Service and AWS analytics services

AWS Big Data

Data analytics – Business analysts gather operational insights from multiple data sources, including the location data collected from the vehicles. Athena is used to run geospatial queries on the location data stored in the S3 buckets. For Data source , choose AwsDataCatalog. Choose Run.

article thumbnail

How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

This post provides guidance on how to build scalable analytical solutions for gaming industry use cases using Amazon Redshift Serverless. Flexible and easy to use – The solutions should provide less restrictive, easy-to-access, and ready-to-use data. A data warehouse is one of the components in a data hub.