Data Engineering Podcast

JUNE 29, 2020

This was a great conversation about the complexities of working in a niche domain of data analysis and how to build a pipeline of high quality data from collection to analysis. The team at Audio Analytic are working to impart a sense of hearing to our myriad devices with their sound recognition technology.

Data Collection 100

Data Collection Management High Quality Data Metadata 100

Databand.ai

MAY 30, 2023

Data quality refers to the degree of accuracy, consistency, completeness, reliability, and relevance of the data collected, stored, and used within an organization or a specific context. High-quality data is essential for making well-informed decisions, performing accurate analyses, and developing effective strategies.

Data Cleanse 85

Data Cleanse Datasets Data Governance Data Validation 85

Monte Carlo

JANUARY 10, 2024

Extrinsic data, meanwhile, is more about the context — it’s how your data interacts with the world outside and how it fits into the larger picture of your project or organization. Consider a database that holds customer details. usability) would be about extrinsic data quality.

Data Cleanse 52

Data Cleanse Data Engineering Data Engineer Engineering 52

Knowledge Hut

APRIL 23, 2024

Each data point provides a specific value or attribute that contributes to the overall understanding and analysis of the data. On the other hand, data sources pertain to the origins or locations from which the data is collected. Data from trustworthy and reputable sources are more reliable and dependable.

Big Data 52

Big Data Media Datasets Unstructured Data 52

Ascend.io

JANUARY 10, 2024

AI models are only as good as the data they consume, making continuous data readiness crucial. Here are the key processes that need to be in place to guarantee consistently high-quality data for AI models: Data Availability: Establish a process to regularly check on data availability. Actionable tip?

Data Pipeline 52

Data Pipeline Government Data Governance Raw Data 52

Monte Carlo

MARCH 24, 2023

The key differences are that data integrity refers to having complete and consistent data, while data validity refers to correctness and real-world meaning – validity requires integrity but integrity alone does not guarantee validity. What is Data Integrity? How Do You Maintain Data Integrity?

Data Validation 52

Data Validation Data Integration Data Cleanse Data Pipeline 52

Knowledge Hut

JUNE 28, 2023

Reporting, querying, and analyzing structured data to generate actionable insights. Data Sources Diverse and vast data sources, including structured, unstructured, and semi-structured data. Structured data from databases, data warehouses, and operational systems.

Data Mining 52

Data Mining Business Intelligence BI Datasets 52