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Effective strategies for gathering requirements in your data project

Dataconomy

However, the success of any data project hinges on a critical, often overlooked phase: gathering requirements. Conversely, clear, well-documented requirements set the foundation for a project that meets objectives, aligns with stakeholder expectations, and delivers measurable value. Key questions to ask: What data sources are required?

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Alation 2022.2: Open Data Quality Initiative and Enhanced Data Governance

Alation

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

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Data Profiling: What It Is and How to Perfect It

Alation

For any data user in an enterprise today, data profiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of data profiling, top use cases, and share important techniques and best practices for data profiling today.

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Data Quality in Machine Learning

Pickl AI

Summary: Data quality is a fundamental aspect of Machine Learning. Poor-quality data leads to biased and unreliable models, while high-quality data enables accurate predictions and insights. What is Data Quality in Machine Learning? Bias in data can result in unfair and discriminatory outcomes.

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

DagsHub

As such, the quality of their data can make or break the success of the company. This article will guide you through the concept of a data quality framework, its essential components, and how to implement it effectively within your organization. What is a data quality framework?

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Unlocking the 12 Ways to Improve Data Quality

Pickl AI

Data quality plays a significant role in helping organizations strategize their policies that can keep them ahead of the crowd. Hence, companies need to adopt the right strategies that can help them filter the relevant data from the unwanted ones and get accurate and precise output.

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7 Data Lineage Tool Tips For Preventing Human Error in Data Processing

Smart Data Collective

Data entry errors will gradually be reduced by these technologies, and operators will be able to fix the problems as soon as they become aware of them. Make Data Profiling Available. To ensure that the data in the network is accurate, data profiling is a typical procedure.