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. Research VP, Business Analytics and Data Science.

Analytics 40

Analytics Business Intelligence Metadata Statistics 40

O'Reilly on Data

FEBRUARY 11, 2020

They don’t have the resources they need to clean up data quality problems. The building blocks of data governance are often lacking within organizations. These include the basics, such as metadata creation and management, data provenance, data lineage, and other essentials. An additional 7% are data engineers.

Data Quality 304

Data Quality Metadata Data Governance Publishing 304

IBM Big Data Hub

JANUARY 17, 2023

These new technologies and approaches, along with the desire to reduce data duplication and complex ETL pipelines, have resulted in a new architectural data platform approach known as the data lakehouse – offering the flexibility of a data lake with the performance and structure of a data warehouse.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

CIO Business Intelligence

MARCH 9, 2022

As a result, users can easily find what they need, and organizations avoid the operational and cost burdens of storing unneeded or duplicate data copies. Newer data lakes are highly scalable and can ingest structured and semi-structured data along with unstructured data like text, images, video, and audio.

Data Lake 119

Data Lake Unstructured Data Data Warehouse Data Quality 119

Domino Data Lab

JULY 2, 2019

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. BTW, videos for Rev2 are up: [link]. On deck this time ’round the Moon: program synthesis.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Domino Data Lab

JANUARY 21, 2021

This blog explores the challenges associated with doing such work manually, discusses the benefits of using Pandas Profiling software to automate and standardize the process, and touches on the limitations of such tools in their ability to completely subsume the core tasks required of data science professionals and statistical researchers.

Statistics 106

Statistics Unstructured Data Data Science Predictive Modeling 106

CIO Business Intelligence

JUNE 14, 2023

Additionally, it is vital to be able to execute computing operations on the 1000+ PB within a multi-parallel processing distributed system, considering that the data remains dynamic, constantly undergoing updates, deletions, movements, and growth.

Unstructured Data 64

Unstructured Data IT Manufacturing Visualization 64

AWS Big Data

FEBRUARY 21, 2023

What is a data vault? The data vault approach is a method and architectural framework for providing a business with data analytics services to support business intelligence, data warehousing, analytics, and data science needs.

Data Warehouse 67

Data Warehouse Data Lake Cost-Benefit Modeling 67

Alation

DECEMBER 15, 2021

A data governance strategy helps prevent your organization from having “bad data” — and the poor decisions that may result! Here’s why organizations need a governance strategy: Makes data available: So people can easily find and use both structured and unstructured data. Choose a Metadata Storage Option.

Data Governance 52

Data Governance Strategy Metadata Data Strategy 52