#1 What Is Really Behind The Recurring Data Quality Issues?

Repeat data quality issues are a repeat smoke-and-mirrors show that keep a CDO's team occupied with low value tasks. They are a pain every experienced CDO knows well.

In my career, I noticed data quality issues on the very first retail supply chain integration project I enabled, and I see them still almost everywhere I go (similar to the main character from The Sixth Sense, without Bruce Willis around in my case...). These issues are often misunderstood and handled in a way that compounds the negative impact. This is why I decided to write about them as the first chapter in this series.

Here are top two reasons data quality issues linger....

1. Data quality issues often are attributed to tools or lack thereof. In my experience, they are almost always more about the lifecycle of the data and its management. Without visibility to how the data travels, it is impossible to understand where issues originated. This is where an overall enterprise wide data strategy can rescue a data team from repeat occurances.

2. Sometimes, data quality issues are about the datasets and their properties. Older datasets still in use tend to have more data quality issues because as the volume of data increases, context behind the data is lost over time. New data vs old data may not have same contextual logic. This is similar to uncommented codebase produced by a developer, who eventually leaves the team. New team members have no context behind the algorithmic choices made by previous developer. As they make changes to code or even configurations, the underlying properties of the system change.

At GoEmerald, we specialize in taking a different approach to address data quality issues. Instead of finding solutions in tools, we look across the organization to help you locate the root cause so the issue does not occur again.

If you have been battling repeat data quality issues persistently, we can help identify the root cause with measurable impact.

Let's talk data quality:

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