Data Quality

Data Quality

In the era of a vast canvas, wild-spreading data world, the inherent preciousness of information is invaluable. Some might say that the data is digital petroleum, some even say it is like gold [1]. However, how well do we know that it is really a costly metal or just a yellow rock? It would be a huge waste of time and resources to dig for gold only to realize later that it is just a worthless stone.  In search of the holy data-driven grail, “Data Quality” is hence utilized as a qualitative measurement of data usefulness.

Data quality, known as one of the three crucial pillars of data governance [2], refers to the condition of data based on its ability to serve intended purposes effectively, thereby affecting the confidence level of businesses and consumers regarding the dataset and data analysis [3].

Key Dimensions of Data Quality

Michelle Knight states in [4] that the data quality consists of 6 key dimensions as follows:

  1. Accuracy: This measures how well the data precisely associates with real-world entities, such as building locations, phone numbers, and dates of birth.
  2. Completeness: This metric ensures that all necessary data are present and sufficient to avoid deceptive analysis or biased decisions.
  3. Consistency: This measures the uniformity of data across different systems and instances, ensuring that identical information matches wherever it is stored or used in multiple records or storage locations.
  4. Integrity: This dimension assesses the preservation of data structure and relationships throughout various processes and system transformations, ensuring traceability and connectivity [5].
  5. Uniqueness: This ensures that data entries are singular and free from duplicates, ensuring each entity is represented only once in the dataset.
  6. Validity: This evaluates whether data adheres to predefined formatting rules, including metadata such as data types, ranges, and patterns, ensuring alignment with business expectations and domain-specific requirements [2].

Pros of Good Data Quality / Cons of Poor Data Quality

Data-driven businesses considering the necessity of good data quality could gain countless impactful benefits. For example, it enables companies to make better, less biased decisions, improve business processes, save time, reduce costs, and even accelerate business growth [6].

However, when the data quality does not meet the minimum standard, it could lead to unreliable decision-making, profit losses, and missed opportunities, thereby harming the business performance [7]. In many cases, the poor data quality could even impact on the steps taken before making any decisions; a huge amount of time-consuming data cleansing is required to get rid of erroneous, unnecessary data. It is stated in [8] that bad data quality generally comes from 5 sources, including data entry errors, incomplete data, duplicate data, outdated data, and the lack of data standards. The following examples provide a better understanding of the mechanisms of poor data quality:

  • Inaccurate: The absence of the postal code in customer addresses leads to delayed shipment or even misdelivery, resulting in a bad customer experience.
  • Duplication: A salesperson recorded two identical customer details in the database, leading to inaccurate business decisions.
  • Invalidate: There are 10 or more different ways to write the date, which not only leads to confusion in data processing and analysis but also results in data misinterpretation, reporting errors, and even compliance issues in contracts or regulatory filings. Here are some formats you may imagine:
    • January 9, 2024
    • January ninth, 2024
    • Jan 9, 2024
    • Jan ninth, 2024
    • 9 January 2024
    • 9 January 2024 AD
    • 9 January 2567 BE
    • 01-9-2024
    • 01/9/2024
    • 9. 2024
    • 9/01/2024
    • 9-01-2024

Importance of Data Quality

“Garbage in, garbage out (GIGO)” is one of the ideas that can well describe and emphasize how important data quality is. The output quality is directly related to the input quality [9]. As per the examples mentioned above, wrongful decision-making and significant profit loss might occur even with small amounts of data discrepancies. High-quality data is therefore critical for data-driven organizations in order to make informed decisions, drive efficiencies, and maintain compliance. It ensures that data is accurate, complete, reliable, and relevant [10].


Data quality is crucial in this digital age, ensuring that data is accurate, complete, and reliable. It supports informed decision-making, enhances business processes, and fosters growth. Poor data quality, on the other hand, can lead to unreliable decisions, financial losses, and missed opportunities. Thus, maintaining high-quality data is essential for businesses aiming to thrive in a data-driven environment.

For those who are looking to start having a team to help with data analysis or create a data storage system in your organization, Davoy is ready to provide comprehensive data services starting at 25,000 baht per month. If you are interested in learning more, you can add us on Line: @DAVOY.


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