Alation

JANUARY 20, 2022

In Ryan’s “9-Step Process for Better Data Quality” he discussed the processes for generating data that business leaders consider trustworthy. To be clear, data quality is one of several types of data governance as defined by Gartner and the Data Governance Institute. Step 4: Data Sources.

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Data Quality Data Governance Data Profiling Clean Data 94

Pickl AI

OCTOBER 18, 2023

Data Enrichment Services Enrichment tools augment existing data with additional information, such as demographics, geolocation, or social media profiles. This enhances the depth and usefulness of the data. It defines roles, responsibilities, and processes for data management. How to Use AI in Quality Assurance?

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DagsHub

AUGUST 23, 2024

Data quality is crucial across various domains within an organization. For example, software engineers focus on operational accuracy and efficiency, while data scientists require clean data for training machine learning models. Without high-quality data, even the most advanced models can't deliver value.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

Pickl AI

JULY 24, 2024

Clear Formatting Remove any inconsistent formatting that may interfere with data processing, such as extra spaces or incomplete sentences. Validate Data Perform a final quality check to ensure the cleaned data meets the required standards and that the results from data processing appear logical and consistent.

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Snorkel AI

MAY 12, 2023

To borrow another example from Andrew Ng, improving the quality of data can have a tremendous impact on model performance. This is to say that clean data can better teach our models. Another benefit of clean, informative data is that we may also be able to achieve equivalent model performance with much less data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Snorkel AI

MAY 12, 2023

To borrow another example from Andrew Ng, improving the quality of data can have a tremendous impact on model performance. This is to say that clean data can better teach our models. Another benefit of clean, informative data is that we may also be able to achieve equivalent model performance with much less data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52