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5 most common data quality issues and how to overcome them.

With the advent of data socializing, many organizations acquire, exchange, and make data accessible to all employees in an effective manner.

Most businesses benefit from having such information resources at their fingertips, others have concerns about the data's accuracy. This is especially nowadays, that most businesses consider deploying artificial intelligence systems or connecting their operations via the Internet of Things.

Duplicate, incomplete, unstructured data, missing data can cause quality issues. In this article we will the common data quality issues and how to overcome those using DQLabs Augmented DataQuality Platform.

Duplicate Data: Duplicate data arises when the same data is stored in a database multiple times, often in slightly different ways. If duplicate data isn't recognized, it can lead to inaccurate results.

Unstructured Data: Numerous times, the data has not been entered correctly within the frameworks, few records have been corrupted and the remaining data has some missing variables.

Security Issues: The security of data is based on three fundamental principles; confidentiality, integrity, and availability. An organization's business-critical data, as well as private and personal information, must be protected. A strong data security strategy differentiates the protection of the organization's data assets, prioritizing the protection of the most vital data.

Human error: Human error is the biggest challenge to achieve data quality. The effective way to minimize this issue is to minimize human effort and the use of AI-based systems, as well as advanced algorithms, ensures that human error is minimized.

Inaccurate data: There’s no point in running enormous information analytics based on information that’s fair plain off-base. By not gathering all the up-to-date data, your information isn’t total and limits you from making choices based on a complete and precise information set.


How DQLabs help to overcome data quality issues?

DQLabs helps organizations to solve data quality issues by leveraging DQLabs' augmented data quality platform. That scans various types of data sources and data sets in real-time and generates a trustable DQScore™ with the ability to track, manage and improve data quality over time.

Here are the features of DQLabs Data Quality:

  • Out of the box Data Quality Measurement
  • Semantics-based DQ Visual Learning
  • Create and Integrate Issue Workflows
  • Create Domain Level Scoring
  • Create Complex Rules with Ease
  • Integration with other Data Catalog/Governance Platforms


Want to learn more about how DQLabs solves data quality issues with its ML and self-learning capabilities in detail?
Request for a free demo.

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Comment by ABHAY SHARMA on September 28, 2021 at 3:16am

Nice insights I found here. I am pleased to see that human error has also been included as a factor affecting data quality which is a bit intruiging. 

I think for our readers to boost up their knowledge on data science and data analysis, they can also visit Skillslash blogs, which I also found a lot helpful

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