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Data Quality Management: The Three Pillars for Mastering Excellence

Date: 12 Jul 2023

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Building a data-driven culture requires companies to recognise that excellence in data quality is no longer an afterthought, but rather the foundation for success in the digital economy.


Data is the currency of the modern business world. From automation to AI & machine learning, data plays a fundamental role in nearly every facet of corporate strategy and operations. It’s time to recognise that excellence in data quality is no longer an afterthought – it’s the foundation for success in the digital economy. Business leaders must take decisive and focused actions to improve their enterprise data quality to transform for growth and accelerate data impact.

However, many businesses are hindered by the complexity and fragmentation of their internal data ecosystems, resulting in poor data quality across a range of measures. This makes achieving and sustaining good data quality standards a bigger challenge for organisations.

Issues such as incomplete data, missing or incorrect values, and outdated or duplicated data entries not only compromise day-to-day operations but also impede the adoption of digital workflows and slow the deployment of advanced analytics solutions. According to a Gartner report, poor data quality costs organisations an estimated USD 12.9 million each year.

As data quality management becomes increasingly crucial, businesses that strive for excellence must identify and embrace the three key tenets of achieving data quality. This article examines these three pillars in detail, beginning with a discussion of data quality and why it is important.

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Business leaders must take decisive and focused actions to improve their enterprise data quality to transform for growth and accelerate data impact.

What exactly is Data Quality, and why is it Important?

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Data quality is pivotal for leveraging data to its fullest potential. It involves assessing the integrity and accuracy of data for its intended use, along with additional quality metrics such as timeliness, consistency, and uniformity. So, why is data quality important? Here are some of the key advantages:

For accurate decision-making: Accurate data is essential for informed decision-making. For example, precise sales data allows companies to easily identify profitable customer segments and allocate resources more effectively.

For operational efficiency: Clean and reliable data promote smooth operations while minimising errors.

For data compliance: Established data compliance standards, such as GDPR and HIPAA, require businesses to adhere to data accountability principles. Hence, maintaining clean and reliable data is mandatory for compliance with these standards.

1. Data Discovery and Assessment


Data quality excellence begins with data discovery and assessment. This involves uncovering and categorising datasets from multiple sources to build a comprehensive repository of critical data assets. It also requires assessing the quality of the data, as well as identifying any remediation or preparation needs to ensure trusted and reliable data is available across the enterprise.

When assessing data, it’s important to identify any potential data quality issues. These can include:

  • Inaccurate or Incomplete Data: Discovered data can have inaccuracies or incompleteness due to human errors or data entry mistakes.
  • Data Bias: Bias occurs when the data does not accurately represent the desired group or activity. It can result from sampling or selection methods, human error, or systemic bias leading to skewed or misleading information.
  • Data Relevance: Collecting irrelevant or unnecessary data can increase storage costs and decrease efficiency.
  • Inconsistent Data: Discrepancies can arise in formats, units, or other aspects when dealing with multiple data sources. For example, a customer’s email address in the CRM may differ from the one in the account’s application.
  • Duplicate Data: Data discovery often encounters issues of duplicate information, such as collecting duplicate contact details.
  • Missing Data: Data may have missing values or blanks, such as when the job title of customers is missing from a dataset.
  • Outdated Data: Data may not be current and could contain outdated information, such as customer mailing addresses that are years old.

To measure data quality during the data discovery process, you can consider the following metrics:

  • Accuracy: This metric calculates the proportion of correctly collected data to all collected data. It helps you determine how accurate data collection procedures are.
  • Completeness: This metric determines the percentage of complete data collected against the expected data. It helps identify the extent to which data is missing or incomplete.
  • Timeliness: Timeliness measures the readiness of data for use when it is needed or expected. It is essential for determining whether data is current and corresponds to the necessary timeframe.
  • Bias: This entails determining the measure of bias in the process of gathering the data.
  • Data Integrity Index: The data integrity index measures the collected data’s overall accuracy and reliability.

2. Data Cleaning and Validation

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Achieving data quality requires thorough data cleansing and validation. Data cleansing identifies and resolves inconsistent, incorrect, inaccurate, duplicate, or incomplete elements within a dataset, while data validation verifies conformity to predefined standards and rules.

For example, when analysing sales data, data cleaning could include eliminating duplicate customer records or remedying incorrectly spelt product names. Validation would then involve checking to ensure that all sales entries have a valid date.

Some data-cleaning techniques for data quality are explained below.

  • Eliminating Duplicate Entries: Identify and eliminate any duplicate entries from your dataset with simple formulaic checks using tools like Excel for a more accurate result.
  • Handling Missing Values: This problem can be solved by substituting missing data using methods like mean, median, and mode.
  • Normalising and Standardising Data: Data can be in different forms and units. Standardising and normalising can help to maintain consistency. For example, numerical values could be transformed to a common range, such as 0-1, with min-max scaling.
  • Removing Outliers: Outliers are extreme values that can distort statistical analysis and machine learning models. You can use statistical techniques such as z-score and modified z-score to identify and remove outliers.

Data validation is an important step to ensure the accuracy and quality of datasets. Here are some of the common techniques used to validate data:

  • Error and Exception Handling: This technique involves capturing and handling errors and exceptions encountered during the validation process. It includes logging errors and implementing error resolution processes to address and correct data quality
  • Range Checks: The range rule ensures that the entered data falls within a specified range. Values outside the predefined range are considered invalid. Range checks help identify outliers and data inconsistencies.
  • Format Validation: Some data types have specific formats, such as dates or email addresses. The format validation rule ensures that the entered data adhere to the required format.
  • Uniqueness: In cases where certain fields need to contain unique values. The uniqueness validation rule checks if the entered data already exists in the dataset. This helps avoid duplicates and ensures data uniqueness.
  • No Null Values: This rule ensures that certain input fields cannot be left empty and must contain a value. It prevents the presence of null or missing data in critical fields.
  • Regular Expressions: Regular expressions techniques help to validate data against predefined rules. This is particularly useful for validating complex patterns like credit card numbers or identification numbers.

3. Data Governance and Control


Data Governance and control is the ongoing monitoring and management of data to maintain its quality and integrity through implementing policies and standards. One example of data governance and control is determining data stewards, data owners, and establishing data access protocols to oversee data monitoring and compliance within an organisation. Overall, data governance helps to establish and promote data management practices that drive high-quality and usable data throughout the data life cycle.

To achieve data quality excellence, you can implement the various data governance and control principles discussed below.

  • Accountability: Establishing and defining roles and responsibilities with accountability to ensure ownership, stewardship and control of data governance policies throughout the organisation.
  • Data Access and Security Controls: Ensuring the protection of data from unauthorised access. Implementing user authentication mechanisms and encryption techniques to maintain data quality and integrity.
  • Data Monitoring and Reporting: Establishing data quality monitoring and reporting mechanisms to continuously evaluate data quality. Incorporating data quality checks and automated data validation routines to detect data anomalies or errors. Generating data reports and dashboards to provide insight into data quality metrics and trends.
  • Data Automation: Automation streamlines data management processes, reducing human errors, boosting efficiency and enhancing data quality. By automating activities such as discovering, validating, enhancing, wrangling and cleansing data, businesses can accurately identify and resolve data inconsistencies quickly and efficiently.

Data governance and control is the ongoing monitoring and management of data to maintain its quality and integrity through implementing policies and standards.


In conclusion, building a culture of data quality excellence is an ambitious journey, yet undeniably worth the effort that each business must strive to attain and maintain – good data quality lies at the heart of a successful data strategy.

Developing trust in data through these pillars requires disciplined execution, meticulous effort, and unwavering commitment to data quality. When done right, it will set your organisation apart as you ascend the data maturity ladder towards successful adoption of advanced analytics, automation technologies and integrating artificial intelligence (AI) solutions.

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If you have any questions or need help discovering the capabilities, tools and expertise to achieve data quality excellence and harness data intelligence for impactful results, contact us at Trellisi – we’d love to chat.

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