Data quality: how to achieve efficient measurement

[najmulislam77]

Far from being an unimportant aspect, ensuring data quality is a fundamental step for organizations.

When data quality is guaranteed, information consistency is maintained to make good decisions and boost business.

Now, what aspects should be considered when analyzing the level of reliability, completeness and precision of the information? We will tell you about them in this article.

Data quality, a key aspect for business success
Before we discuss how to measure data quality, let’s review why it is an essential factor for business growth.

When we talk about data quality , we refer to a set of methods, techniques and practices that are deployed to ensure that data is objective, solid and suitable for making decisions.

Data quality determines process efficiency, operational agility, customer satisfaction and business evolution.

Ensuring the quality of information allows companies to:

Optimize your decision-making process , which is now based china phone number lead on reliable records. In this way, companies create successful strategies that improve their performance and increase their competitive advantages in the market.
Improve collaboration because when all members of an organization can access accurate data to carry out their activities, they align with business priorities and achieve cohesive results.
Increase your operational efficiency because by eliminating bad or poor quality data, you reduce errors, downtime and the rework required to correct mistakes.
Understand your customers better by gaining accurate insights. When data is reliable, businesses better understand their customers' pain points, needs, and aspirations. This allows them to create offers that fit these requirements and improve interactions with users.
Ensure regulatory compliance by complying with rules and regulations in order to avoid penalties and inconveniences that may lead to loss of reputation or legal problems.
Data quality: 7 dimensions that allow it to be measured
It is clear that it is important for organizations to ensure that their data is of high quality. But how can data quality be measured?

The answer lies in the dimensions of data quality, which are metrics used by data specialists to calculate the quality and usefulness of records for users. While each organization can determine its own parameters, generally six characteristics are used.

Precision
When data is inaccurate, using it in a business strategy is dangerous, as it can lead to undesirable results, reducing the trust of employees and customers in the company.

Data values must be consistent with actual values, so organizations need to analyze their accuracy in order to obtain real value from them.

Relevance
Beyond its accuracy, to truly add value, data must be relevant to the business itself. Having unnecessary information can disorganize databases, generating losses of time and effort.

Therefore, data must be relevant, that is, serve the purpose for which it was collected in the first place.

Coherence
Inconsistencies in information lead to confusion and the need to implement mechanisms to detect inconsistencies. This ultimately means investing time and effort to find and eliminate erroneous records.

In this sense, it is essential to ensure that the values of certain data comply with a specific rule. It represents the degree to which a single piece of data contains the same value across multiple data sets.

Integrity
Is it worth collecting large amounts of data without assessing its completeness? When information is not complete, it could be unusable.

Therefore, when collecting data, it is essential to ensure that it is complete. Only in this way is it possible to make good decisions and drive organizational strategy.