6 Pillars of data quality and how to improve your data across your organisation

Have you ever stopped to consider how much bad data could cost your organisation?

The answer might surprise you: poor-quality data costs businesses millions each year, not just in financial terms but also in wasted time, inefficiencies and missed opportunities. When data is inaccurate, incomplete, or outdated, the consequences ripple through every decision, process and customer interaction.

High-quality data, on the other hand, serves as the foundation for informed decision-making, streamlined operations and trust (both internally and externally). But achieving and maintaining quality data is no small feat. In this article, we’ll explore what data quality means, why it’s crucial, and, most importantly, how you can improve the quality of your data to ensure your organisation thrives in a data-driven world.

What is data quality?

Data quality refers to how well data meets the requirements of its intended use. High-quality data is accurate, complete, consistent, up-to-date, valid and accessible. These attributes ensure that data is suitable for analysis, decision-making and operations.

For example:

• A sales report may show revenue growth, but if it contains duplicate entries or errors, it misrepresents the actual performance.
• Similarly, a healthcare database missing patient allergy information can lead to dangerous treatment decisions.

Good data quality ensures that organisations can rely on their data to make sound decisions, run operations smoothly and deliver value to customers.

Why is data quality important?

Organisations generate and rely on vast amounts of data for nearly every aspect of their operations, whether it’s analysing market trends, forecasting demand, or improving customer experiences. However, the value of this data hinges entirely on its quality. Even the most advanced analytics tools or predictive models cannot deliver meaningful results if the underlying data is inaccurate, incomplete or outdated.

Data is often described as the lifeblood of modern businesses, but poor-quality data acts more like a toxin, it introduces errors, slows down processes and leads to costly missteps. On the other hand, high-quality data allows organisations to operate with confidence, efficiency and precision. 

Below are the key reasons why ensuring data quality should be a top priority for any organisation:

1. Better decision-making

Every strategic decision an organisation makes depends on accurate and reliable data. Poor-quality data leads to flawed assumptions, which result in poor outcomes. 

• For example: A company forecasting demand based on erroneous sales data might overproduce, tying up resources in unsold stock, or underproduce, losing customers to competitors.

High-quality data eliminates guesswork, enabling decision-makers to act based on a clear and accurate understanding of the situation.

2. Improved operational efficiency

Poor data quality creates inefficiencies at every level of an organisation. Processes slow down as employees spend extra time correcting errors, searching for missing information or reconciling inconsistencies. 

• For example: A logistics company relying on outdated addresses may face increased delivery times, higher costs and dissatisfied customers.

With accurate and complete data, organisations can streamline workflows, reduce costs and avoid unnecessary manual intervention.

3. Regulatory compliance

Regulatory bodies increasingly expect organisations to maintain high standards of data accuracy, completeness and security. Failing to meet these standards can lead to significant financial penalties and reputational damage. 

• For example: Under GDPR, businesses must keep customer data accurate and up to date. If a customer requests corrections or deletions and the organisation cannot comply, it risks non-compliance and fines.

Maintaining high-quality data helps organisations stay on the right side of regulatory requirements and demonstrates accountability to stakeholders.

4. Builds trust with customers and partners

Customers and business partners expect organisations to use data responsibly and accurately. When errors occur, such as incorrect billing, duplicate communications, or irrelevant offers, it undermines trust. 

• For example:A customer who receives marketing emails with misspelled names or irrelevant offers might question the competence of the organisation and lose interest in engaging further.

High-quality data enables businesses to deliver personalised and relevant interactions, strengthening relationships and building loyalty.

5. Competitive Edge

Businesses with accurate, complete and timely data can respond more effectively to changes in the market, identify emerging trends and stay ahead of the competition. 

• For instance: A retailer tracking sales trends in real time can quickly adjust stock levels or introduce promotions to maximise profits during peak periods.

In competitive industries, high-quality data allows organisations to act decisively and remain agile, ensuring they are better equipped to meet the needs of their customers and the market.

Data Quality vs Data Integrity

Data is one of the most critical assets an organisation can possess, but ensuring its effectiveness involves more than just quality. While data quality and data integrity are closely related concepts, they focus on different aspects of data management. Confusing or neglecting either of these concepts can result in unreliable insights, operational errors and reputational damage.

Think of data as a house: data quality ensures that the materials used to build the house are strong, accurate and fit for purpose, while data integrity ensures that the structure of the house remains intact and protected over time. Both are essential and understanding the difference between them is key to building a reliable and trustworthy data ecosystem.

What is Data Quality?

Data quality refers to how well data meets specific criteria to be useful, such as accuracy, completeness, consistency, timeliness, validity and accessibility. It ensures that the data is correct, relevant and ready to serve its intended purpose.

• Example of poor data quality:
  A retail company maintains a customer database with duplicate entries for the same individual, where one record lists the customer as "John Smith" and another as "Jon Smith." This inconsistency can lead to duplicate communications, skewed analytics and wasted resources.

High-quality data provides confidence in decision-making, supports efficient processes and ensures compliance with regulatory standards.

What is Data Integrity?

Data integrity, on the other hand, refers to the reliability, security and consistency of data over its lifecycle. It ensures that data remains unchanged, complete and protected from unauthorised modifications as it moves between systems, users, or environments. Data integrity focuses on preserving the accuracy of data at every stage, whether it’s being created, stored, transferred, or processed.

• Example of compromised data integrity:
  A company experiences a system failure that corrupts several key financial records, rendering them unreadable. Even though these records were accurate before the failure, their integrity has been lost and they are no longer usable or trustworthy.

Good data integrity practices ensure that data remains secure and reliable, even as it undergoes transformations or is shared across systems.

Data Quality vs Data Integrity: Key Differences between Data Quality and Data Integrity

Below is a side-by-side comparison to illustrate their key differences between Data Quality and Data Integrity:

How do they work together?

Data quality and data integrity are interdependent. Even the highest-quality data is useless if its integrity is compromised and data with strong integrity can still fail to provide value if it lacks quality.

For example:

• A customer database may contain perfectly accurate and complete information (high quality), but if an unauthorised user deletes 20% of the records, the database loses its integrity and becomes unreliable.
• Alternatively, a financial report stored securely with perfect integrity may still be inaccurate if it was generated from incorrect or incomplete input data.

By addressing both quality and integrity, organisations can ensure their data remains accurate, secure and reliable for all purposes.

Building both data quality and integrity

To maintain both data quality and integrity:

1. Establish robust data governance: Define roles, policies and procedures to manage data throughout its lifecycle.
2. Implement validation processes: Ensure data is accurate and complete before it enters the system.
3. Protect data with security measures: Use encryption, access controls and backup systems to prevent unauthorised changes or losses.
4. Monitor continuously: Regularly audit data for accuracy and detect integrity issues, such as corruption or unauthorised modifications.

The 6 pillars of data quality

Data quality is not a single attribute but a combination of several key characteristics that ensure data is fit for its intended use. These characteristics, often referred to as the "pillars" of data quality, provide a framework for assessing and improving data across different dimensions.

Each pillar represents a specific aspect of data quality (such as accuracy, completeness, or timeliness) and together, they form the foundation for reliable and actionable data. Neglecting even one pillar can compromise the effectiveness of your data, leading to flawed insights and inefficient processes.

Let’s explore these six pillars in detail, breaking down what they mean, why they matter and how to address challenges in each area.

1. Accuracy

Accuracy refers to how well data represents the real-world object or event it describes. Inaccurate data leads to flawed insights, poor decision-making and operational inefficiencies.

Why it matters:
If a retail company records incorrect sales figures, the resulting analysis might overestimate profitability. Similarly, inaccurate customer information can lead to failed communication or dissatisfaction.

Example:
A customer database lists an individual’s email address as "jane.doe@gamil.com" instead of "jane.doe@gmail.com". This small error prevents successful email communication and wastes marketing resources.

How to improve accuracy:

• Implement automated validation tools to verify data during entry (e.g., checking email syntax or confirming postal codes).
• Cross-check data against trusted external sources, such as government databases or standardised directories.
• Provide training to employees responsible for data entry to reduce human errors.

2. Completeness

Completeness ensures that all necessary information is included and no critical data is missing. Incomplete data can skew results, lead to biased analysis, or render datasets unusable for their intended purpose.

Why it matters:
A lack of crucial details can disrupt processes and decisions. For instance, if a healthcare provider’s patient records omit allergy information, the risk of prescribing harmful treatments increases significantly.

Example:
A survey response database captures names and feedback but omits respondents' contact details. This makes it impossible to follow up with participants or clarify ambiguous responses.

How to improve completeness:

• Define mandatory fields during data collection (e.g., name, email, phone number).
• Regularly audit datasets to identify missing information and fill in gaps through additional data collection or enrichment services.
• Establish clear data collection processes and set expectations for completeness at every stage.

3. Consistency

Consistency ensures that data remains uniform across systems, datasets and time. Inconsistent data creates confusion, errors and inefficiencies, especially when data is shared across departments or systems.

Why it matters:
Inconsistent data leads to conflicting insights and damages trust in the data. For example, if a product is listed at £100 in an e-commerce system but £120 in the inventory management system, it confuses both employees and customers.

Example:
A customer is recorded with two different spellings of their name ("Jane Doe" and "Janet Doe") in separate systems. This inconsistency causes duplicate records and inaccurate customer segmentation.

How to improve consistency:

• Implement data integration tools to ensure all systems are updated simultaneously and share consistent information.
• Use master data management (MDM) solutions to create a single source of truth for key data entities, such as customer or product information.
• Standardise data formats, naming conventions and structures across systems.

4. Timeliness

Timeliness measures how current and up-to-date data is and whether it is available when needed. Stale or outdated data can lead to irrelevant decisions or missed opportunities.

Why it matters:
In industries where conditions change rapidly—such as stock trading or e-commerce—real-time or near-real-time data is essential for making accurate decisions. Delayed data creates a lag that can disrupt planning or responses.

Example:
An airline uses outdated weather data to plan flight schedules, leading to delays and dissatisfied passengers. Similarly, a marketing team relying on outdated customer preferences might send irrelevant offers that fail to engage recipients.

How to improve timeliness:

• Automate data updates for dynamic systems that require frequent refreshes (e.g., inventory tracking or financial reporting).
• Schedule periodic reviews of less dynamic datasets to ensure they remain current.
• Implement real-time data pipelines where applicable, especially for industries like logistics, retail, or finance.

5. Validity

Validity ensures that data adheres to predefined formats, rules, or standards. Invalid data cannot be used effectively and often causes errors in downstream processes or analyses.

Why it matters:
Invalid data creates inefficiencies and wastes resources. For example, if a form accepts phone numbers in an incorrect format, it results in unusable entries that disrupt communication or automation workflows.

Example:
A contact form allows users to submit "1234" as a phone number. This invalid entry renders the phone number field useless, as it does not meet the required format for communication.

How to improve validity:

• Enforce validation rules at the point of data entry. For instance, require email addresses to follow standard syntax or ensure dates are entered in a consistent format.
• Use software tools to detect and flag invalid data before it is stored or processed.
• Conduct routine data cleaning to identify and correct invalid entries.

6. Accessibility

Accessibility ensures that data is available to authorised users when and where they need it. At the same time, accessibility must be balanced with security and privacy considerations to prevent unauthorised access.

Why it matters:
If key stakeholders cannot access the data they need, decision-making and operations are delayed. However, if data is too accessible, it can lead to breaches or misuse.

Example:
A sales team cannot access the latest customer insights because the data is stored in siloed systems managed by IT. This lack of accessibility hinders their ability to tailor pitches or close deals.

How to improve accessibility:

• Implement role-based access controls to ensure only authorised users can access specific datasets.
• Centralise data storage in systems that are user-friendly and integrated with other tools used by employees.
• Regularly evaluate and update access permissions to align with evolving organisational needs and security policies.

Strategies for improving data quality

Improving data quality is not a one-time task, it is a continuous process that requires a combination of strategic planning, technology and cultural change within an organisation. Data quality issues can stem from various sources, such as human error, system inefficiencies or lack of standardisation. To address these challenges, organisations must implement robust frameworks and processes to ensure that data remains accurate, consistent and usable across its lifecycle.

Below, we outline a step-by-step strategy approach to improving data quality, providing detailed explanations and practical examples for each stage.

1. Implement data governance framework

Data governance is the foundation for managing data quality across an organisation. It provides clarity on who is responsible for maintaining data quality and how it is monitored and improved.

How it works:
Create a structured framework that defines roles, responsibilities and workflows for managing data. This includes assigning:

• Data owners: Individuals responsible for the overall quality of specific datasets.
• Data stewards: Team members who monitor and maintain data integrity on a day-to-day basis.
• Data custodians: IT professionals responsible for securely storing and processing data.

Example:
A healthcare provider might assign a data steward to oversee patient records, ensuring that medical histories, allergies and contact details are accurate and regularly updated.

Implementation tips:

• Develop policies outlining who can add, update or delete data.
• Use workflow tools to track approvals for changes to critical datasets.
• Set up regular governance meetings to review data quality metrics.

2. Implement robust data validation at entry points

Errors are easiest to prevent at the point of entry. By implementing strong validation rules, organisations can minimise incorrect or incomplete data from entering their systems.

How it works:
Use input validation techniques, including:

• Mandatory fields: Ensure critical information, such as email addresses or phone numbers, cannot be left blank.
• Format verification: Enforce rules for date formats (e.g., DD/MM/YYYY), numerical ranges, or email syntax.
• Reference list validation: Restrict entries to predefined values, such as product categories or country names.

Example:
A retail company uses a web form for customer sign-ups. By requiring email addresses to follow a valid format and phone numbers to match country-specific rules, the system prevents incorrect data from entering the CRM.

Implementation tips:

• Integrate validation rules into online forms, CRM systems and data entry software.
• Train staff to review and validate data before submitting it into the system.
• Use dropdown menus and pre-defined lists to minimise input errors.

3. Schedule regular data cleansing and remove duplication

Even with strict validation rules, errors and redundancies can accumulate over time. Regular data cleansing ensures that datasets remain accurate, consistent and free of duplicates.

How it works:
Use data cleansing tools to identify and fix:

• Duplicate records: Consolidate duplicate entries for the same customer or supplier.
• Inconsistencies: Correct mismatched formats or misspelled names.
• Outdated information: Remove obsolete records, such as inactive accounts.

Example:
A marketing team finds that multiple entries exist for the same customer across different systems. Using a cleansing tool, they merge duplicates into a single, accurate record.

Implementation tips:

• Schedule data cleansing activities quarterly or monthly, depending on the volume of new data.
• Combine automated cleansing tools with manual reviews for high-priority datasets.
• Use enrichment services to update missing or outdated information, such as postal addresses or demographic details.

4. Utilise data profiling and monitoring tools

Data profiling involves analysing datasets to identify patterns, anomalies and quality issues, while monitoring tools provide continuous oversight to detect and resolve issues in real time.

How it works:
Data profiling tools analyse attributes such as completeness, accuracy and format compliance. Monitoring tools alert teams when anomalies arise, such as missing values or unexpected spikes in duplicate records.

Example:
A financial institution uses data profiling software to identify that 20% of customer income fields are blank. The team creates a targeted campaign to update these records.

Implementation tips:

• Deploy tools like for profiling and monitoring.
• Set up automated alerts to notify stakeholders of emerging data quality issues.
• Use monitoring dashboards to track data quality metrics over time.

5. Standardise data formats and create a data dictionary

Standardisation ensures uniformity across systems, making it easier to analyse and share data without confusion.

How it works:

• Create a data dictionary that defines each field, its purpose and the required format (e.g., "Date of Birth: DD/MM/YYYY").
• Ensure all departments adhere to consistent naming conventions, capitalisation rules and measurement units.

Example:
A logistics company standardises its "delivery status" field across systems, using the values "In Transit," "Delivered," and "Cancelled." This eliminates discrepancies caused by variations like "delivered" or "Delivered-Complete."

Implementation tips:

• Distribute the data dictionary to all employees who handle data.
• Include rules for capitalisation, abbreviations and data units (e.g., kilograms vs. pounds).
• Use automated formatting tools to enforce rules during data entry.

6. Adopt Master Data Management (MDM)

Master Data Management involves creating a centralised "single source of truth" for critical business data, such as customer, product and supplier records.

How it works:
MDM consolidates data from multiple systems into one authoritative dataset. This prevents inconsistencies and ensures that all departments work with the same information.

Example:
A manufacturing firm consolidates supplier records from its procurement and accounting systems into a single MDM platform, eliminating duplicate entries and discrepancies in payment terms.

Implementation tips:

• Identify core data domains (e.g., customer, product, supplier) that require centralisation.
• Implement tools like Boomi.
• Assign a dedicated team to maintain and update the master data.

7. Perform regular data quality audits

Audits provide a thorough examination of your data’s health, uncovering hidden issues and areas for improvement.

How it works:
Combine automated tools (to scan for anomalies and duplicates) with manual reviews (to validate accuracy and completeness).

Example:
A telecom company audits its billing system and discovers that 5% of customer accounts have incorrect charges due to data entry errors.

Implementation tips:

• Document audit findings and track improvements over time.
• Prioritise high-impact datasets for more frequent audits.
• Use audit results to refine data governance and collection processes.

8. Train employees on data quality best practices

The human element plays a crucial role in maintaining data quality. Training employees helps them understand their responsibilities and the broader impact of their actions.

How it works:
Provide training on topics such as:

• Proper data entry techniques.
• Identifying and reporting errors.
• Using tools for validation and cleansing.

Example:
A retail chain conducts workshops for store managers, teaching them how to validate inventory data before submitting it to headquarters.

Implementation tips:

• Include data quality training as part of onboarding for new employees.
• Conduct refresher sessions periodically to reinforce best practices.
• Use real-world examples to demonstrate the consequences of poor data quality.

9. Employ Artificial Intelligence and Machine Learning

AI and machine learning can process large datasets to detect anomalies, predict errors and recommend corrective actions.

How it works:

• Use AI to identify patterns that indicate data quality issues, such as sudden spikes in duplicates or outliers.
• Train machine learning models to improve over time as they process more data.

Example:
An e-commerce company uses AI to detect and flag fraudulent customer accounts based on unusual patterns in order histories.

Implementation tips:

• Start with AI tools that offer pre-built models for anomaly detection.
• Integrate AI-driven insights into your existing monitoring systems.
• Regularly retrain models using updated datasets.

Conclusion

What would your organisation achieve if you could trust every piece of data to be accurate, complete and reliable? The answer is simple: better decisions, greater efficiency and stronger relationships with customers and stakeholders. However, reaching this level of data quality is no easy task, it requires focus, expertise and the right tools.

This is where we come in. Our services are designed to help you address your organisation’s data challenges, from resolving inaccuracies and inconsistencies to implementing systems that ensure data is always timely and valid. By working with us, you gain more than just clean data, you gain confidence in every decision you make, assurance in meeting regulatory requirements and a foundation for building long-term success.

Don’t let poor-quality data hold your organisation back. Let’s discuss how we can support your journey towards better data quality, so you can focus on achieving your goals with clarity and certainty. Contact us today!

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