What is data Quality & why it is important?

With data being at the center of all decision-making in an organization, it’s crucial to correctly manage this data for optimum results. To make sure that the data quality is up to date, Data management practices are adopted. Data management looks over the task of gathering, regulating, sorting, and storing data efficiently for its better utilization. Data Quality is one such criterion ensured during data management.

Data Quality determines the condition of the data i.e. how accurate, complete, reliable, unique, and reliable it is. Data Quality is a measure of data being as reliable as it can so that it can enhance the decision-making process.

According to Gartner, 40% of business Initiatives Fail Due to Poor Data Quality, Poor data costs 12% of the Overall Revenue of the Company, and “organizations believe poor data quality to be responsible for an average of $15 million per year in losses.” Gartner also found that nearly 60% of those surveyed didn’t know how much bad data costs their businesses because they don’t measure it in the first place.

Research firm Forrester said in its research found that “less than 0.5% of all data is ever analyzed and used” and estimates that if the typical Fortune 1000 business were able to increase data accessibility by just 10%, it would generate more than $65 million in additional net income.


There are a few factors through which we can measure the quality of the collected data:

  • Accuracy: The data collected should have correct details. Incorrect data entries must be identified, documented, rectified, or removed if need be so that the system remains efficient.
  • Completeness: The data entries must be complete to provide all adequate information. If there’s a database for employee information, details regarding personal and professional data must be completely mentioned
  • Consistency: Consistency ensures that the same data at different locations match each other, i.e. no two records belonging to the same object should have different values. E.g. if we have two databases for employee data belonging to different departments, where in one department ‘date’ field records are entered in the format ‘DD/MM/YYYY’ and in the other, it entered in the format ‘MM/DD/YYYY’. Collecting data from both databases will result in inconsistency and hence a standard format must be selected.
  • Reduced Redundancy: Redundancy exists when there’s a duplicity of data. We need to make sure our data collection is free from such unwanted, extra, and repeated information that won’t add value to our system.
  • Uniqueness: Uniqueness is a result of removing redundant data, i.e. to ensure the data collected is distinct and desirable to our system.

If we ensure our data follows all these features, we can ensure data quality.


All industries today are data-driven. They use data to upgrade their systems, boost their sales, advance their marketing, and collectively increase their revenue. This increases the need for attention towards maintaining data quality.

The answer to the question Why data quality is important? lies in the following points:

  1. Good data quality helps in conducting favorable data analysis: The main aim is to improve the quality and accuracy of the analysis performed, which is possible only if the data used is up to the mark.
  2. Improved decision-making: As a result of refined analysis using relevant data, decision-making also improves.
  3. Reduced efforts in identifying and rectifying errors: Ensuring data is of good quality makes it less prone to errors and reduces the cost, time, and efforts required to identify and remove them.
  4. Helps avoid process breakdowns: Using unprocessed, rough and unfiltered data can result in undesired and inappropriate results. This can hamper decision making, the functioning of certain operations and decrease the overall revenue of an organization.


We learned how important it is to maintain data quality and how it affects not only data collection but also the subsequent data analysis process which is crucial for decision making.

Let us now see how data quality can be determined:

  1. Poor data quality analysis: The first step is to analyze issues reported by testers and users. Analysts need to understand unwanted data characteristics and layout data quality requirements which will help in organizing the data further.
  2. Data Profiling: Next step is to analyze what kind of data the organization requires for analysis, i.e. to understand the problem statement and identify the type of data which will be beneficial.
  3. Understanding Quality criteria: Analysts have to come up with methods to measure data quality, create acceptability standards and evaluate its business impact.
  4. Setting up Data Management rules: Valid rules and definite standards are agreed upon and set up for data quality measurement.
  5. Practical Application: One of the most important steps of maintaining data quality is to implement the above-decided convention into practice.
  6. Data Monitoring and Updates: Updating the progress and continuously monitoring the process for its smooth and systematic execution.


  1. Dividing Responsibilities: It is important to divide roles and responsibilities among the team members. Deciding who will be responsible for strategic activities, who will take in-charge of execution activities, and who will organize and manage operations is crucial and can be strenuous.
  2. Recognizing Data Quality Issues: The members need to correctly identify which data is valid and separate it from the invalid data. Correct standards for data quality must be followed in order to collect an ordered set of data.
  3. Managing Teams: The whole process is a task of numerous teams working together. The data architects, engineers, testers, and solution architects, everyone should communicate with each other with full transparency and report to each other about their progress.
  4. Monitoring efforts: Be it time, cost, or manual labor, it needs to be monitored and tracked for progress. It is important to set KPIs (Key Performance Indicators) in order to carry on the process smoothly.
  5. Maintaining Organization: There must be understanding and trust among the different teams that work for data quality assurance. Communication is a must for the proper and orderly execution of tasks.

Read about Data Silos here.

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