In today’s digital economy, data is the fuel that powers decision-making, innovation, and transformation. But not all data is valuable — only high-quality data can be trusted and acted upon.

At DataEnabler.org, we guide organizations across Saudi Arabia in building robust data quality frameworks that align with national priorities like NDMO policies and Vision 2030 initiatives. At the heart of these frameworks lie the Data Quality Dimensions — a critical concept every data leader must understand.

What Are Data Quality Dimensions?

Data Quality Dimensions are standardized criteria used to assess, monitor, and improve the condition of data. Think of them as lenses through which you evaluate how “fit-for-purpose” your data is.

While there are many variations, global standards such as DAMA-DMBOK and NDMO’s Data Quality Policy commonly recognize the following core dimensions:

1. Accuracy (الدقة)

Definition: The degree to which data correctly describes the "real-world" object or event it represents.
Example: A patient’s blood type recorded as AB instead of O is an inaccuracy.
Measurement:

• Compare data values with trusted sources (e.g., master records)

• Use error rate formulas

Formula: (Number of accurate records / Total records) × 100%

2. Completeness (الاكتمال)

Definition: The extent to which expected data is present.
Example: If a customer’s profile is missing a national ID or phone number, it’s incomplete.
Measurement:

• Calculate percentage of non-null fields in critical columns

Formula: (Filled fields / Total expected fields) × 100%

3. Consistency (الاتساق)

Definition: Data is the same across systems or formats where it appears.
Example: An employee’s department in HR records is "Operations", but in payroll, it says "Finance".
Measurement:

• Cross-system validation rules

• Business rules and reconciliation reports

4. Timeliness (الوقتية)

Definition: The extent to which data is available when it is needed and reflects the latest updates.
Example: A COVID-19 test result updated in the system 5 days late is untimely.
Measurement:

• Track data latency or refresh intervals

• Define data freshness KPIs

Formula: (Records updated within target time frame / Total records) × 100%

5. Uniqueness (التميز)

Definition: No duplicate records exist for the same entity.
Example: A patient registered twice under different spelling of the name.
Measurement:

• Run duplicate detection rules

• Match and merge techniques

Formula: (Total unique records / Total records) × 100%

6. Validity (الصلاحية)

Definition: Data conforms to the defined format, structure, or domain rules.
Example: An email address without “@” or a date in wrong format is invalid.
Measurement:

• Rule-based checks using data validation tools

• Regex, pattern matching, and domain lists

How to Measure and Improve Data Quality

Effective data quality measurement requires a framework that combines:

• Policies and standards (aligned with NDMO)

• Roles and responsibilities (e.g., Data Stewards)

• Tools and automation (profiling, cleansing, cataloging)

• Continuous monitoring via dashboards or DQ scorecards

Here’s a simple 4-step approach we recommend:

✅ Step 1: Identify Critical Data Elements (CDEs)

Focus on high-impact data fields used in reporting, compliance, or decision-making.

✅ Step 2: Define DQ Rules per Dimension

Specify what “accurate”, “complete”, or “valid” means for each field.

✅ Step 3: Perform Profiling & Baseline

Use tools (or manual queries) to measure current data quality levels.

✅ Step 4: Monitor and Govern

Establish a Data Quality Dashboard with trends, thresholds, and ownership.

Why It Matters for Saudi Organizations

For entities in Saudi Arabia, data quality is not just a best practice — it’s increasingly a regulatory obligation. The NDMO’s Data Quality Policy mandates government and semi-government entities to:

• Monitor the quality of their data assets

• Report compliance on key DQ metrics

• Support strategic initiatives with trustworthy data

Failing to meet these expectations can compromise digital transformation goals and delay national-level initiatives.

How DataEnabler.org Can Help

At DataEnabler.org, we provide:

• Data Quality Assessments using recognized dimensions

• Templates and scorecards for ongoing monitoring

• NDMO-aligned compliance frameworks

• Training and support for building DQ culture

Whether you’re just starting or looking to scale, we can help you transform your data into a reliable strategic asset.

📌 Final Thoughts

Data quality isn’t a one-time task — it’s a continuous discipline. Understanding and measuring data quality dimensions is the first step toward ensuring that your data drives the right decisions at the right time.

Good data builds great nations — and it starts with quality.