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AI Implementation: The Roadmap to Leveraging AI in Your Organization

Ascend.io

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.

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[O’Reilly Book] Chapter 1: Why Data Quality Deserves Attention Now

Monte Carlo

As the data analyst or engineer responsible for managing this data and making it usable, accessible, and trustworthy, rarely a day goes by without having to field some request from your stakeholders. But what happens when the data is wrong? In our opinion, data quality frequently gets a bad rep.

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Data Teams and Their Types of Data Journeys

DataKitchen

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. The Hub Data Journey provides the raw data and adds value through a ‘contract.

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The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure

DataKitchen

The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure. While working in Azure with our customers, we have noticed several standard Azure tools people use to develop data pipelines and ETL or ELT processes. We counted ten ‘standard’ ways to transform and set up batch data pipelines in Microsoft Azure.

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Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

DataKitchen

L1 is usually the raw, unprocessed data ingested directly from various sources; L2 is an intermediate layer featuring data that has undergone some form of transformation or cleaning; and L3 contains highly processed, optimized, and typically ready for analytics and decision-making processes. What is Data in Use?

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Observability Platforms: 8 Key Capabilities and 6 Notable Solutions

Databand.ai

An observability platform is a comprehensive solution that allows data engineers to monitor, analyze, and optimize their data pipelines. By providing a holistic view of the data pipeline, observability platforms help teams rapidly identify and address issues or bottlenecks.

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How Assurance Achieves Data Trust at Scale for Financial Services with Data Observability

Monte Carlo

As the company scales, various teams within Assurance—including data engineering, machine learning engineering, data science, business intelligence, and analytics engineering—leverage the platform to create new data assets. The platform has introduced a host of other data-related possibilities. Quick time to value 2.