The Greek philosopher Heraclitus once said, “Everything flows, and nothing abides; everything gives way, and nothing stays fixed.” In other words, “The only constant is change.” Time marches on. In the evolving landscape of data management, the frameworks we use to define quality are not static. While many data professionals know the traditional six or eight dimensions of data quality, the modern data environment demands a more comprehensive approach. This article proposes an expanded model of ten core dimensions, arguing that this updated framework is essential for building a truly effective and trustworthy data quality program. By adding Security and Regulatory compliance to the established list, we can move beyond vague assumptions about data being “good” or “bad” and instead establish a measurable rubric that aligns data integrity with contemporary business needs and legal obligations.
If you’ve been a long-term data quality professional, you’ve seen the eight dimensions of data quality. Some might have had that at six , but I believe there are now 10 dimensions. They are:
Validity
Completeness
Uniqueness
Accuracy
Consistency
Authority
Timeliness
Relevance
Security
Regulatory
Not All Data Is Created Equal
Why do we even care about the dimensions of data quality? Why are they relevant? Because these dimensions give us an approach, a rubric for understanding whether or not our data is of good quality or bad quality. 95% to 99% of organizations have no clue about the quality of their data other than they know it’s bad. Or some will say — and this is the harder group to work with — “Oh, it’s great,” yet they have no metrics to prove it.
When building a data quality program, KPIs are done based on the below dimensions of data quality so that there is transparency in these dimension qualities. Are they getting better or are they getting worse? If better, how much have we improved? It should be noted that not every business has all 10 dimensions of data quality. Some do; some don’t. For example, regulatory PII, or personally identifiable information, is a huge topic for businesses doing data management. There are a bunch of regulations around it. Data that is not PII probably won’t fall into this regulatory component. However, just about every business will profit from ensuring these 10 dimensions are as strong as they can be.
10 Types Dimension of Data Quality The 10 Dimensions of Data Quality
The Strategic Approach
Phase Dimension Focus Question Primary Action 1. Foundation Validity Is the data formatted correctly? Implement syntactic validation rules. Completeness Is the data there? Profile for & mandate missing values. Uniqueness Is it duplicated? Deduplicate records. 2. Core Quality Accuracy Is the data correct? Validate against trusted sources. Consistency Does it agree elsewhere? Reconcile reports across systems. Timeliness Is it available when needed? Define and monitor data SLAs. 3. Governance Relevance Do we need this data? Align data collection with business goals. Authority Which source is the truth? Designate Systems of Record. 4. Constant Security Is the data protected? Implement access controls & encryption. Regulatory Is its use compliant? Apply privacy & retention rules.
Validity
This dimension is a classic one. I almost always go after validity and conformity along with accuracy early on. This dimension measures whether data conforms to a defined set of rules, syntax, or format. It answers the critical question, “Does the data comply with required structural and formatting rules?” It can be considered as the “syntax” of data quality. Data can be valid without being accurate (e.g., a perfectly formatted phone number can belong to the wrong person), but invalid data is almost always problematic because it fails the first basic check for usability. One thing to keep in mind: validity is not synonymous with accuracy.
Invalid data can create system errors and cause failed processes. When merging data from different sources, invalid formats can prevent successful matching and integration. For example, one system may store a date as MM/DD/YYYY and another as DD-MM-YYYY, causing conflicts. Invalid data requires cleansing, rework, and manual intervention, which consumes time and resources, which aren’t free. Entering data only to have it rejected by a system due to an invalid format (like a zip code) creates friction and user frustration.
Validity is typically measured by the percentage of data values that conform to their defined rules:
Validity Score = (Number of Valid Values / Total Number of Values) × 100
How to define validity
Data Type: Is the value the correct type? (e.g., text, number, date).
Format Mask: Does the value follow a specific pattern? (e.g., Phone Number: (XXX) XXX-XXXX, SSN: XXX-XX-XXXX).
Regular Expressions (Regex): A powerful sequence of characters that defines a search pattern for validating strings (e.g., email addresses, URLs).
Domain/Range Constraints: Does the value fall within an accepted list or range? (e.g., State_Code must be a valid USPS two-letter abbreviation; Age must be a number between 0 and 120).
Check Digits: A form of validation used for numerical data (like credit card numbers) to detect errors.
Validity in the Context of Other Dimensions
It’s crucial to distinguish validity from accuracy:
Validity: “Is the phone number formatted correctly as (XXX) XXX-XXXX?” (The syntax is correct).
Accuracy: “Is that phone number the correct phone number for this specific customer?” (The semantics are correct).
A phone number can be valid but inaccurate (it’s the number for the customer’s previous workplace).
Validity is the gatekeeper of data quality. It ensures that data is structurally sound and follows the basic rules necessary for it to be processed and stored correctly in a data warehouse. It forms the foundation of other, more complex dimensions of data quality.
How to Improve Validity
1
Define Standards
Clearly define the valid formats, patterns, and domains for all critical data elements.
2
Implement Validation at the Point of Entry
The most effective way to ensure validity is to prevent invalid data from entering the system in the first place. Use input masks, drop-down menus, and real-time validation.
3
Profile and Cleanse Data
For existing data, use data profiling tools to scan databases and identify invalid values. Then, use data cleansing (scrubbing) tools to correct them, often by applying standardization rules.
4
Document Rules
Maintain a central glossary or data dictionary where the validity rules for each data element are documented and easily accessible to everyone.
Completeness
This dimension measures if a dataset contains all necessary data. It answers the simple but critical question, “Is there a value for every required field?” For example, if a data set contains a person’s work address but it’s missing the city and the state, then the address portion of the data set is not complete. A customer record needs a complete address. If something’s lacking, then the system has failed the completeness dimension.
However, “complete” doesn’t mean every single field in a database must be populated. Instead, it means that data is present for all fields defined as mandatory for a specific use case. A field can be legitimately empty (a NULL value) if it is not required.
Companies can ensure completeness by enforcing required fields in forms and applications. Drop-down menus and controlled vocabularies can prevent free-text errors. Tools like Talend Data Quality, Informatica Data Quality, and IBM InfoSphere Information Analyzer can scan databases and report missing values in key fields (NULL checks).
Completeness is primarily concerned with fields defined as essential. For example, a CustomerID is usually mandatory, while a Middle_Name might be optional. A NULL value in a mandatory field represents incomplete data. However, a NULL in an optional field is acceptable and does not harm completeness. Sometimes systems use default values (e.g., “N/A” or “Unknown”) to ensure completeness. While this technically fills the field, it’s a poor substitute for actual data and can harm other dimensions like Accuracy.
How to Improve Completeness
1
Assess
Start by profiling your most critical data assets to measure your current level of completeness.
2
Prevent
Define mandatory fields and implement validation at the point of entry.
3
Detect
Build automated checks into your data pipelines to identify incomplete data as it flows in.
4
Correct
Cleanse, enrich, and fix existing incomplete data. Quarantine bad data that can’t be immediately fixed.
5
Govern
Assign owners, perform root cause analysis, and report on progress to create a sustainable culture of data quality.
Uniqueness
This dimension is fundamental for creating a “single source of truth.” When uniqueness is violated, it leads to duplicate records, which corrupts reporting, analytics, and operational processes. We want to ensure that data is not duplicated within data sets and/or within applications. We don’t want to have duplicate customer records. When we do, we have problems. Customer records should not be needlessly duplicated across applications. Uniqueness mandates that each real-world entity (a customer record, product number, billing transaction, etc.) is represented only once within a dataset or database. It answers the critical question: “Are there duplicate records for the same real-world thing?”
Duplicate records can skew metrics. Two records for the same customer inflates customer count, distorts sales figures, and makes accurate data analysis hard, if not impossible. Duplicate records lead to wasted resources. A company might send multiple marketing catalogs to the same person, ship two of the same product to the same address or have support agents working from different versions of a customer’s record. Contacting a customer multiple times due to duplicate records is annoying and erodes trust. It makes a company look disorganized.
Uniqueness is the foundation for establishing relationships between tables in a database (via primary and foreign keys). Duplicates break these relationships while corrupting data integrity. It is a cornerstone of data trustworthiness, ensuring each entity is represented by a single, authoritative record. It enables reliable operations, accurate reporting, and effective decision-making. Without it, data loses its fundamental integrity, a problem that will reverberate throughout an entire organization.
How to Measure Uniqueness
Uniqueness is measured by analyzing datasets for duplicate entries. The process involves:
Identifying a Key: Determine which field or combination of fields should be unique for each record (e.g., CustomerID, Email Address, or a composite key like FirstName + LastName + PostalCode).
Profiling and Counting: Use data profiling tools or SQL queries to count the number of duplicate values based on that key.
Calculating the Metric:
Duplicate Rate = (Number of Duplicate Records / Total Number of Records) × 100
The goal is for this rate to be as close to 0% as possible.
How to Improve Uniqueness
1
Prevent
Define business keys and enforce unique constraints at the database level where appropriate. Implement real-time search in UIs.
2
Identify
Profile your data for both exact and fuzzy duplicates. Use algorithms like Jaro-Winkler and Soundex for name matching.
3
Cleanse
Standardize data (lowercase, remove punctuation) before running matching algorithms to improve accuracy.
4
Resolve
Establish a clear “merge” process to combine duplicate records into a single golden record without losing data.
5
Monitor & Govern
Automate regular uniqueness checks. Perform root cause analysis to fix the sources of duplication and assign stewardship for ongoing management.
Accuracy
According to their IBM article, What is data accuracy?
So, if a real person with a social security number said their social security number is 99999, that would be an inaccurate social security number. So, we need our data to be accurate, meaning it must reflect real world value.
The best way to ensure accuracy is to prevent errors at the source. Companies should implement validation rules and input masks at the point of data entry to prevent simple errors, such as preventing letters in a phone number or social security number field. They can also automate data entry from reliable sources (e.g., scanning forms with OCR) to reduce human error.
Data cleansing is paramount as well. Regularly running data cleansing routines can correct inaccuracies identified in measurements. This can automatically standardize addresses and remove duplicates. Staff should be trained in the importance of data quality as well as on the impact inaccurate data has on their job and the company overall.
In summary, measuring accuracy is a process of comparing data to a verified source of truth, typically through a combination of sampling, automated checks, and business logic rules. The result is a metric that quantifies how reliably your data reflects reality.
How to Improve Accuracy
1
Prevent
Define business keys and enforce unique constraints at the database level where appropriate. Implement real-time search in UIs.
2
Identify
Profile your data for both exact and fuzzy duplicates. Use algorithms like Jaro-Winkler and Soundex for name matching.
3
Cleanse
Standardize data (lowercase, remove punctuation) before running matching algorithms to improve accuracy.
4
Resolve
Establish a clear “merge” process to combine duplicate records into a single golden record without losing data.
5
Monitor & Govern
Automate regular uniqueness checks. Perform root cause analysis to fix the sources of duplication and assign stewardship for ongoing management.
Consistency
This dimension refers to the absence of conflicting or contradictory information within the same dataset or between different datasets. Data is consistent when values are uniform and compatible across various systems and sources. Data entries describing the same entity (e.g., a customer’s address, date of birth, or product code) should match across all records and company databases. Consistent data avoids discrepancies such as varying formats, misspellings, or outdated values causing confusion or errors. This dimension does not completely guarantee accuracy, but it does ensure the data organization-wide doesn’t contradict itself.
Normally, I don’t go after consistency first. I wait on this dimension. The same customer should have one customer number across all applications. If one application shows a customer number of “12345” and another one shows “67890” for the same customer, the data fails the consistency dimension test. This dimension is usually in phase two or phase three of the process. As the organization matures, this dimension becomes important because it really forces the standardization and harmonization of data.
How to Measure Consistency
Consistency can be measured by checking for conflicts or mismatches within or across datasets, using data validation rules or reconciliation processes. Consistency is essential for building trust in data by ensuring that information is reliable and harmonious across different systems and timeframes.
How to Improve Consistency
1
Define
Create a Business Glossary and document data standards for formats and approved values.
2
Prevent
Architect around a Single Source of Truth and consider master data management (MDM) for critical master data.
3
Detect
Codify and automate data quality rules that check for intra-record, cross-field, and cross-system consistency. Use profiling tools to scan for violations.
4
Correct
Use ETL/ELT transformation logic to standardize data. Establish a process for stewards to investigate and resolve inconsistencies.
5
Govern
Implement change management and root cause analysis. Assign owners and stewards, and monitor consistency metrics over time.
Timeliness
This dimension answers the critical question: “Is the data available when we need it and is it sufficiently current?” Timeliness is a combination of two related but distinct concepts. These are:
Currency: How up to date is the data with the real-world entity or event it represents? This is often measured as the gap between the time the data was last updated and the present moment (e.g., “The customer address data is 30 days old”).
Availability: The delay between when a real-world event occurs and when the data representing that event is available for use in the system. It’s about the speed of data delivery (e.g., “Sales data is available in the dashboard with a 24-hour delay”).
Data can be current but not timely if it’s not available when a decision needs to be made. Conversely, data can be available instantly but not current if the sources aren’t updated frequently.
Timeliness is extremely important because the value of data often degrades over time, sometimes incredibly quickly. Making a marketing decision based on a customer’s last-minute data point rather than last month’s purchase figures is much more difficult in terms of data utilization, but offering a coupon for something the customer might be in the process of purchasing is probably more useful than providing them with a twenty percent off coupon for something they might have already purchased weeks ago.
Real-time decisioning requires data with extremely high timeliness. Out-of-date information can hurt an operation. A delivery driver using a navigation app with outdated traffic data will be inefficient. A customer service agent looking at a customer’s record from six months ago cannot resolve a current issue effectively. This might also adversely affect regulatory compliance. Many regulations require the reporting of data to be within a specific timeframe. Failure to do so can result in expensive financial penalties.
I worked with a client once who had a third-level data warehouse — that’s a data warehouse receiving data from another data warehouse, the second level data warehouse, which is receiving data from the first level data warehouse, i.e., the original one. There are a lot of different styles of data warehouse architectures available to you, many that work just fine. However, a third level data warehouse fails miserably every time. To be quite frank, it’s pretty much the definition of bad data warehouse architecture. When you have nested data warehouses like this, the third level data warehouses will take in all the constraints of the second data warehouse, which will take in all the constraints of the first data warehouse, making timeliness a huge issue. This methodology will always end badly.
Hot to Measure Timeliness
You can measure timeliness through several key performance indicators (KPIs), including data freshness, data latency, time lag measures, and time-to-insight. Data freshness measures the age of the data at the moment it is accessed. The fresher the data, the timelier it is. For example, calculating the time difference between the most recent event timestamp and the current timestamp gives the data freshness.
Data latency measures the delay from data generation to its availability for processing or analysis. Lower latency means higher timeliness. Similar to latency, time lag measures the actual delay or lag in data capture and delivery relative to expected or real-time requirements. Time-to-insight is the total elapsed time from data generation to actionable insight, which includes data latency plus the time for processing and analysis. Timeliness is context-dependent: data might be valid but not timely if it arrives too late. For example, late order data may be accurate but no longer useful if it misses operational deadlines.
How to Improve Timeliness
1
Define Requirements
Establish clear business Service Level Agreements (SLAs) for data freshness and latency.
2
Architect for Need
Choose the right pattern (batch, micro-batch, streaming) based on the SLA.
3
Build Efficiently
Optimize pipelines with incremental loads, parallel processing, and efficient code.
4
Monitor Relentlessly
Implement automated checks for data freshness and pipeline performance. Set up alerts for SLA violations.
5
Govern & Improve
Foster a DataOps culture, conduct post-mortems, and continuously tune your system for better performance.
Relevance
This dimension measures the degree to which data is applicable and helpful for the specific context and purpose for which it is being used. It answers the critical question: “Does this data matter for my specific business need or decision?” Unlike the Validity and Accuracy dimensions, which are intrinsic properties of the data itself, relevance is extrinsic, i.e., it is determined entirely by the context of its use. Data can be perfectly accurate, timely, valid, and complete but still be irrelevant to the task at hand.
Relevance is important because irrelevant data creates “noise” that obscures the meaningful “signal.” It wastes storage, processing power, and, most importantly, it wastes an analyst’s time because he or she must sift through the data to find out what matters. Good business decisions require the most pertinent information possible. Including irrelevant data into models can lead to analysis paralysis, which can distract from any core insights. Presenting users with unneeded data creates confusion. It might also reduce trust in a reporting system.
In business, the bottom line is always the bottom line. Collecting, storing, and processing data is not free. Adding irrelevant data not only compromises analytical models, but it can also add cost to an IT system. Ensuring data is relevant helps avoid expenses associated with maintaining useless and/or obsolete information.
It’s helpful to distinguish Relevance from Completeness:
Completeness: “Do we have all the values for data attribute X?”.
Relevance: “Do we even need data attribute X for this purpose?”
All-in-all, Relevance is the dimension that connects data to business value. It ensures the right data is available for the right purpose, preventing wasted resources and enabling sharper, more focused insights and decisions. It reminds us that not all data is created equal, and its value is determined by the problem it helps solve.
How to Measure Relevance
Measuring Relevance is more subjective than measuring Accuracy or Uniqueness, but it can be assessed through:
User Feedback: The most direct method. Surveying or interviewing data consumers on whether the data provided meets their needs.
Usage Metrics: Analyzing data consumption.
High Relevance: Data that is frequently accessed, queried, and included in reports and dashboards.
Low Relevance: Data that is never or rarely accessed.
Business Impact Analysis: Evaluating whether the data has a tangible connection to key business outcomes, KPIs, or decisions.
How to Improve Relevance
1
Engage with Data Consumers
The single most important step. Regularly communicate with the business users and analysts who rely on the data to understand their evolving needs and challenges.
2
Define Clear Business Objectives
Before collecting or analyzing data, clearly articulate the business question you are trying to answer. This acts as a filter for relevance.
3
Data Cataloging & Governance
Maintain a robust data catalog that includes business glossaries and data dictionaries. These tools should define what each data element means and for what business purposes it is intended.
4
Conduct Relevance Audits
Periodically review datasets and reports with stakeholders to ask: “Do we still need this data? Why? What decisions does it inform?” This helps retire obsolete data.
5
Implement Tiered Storage
Not all data needs to be in a high-performance, expensive database. Archive historical data that is kept for compliance reasons but is no longer relevant for daily operations.
Authority
This dimension assesses whether the data comes from a verified, reputable, and official source that is recognized as the definitive originator or owner of the information being assessed. It answers the critical question: “Can we trust the source of this data?” Data with high authority is sourced from providers who are subject matter experts, official entities, or have a proven track record of reliability. Data with low authority might come from an unverified or unofficial origin, making it risky to use for decision-making.
Authority builds trust. Using data from an authoritative source increases confidence in any analysis, report, or decisions derived from a set of data. It advances the idea that analysts are getting closer to the truth while also, just as importantly, reducing data risk. Basing decisions on data from an unofficial or unreliable source can lead to faulty conclusions, financial losses, and, just as importantly, reputational damage. In many industries, regulations mandate that certain data must be sourced from specific authorized bodies. This dimension also helps resolve discrepancies because, when two data sources conflict, the one with higher authority wins out. That one is typically considered the “single source of truth.”
“Is the quality of the data backed by accumulated authority?” is one question to ask about this dimension. What do I mean by accumulated authority? An example of this would be in an organization, you would create a certification process for data that actually measures data quality and does it programmatically. Maybe you even have a data store? So existing processes, practices and data are inspected, corrected, and then a data steward or an owner certifies the data has been checked and is correct.
Authority is often a qualitative measure, but it can be assessed through a set of criteria:
Source Certification: Is the source certified by a recognized standards body or industry group?
Provenance: Can you trace the data’s origin and its journey through systems? A clear lineage enhances perceived authority.
Reputation: Does the source have a long-standing reputation for accuracy and reliability?
Official Mandate: Is the source the officially designated producer of this data?
Transparency: Is the source transparent about its data collection and processing methods?
Hot to Measure Authority
Data Catalog: (e.g., Alation, Collibra, Atlan) The central tool for documenting SOR designation, owners, and gathering user ratings.
Data Lineage Tools: (e.g., MANTA, Solidatus, integrated lineage in catalogs) Provide the evidence for the lineage metrics.
Data Governance Platform: Helps manage the policies, owners, and access controls.
Usage Analytics Tools: (e.g., Snowflake’s Access History, Tableau Usage Stats) Provide the data for adoption rate metrics.
How to Improve Authority
1
Identify and Declare
For each key data entity, formally designate its System of Record (SOR).
2
Centralize and Curate
Build a Single Source of Truth (SSOT) in your data warehouse by ingesting data from the SORs. This becomes the authoritative source for analytics.
3
Catalog and Lineage
Implement a Data Catalog to help users find authoritative sources. Use Data Lineage to prove the data’s origin.
4
Govern and Control
Establish a Data Governance Council to make decisions. Control access to prevent the creation of unofficial data extracts.
5
Culture and Training
Communicate the importance of authority and train users on how to find and use approved data sources.
Security
This dimension is a new one and it refers to the protective measures and controls implemented to ensure that data is not accessed, used, modified, or disclosed in any unauthorized or unintended manner. It answers the critical question: “Is our data protected from unauthorized access and corruption throughout its lifecycle?” It encompasses the protection of data both at rest (in databases, data warehouses) and in transit (moving over a network).
Many companies grapple with this dimension. An organization may have security classifications like “public”, “sensitive”, “secret”, or “top secret”. So, for example, “public” information is the thing companies don’t care much about. Users can download it. It’s fine that people know this. “Sensitive” is something that the company really doesn’t want to make public, but it’s not that relevant. It’s not damaging in any way. “Secret”, however, could be a trade secret, something that it’s critical to the organization. Research and development data fits into this. “Top secret” is data that, if it is release, would put the company in deep, deep trouble. A customer list would be a great example of “Top secret” information.
So, if you are a large consulting firm, one of those multi-billion-dollar entities, the last thing you want is to have your customer list along with contact names and addresses released to the public. This is about as top secret as it gets. So, security classifications are vital and highly needed.
Data security preserves integrity and accuracy. A security breach threatens the unauthorized modification of data directly, which could corrupt it. It also ensures confidentiality. Many dimensions of quality are meaningless if data is seen by the wrong people. Leaking sensitive customer data (PII), financial records, or intellectual property is a failure of fitness for purpose, as it destroys trust and violates legal and/or regulatory agreements.
Security attacks like Ransomware or Denial-of-Service (DoS) attacks make data unavailable. If data cannot be accessed when needed for decision-making, its timeliness and usefulness drop to zero. Users, customers, and partners must trust that their data environment is secure. Without this trust, they will be reluctant to provide accurate data or use the data for critical decisions, undermining the entire data ecosystem.
Laws like GDPR, CCPA, HIPAA, and others mandate strict security controls to protect personal data. Failure to implement security is a violation of law and a failure of data compliance, a key aspect of fitness for use.
How to Improve Security
1
Classify
Label data by sensitivity (e.g., Public, Confidential, Restricted)
2
Govern
Assign owners and a governance council to set policies that balance security and quality needs.
3
Control Access
Implement granular, role-based access control (RBAC). This is the most important step.
4
Obscure Sensitive Data
Use dynamic data masking and tokenization for non-production environments and for users who don’t need to see full values.
5
Protect Data
Communicate the importance of authority and train users on how to find and use approved data sources.
6
Monitor and Audit
Log all data access and track lineage to detect breaches and investigate quality issues.
7
Train Users
Educate your team on how to use data both securely and effectively.
Regulatory
This dimension is also a new one. It measures the extent to which data collection, storage, processing, and usage comply with relevant laws, industry regulations, and internal organizational policies. So, something like PII, social security number, that’s going to hit into GDPR or CCPA if you’re in the United States. This dimension answers the critical question: “Is our handling of this data legally compliant?” Unlike accuracy or completeness dimensions, which are about the data’s intrinsic properties, regulatory compliance is about the context and governance of how the data is managed throughout its lifecycle.
This dimension really talks about the degree to which the data is useful for its intended purpose. So, the data could be accurate, it could be well defined, it could have the right format, but it just isn’t relevant, it just doesn’t meet our requirement standards. I’ve seen this many times and that’s why I share that the data can meet the other nine dimensions but if it’s not relevant to meet our needs then that’s a problem.
How to Improve Regulatory
1
Discover & classify
Identify the regulations you must follow and the sensitive data you hold. You can’t protect what you don’t know about.
2
Govern & Policy
Assign clear ownership (Data Owners/Stewards). Write clear policies for privacy, retention, and incident response.
3
Automate and Enforce
Use technology to your advantage. Automate data discovery, access controls, masking, and regulatory checks within your pipelines.
4
Monitor & Audit
Continuously track compliance through lineage, logging, and regular audits. Be ready to prove your compliance to regulators.
5
Cultivate and Train
Foster a company-wide culture where data protection and regulatory adherence are seen as everyone’s responsibility.
The Dimensions of Data March On…
The expansion from eight to ten data quality dimensions reflects a necessary maturation in the field of data quality, acknowledging that high-quality data is not just about technical correctness but also about business value, security, and legal compliance. This comprehensive framework—spanning from foundational aspects like Validity and Uniqueness to critical governance-oriented dimensions like Authority, Security, and Regulatory—provides the necessary structure to transform data from potential liability to a strategic and trusted asset. Ultimately, by systematically measuring and improving across these ten dimensions, organizations can escape the cycle of uncertainty and build a data quality program that is transparent, accountable, and capable of driving confident decision-making.
To paraphrase Heraclitus, “The only constant is change.” Time marches on, as do the data quality dimensions. With both the complexity and value of data increasing substantially over the past few years, this is an effort that will pay off while paving the way for a far more valuable destination: a trusted, authoritative, and actionable single source of truth.
