Data Governance Data Management Data Pipeline High Quality Data 
Data Engineering Weekly
MARCH 3, 2024
Here is the agenda, 1) Data Application Lifecycle Management - Harish Kumar( Paypal) Hear from the team in PayPal on how they build the data product lifecycle management (DPLM) systems. This approach helps maintain accuracy, relevance, and compliance in generative AI applications.
Databand.ai
AUGUST 30, 2023
This includes defining roles and responsibilities related to managing datasets and setting guidelines for metadata management. Data profiling: Regularly analyze dataset content to identify inconsistencies or errors. Additionally, high-quality data reduces costly errors stemming from inaccurate information.
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Ascend.io
JANUARY 10, 2024
Visual representation of Conway’s Law ( source ) Read More: The Chief AI Officer: Avoid The Trap of Conway’s Law Process: Ensuring Data Readiness The backbone of successful AI implementation is robust data management processes. AI models are only as good as the data they consume, making continuous data readiness crucial.
Monte Carlo
JANUARY 10, 2024
On the other hand, “Can the marketing team easily segment the customer data for targeted communications?” usability) would be about extrinsic data quality. In this article, we present six intrinsic data quality techniques that serve as both compass and map in the quest to refine the inner beauty of your data.
Precisely
MARCH 14, 2023
Read Quality data you can depend on – today, tomorrow, and beyond For many years Precisely customers have ensured the accuracy of data across their organizations by leveraging our leading data solutions including Trillium Quality, Spectrum Quality, and Data360 DQ+. What does all this mean for your business?
Monte Carlo
JUNE 16, 2023
Data integrity and quality may seem similar at first glance, and they are sometimes used interchangeably in everyday life, but they play unique roles in successful data management. Impact Now that you understand the purpose of data integrity and data quality, what is their impact on data management and decision-making?
Databand.ai
JULY 5, 2023
What Are Data Observability Tools? Data observability tools are software solutions that oversee, analyze, and improve the performance of data pipelines. Data observability tools allow teams to detect issues such as missing values, duplicate records, or inconsistent formats early on before they affect downstream processes.
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