Data quality management: What, why, how, and best practices – Data Ladder
The most common data issue that enterprises deal with is that of its quality. You have the right data applications deployed, the sources capture the kind of data you need, there is an entire system that uses and analyzes the collected data, and yet, the results are unsatisfactory. On further analysis, you find differences between data expectations and reality; the datasets are filled with blank fields, inconsistent abbreviations and formats, invalid patterns, duplicate records, and other such discrepancies.
To eliminate these problems, you must implement corrective measures that consistently validate and fix data quality issues. But to make the data quality dream a reality, it is necessary to understand the basics of data quality – its meaning, impact, and how to plan for improvement. For this reason, we are sharing with you a comprehensive guide that covers everything related to data quality management: what it means, how it can impact a business, how it can be managed, what it looks like across various verticals, and more.
This guide is divided into three parts:
- Data quality: What is it and why is it important?
- Data quality issues: What are they, where they come from, and how they impact business?
- Data quality management: What it means, its pillars and best practices, and some real-world examples in various industries.
Let’s get started.
