Why is Data Quality Important?

In this era of digital disruption, data is of utmost importance for enterprises to gain a competitive edge. High-quality data enables more efficiency in driving enterprise success because of the dependence on empirically-based decisions instead of human or habitual intuition. Data will be highly useful and will serve its purpose only when it is of high quality. Enterprises that constantly focus on improving their data quality grow faster.

Let us understand the Characteristics of Data Quality.

Completeness – It helps to measure all that required and necessary data which is found in the accurate dataset. It precisely indicates whether there is sufficient information to come up with conclusions or not. Furthermore, it ensures that there are no gaps in the data and what data was collected, and the data that was supposed to be collected.

Consistency –  Measuring two data values that were derived from two different datasets are not conflicting with each other. A common data quality metric for consistency is the percent of values that match across various records.

Accuracy – Accuracy helps to measure how accurately your data corresponds to reality. If you are working in any critical aspects of the data, the data accuracy metric is of high importance. In this context, there is no chance for interpretation i.e., the numbers are either accurate or not.

Timelines – Timelines, for a specific period, help to measure the accuracy of data. This characteristic of the data quality measures the time between expecting the data and the moment you can use it. An ideal metric to measure timeliness is the data time-to-value. 

Integrity – It makes sure that your data remains the same when it travels between multiple systems. The main goal is to make sure that there are no data errors and the data transformation error rate is one of the most commonly used metrics to measure integrity.

Validity – Validity enables checking your data whether it complies with the required value attributes. For example, by making sure the year, month, and days are in the same format.

Common Data Quality Metrics to Measure Quality of the Data.

Making the correct business decisions at the right time is possible with the right quality of your data. It not only impacts your ability to solve complex problems but also helps to reach your goals. Measuring the quality of data using the correct metrics is of utmost importance

Let us discuss some of the metrics, enterprises use in measuring data quality performance

Data transformation error rates – Data transformation includes the process of conversion of the data from one format to another. Any issues that may arise during the process, reflect that there are some problems with the quality of your data.

Observing the specific number of failed data transformations enables you to learn more about the overall quality of the data. One important thing to understand is that if the transformation process or phase is consuming too much time, it’s likely that your data is flawed.

Ratios of data to errors – This metric enables viewing the number of errors in a single data relevant to the size of the data set. Common data errors include redundant, missing, and incomplete entries. If you find fewer errors while the size of your data set stays the same or grows, it reflects that the quality of your data is growing.

Email bounce rates – If you experience emails bouncing back to you, it is regarded as low-quality data. Outdated or missing information is responsible for bouncing back as the emails are sent to the wrong addresses.

Number of empty values– This metric helps to display the data recorded in the wrong field or show the number of empty fields in your data set

Dark Data – Dark data is the data that is acquired through different types of computer network operations and which cannot be used to achieve data insights for decision making. If there is a lot of data it suggests that the overall quality of the data is low.

Data time-to-value – As per this, the amount of time that is spent to derive the results from a given data set will help you in identifying your data quality. This quality data metric is determined by many factors and data quality issues are responsible for slowing down the process of generating vital information

Data Storage costs – This is one of the common signs of data quality issues. When the amount of the data that you use remains the same but the cost of your data storage increases or vice-versa.

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Why is Data Quality Important?

Characteristics of Data Quality.

Metrics to Measure Quality of the Data.