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Fueling Data-Driven Decision-Making with Data Validation and Enrichment Processes

Precisely

77% of data and analytics professionals say data-driven decision-making is the top goal for their data programs. Data-driven decision-making and initiatives are certainly in demand, but their success hinges on … well, the data that supports them. More specifically, the quality and integrity of that data.

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Complete Guide to Data Transformation: Basics to Advanced

Ascend.io

What is Data Transformation? Data transformation is the process of converting raw data into a usable format to generate insights. It involves cleaning, normalizing, validating, and enriching data, ensuring that it is consistent and ready for analysis.

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Interesting startup idea: benchmarking cloud platform pricing

The Pragmatic Engineer

Storing data: data collected is stored to allow for historical comparisons. Benchmarking: for new server types identified – or ones that need an updated benchmark executed to avoid data becoming stale – those instances have a benchmark started on them. Each benchmarking task is evaluated sequentially.

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Webinar: Data Quality in a Medallion Architecture – 2024

DataKitchen

Would you like help maintaining high-quality data across every layer of your Medallion Architecture? Like an Olympic athlete training for the gold, your data needs a continuous, iterative process to maintain peak performance. Want More Detail? Read the popular blog article.

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Snowflake PARSE_DOC Meets Snowpark Power

Cloudyard

Read Time: 2 Minute, 33 Second Snowflakes PARSE_DOCUMENT function revolutionizes how unstructured data, such as PDF files, is processed within the Snowflake ecosystem. However, Ive taken this a step further, leveraging Snowpark to extend its capabilities and build a complete data extraction process. Why Use PARSE_DOC?

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Use Data Enrichment to Supercharge AI

Precisely

The answers lie in data integrity and the contextual richness of the data that fuels your AI. Businesses must navigate many legal and regulatory requirements, including data privacy laws, industry standards, security protocols, and data sovereignty requirements. Contextual data. Data integrity is multifaceted.

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The 6 Data Quality Dimensions with Examples

Monte Carlo

It’s clear that data quality is becoming more of a focus for more data teams. So why are there still so many questions like these: A quick search on subreddits for data engineers, data analysts, data scientists, and more can yield a plethora of users seeking data quality advice.