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

Monte Carlo

Data is a priority for your CEO, as it often is for digital-first companies, and she is fluent in the latest and greatest business intelligence tools. What about a frantic email from your CTO about “duplicate data” in a business intelligence dashboard?

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Mastering Data Quality: 5 Lessons from Data Leaders at Babylist and Nasdaq

Monte Carlo

while overlooking or failing to understand what it really takes to make their tools — and, ultimately, their data initiatives — successful. When it comes to driving impact with your data, you first need to understand and manage that data’s quality. learn when and why data may be down.

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Build vs Buy Data Pipeline Guide

Monte Carlo

Data ingestion When we think about the flow of data in a pipeline, data ingestion is where the data first enters our platform. There are two primary types of raw data. And data orchestration tools are generally easy to stand-up for initial use-cases. Missed Nishith’s 5 considerations?

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

DataKitchen

This proactive approach to data quality guarantees that downstream analytics and business decisions are based on reliable, high-quality data, thereby mitigating the risks associated with poor data quality. There are multiple locations where problems can happen in a data and analytic system.

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

Monte Carlo

The ACE comprises all types of data contributors, from analytics engineers to data engineers to business intelligence analysts, who collaborate to help the business make more strategic decisions using data. The platform has introduced a host of other data-related possibilities. Quick time to value 2.