Remove Data Integration Remove Datasets Remove High Quality Data
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Automation and Data Integrity: A Duo for Digital Transformation Success

Precisely

Key Takeaways: Harness automation and data integrity unlock the full potential of your data, powering sustainable digital transformation and growth. Data and processes are deeply interconnected. Today, automation and data integrity are increasingly at the core of successful digital transformation.

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Data Integration for AI: Top Use Cases and Steps for Success

Precisely

Key Takeaways Trusted data is critical for AI success. Data integration ensures your AI initiatives are fueled by complete, relevant, and real-time enterprise data, minimizing errors and unreliable outcomes that could harm your business. Data integration solves key business challenges.

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Data Integrity vs. Data Quality: How Are They Different?

Precisely

When companies work with data that is untrustworthy for any reason, it can result in incorrect insights, skewed analysis, and reckless recommendations to become data integrity vs data quality. Two terms can be used to describe the condition of data: data integrity and data quality.

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Unleashing GenAI — Ensuring Data Quality at Scale (Part 2)

Wayne Yaddow

First: It is critical to set up a thorough data inventory and assessment procedure. Organizations must do a comprehensive inventory of their current data repositories, recording the data sources, kind, structure, and quality before starting data integration.

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The Challenge of Data Quality and Availability—And Why It’s Holding Back AI and Analytics

Striim

Many organizations struggle with: Inconsistent data formats : Different systems store data in varied structures, requiring extensive preprocessing before analysis. Siloed storage : Critical business data is often locked away in disconnected databases, preventing a unified view. Heres how they are tackling these issues: 1.

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No Python, No SQL Templates, No YAML: Why Your Open Source Data Quality Tool Should Generate 80% Of Your Data Quality Tests Automatically

DataKitchen

Current open-source frameworks like YAML-based Soda Core, Python-based Great Expectations, and dbt SQL are frameworks to help speed up the creation of data quality tests. They are all in the realm of software, domain-specific language to help you write data quality tests.

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Data Appending vs. Data Enrichment: How to Maximize Data Quality and Insights

Precisely

After my (admittedly lengthy) explanation of what I do as the EVP and GM of our Enrich business, she summarized it in a very succinct, but new way: “Oh, you manage the appending datasets.” We often use different terms when were talking about the same thing in this case, data appending vs. data enrichment.

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