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How Data Observability Helps to Build Trusted Data

Precisely

Author’s note: this article about data observability and its role in building trusted data has been adapted from an article originally published in Enterprise Management 360. Is your data ready to use? That’s what makes this a critical element of a robust data integrity strategy. What is Data Observability?

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Why You Need Data Observability to Improve Data Quality

Precisely

It includes streaming data from smart devices and IoT sensors, mobile trace data, and more. Data is the fuel that feeds digital transformation. But with all that data, there are new challenges that may require consider your data observability strategy. Is your data governance structure up to the task?

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What Is Data Observability and Why You Need It?

Precisely

It includes streaming data from smart devices and IoT sensors, mobile trace data, and more. Data is the fuel that feeds digital transformation. But with all that data, there are new challenges that may prompt you to rethink your data observability strategy. Complexity leads to risk. Learn more here.

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Using Agile Data Stacks To Enable Flexible Decision Making In Uncertain Economic Times

Precisely

Business managers are faced with plotting the optimal course in the face of these evolving events. Pipelines must have robust data integration capabilities that integrate data from multiple data silos, including the extensive list of applications used throughout the organization, databases and even mainframes.

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Data Quality Framework: What It Is, Components, and Implementation

DagsHub

Data Quality Dimensions Data quality dimensions are the criteria that are used to evaluate and measure the quality of data. These include the following: Accuracy indicates how correctly data reflects the real-world entities or events it represents. Datafold is a tool focused on data observability and quality.

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Data integrity vs. data quality: Is there a difference?

IBM Journey to AI blog

This is the practice of creating, updating and consistently enforcing the processes, rules and standards that prevent errors, data loss, data corruption, mishandling of sensitive or regulated data, and data breaches. Effective data security protocols and tools contribute to strong data integrity.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

With Talend, you can assess data quality, identify anomalies, and implement data cleansing processes. Monte Carlo Monte Carlo is a popular data observability platform that provides real-time monitoring and alerting for data quality issues. Flyte Flyte is a platform for orchestrating ML pipelines at scale.