Pickl AI

OCTOBER 11, 2023

Data Observability and Data Quality are two key aspects of data management. The focus of this blog is going to be on Data Observability tools and their key framework. The growing landscape of technology has motivated organizations to adopt newer ways to harness the power of data.

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Data Observability Data Quality Data Pipeline Data Governance 52

Pickl AI

OCTOBER 10, 2023

In this blog, we are going to unfold the two key aspects of data management that is Data Observability and Data Quality. Data is the lifeblood of the digital age. Today, every organization tries to explore the significant aspects of data and its applications. What is Data Observability and its Significance?

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Data Observability Data Quality Data Governance Data Pipeline 52

Precisely

SEPTEMBER 9, 2024

. • 41% of respondents say their data quality strategy supports structured data only, even though they use all kinds of data • Only 16% have a strategy encompassing all types of relevant data 3. Enterprises have only begun to automate their data quality management processes.” Adopt process automation platforms.

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Data Quality Data Profiling Data Lakes Analytics 63

Alation

JANUARY 20, 2022

This work enables business stewards to prioritize data remediation efforts. Step 4: Data Sources. This step is about cataloging data sources and discovering data sources containing the specified critical data elements. Step 5: Data Profiling. This is done by collecting data statistics.

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Data Quality Data Governance Data Profiling Clean Data 94

Precisely

SEPTEMBER 19, 2024

Badulescu cites two examples: Quality rule recommendations: AI systems can analyze existing data to understand data ranges, anomalies, relationships, and more. Then, this information can be used to suggest new quality rules that will help prevent data issues proactively.

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Data Governance Data Quality AI AI 77

Precisely

SEPTEMBER 11, 2023

Here are some of the key capabilities, and what they mean for you: Auto metadata discovery: Use data profiles to enable the collection and detection of an extensive range of metadata upon data ingestion or on a scheduled basis.

Data Quality 52

Data Quality AI AI Data Profiling 52

Pickl AI

JULY 24, 2024

Bias Systematic errors introduced into the data due to collection methods, sampling techniques, or societal biases. Bias in data can result in unfair and discriminatory outcomes. Read More: Data Observability vs Data Quality Data Cleaning and Preprocessing Techniques This is a critical step in preparing data for analysis.

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