Poor data quality: where does it originate and how to detect the error?

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shukla7789
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Joined: Tue Dec 24, 2024 4:27 am

Poor data quality: where does it originate and how to detect the error?

Post by shukla7789 »

In this post we tell you what the main reasons for poor data quality are and why it is important to prioritize data quality.
Advances in data and information management technology have forced companies to deal with a large increase in the amount and diversity of data they must manage, as well as in the elements to which all this information is associated.

Consequently, data quality is increasingly a concern for many organizations seeking to achieve a 360 view of the business and be able to make data-driven decisions. Now, what are the reasons for poor Data Quality ? Let's look at the top 4 reasons below.


The exponential increase in the volume of available twitter database has led to increasingly inefficient management of data at the company level, which directly affects their performance and decision-making, and ends up hindering organizational management.

If we understand that data is a strategic resource for the organization and that its management is a priority, it is essential to ensure that we manage the highest possible quality of information.





Investment in data analytics technology is set to increase by more than 20% over the next 5 years, with 77% of businesses surveyed saying data and analytics are critically important to achieving their digital transformation ambitions.

Source: Fujitsu



So, let's get to the root of the problem!
To detect how data quality is being lost, it is advisable to go to the root of the problem. Let's look at the 4 reasons that cause it:
Data entry: The biggest source of errors is manual data entry. This is due to, among other things, communication noise, typos or mistakes, and other external factors that end up causing poor data quality.
External data: External data is often automatically incorporated into organizations' information systems, without taking the necessary precautions. This causes multiple data quality problems, the problems of which can escalate if they are not detected in time.
Loading errors in transactional systems: The different errors that usually occur during loading in transactional systems are another focal point of the deficiency in data quality.
Migrations: When a data migration is carried out without having previously analyzed in depth the changes that must be applied to the information, one of the many consequences is usually the poor quality of that information (obsolete values, a format different from that expected in the new system, duplications, among other issues) .
Many times the first problems begin at the initial load and, as they are rarely corrected, they carry over into all subsequent operations, including extraction, transformation and transactions with the same data.
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