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data quality score

data_qual_scr
operations·Updated Jul 6, 2026

Definition

ISO-11179 Definition

A composite numeric score reflecting the overall quality of a data asset across dimensions such as completeness, accuracy, consistency, timeliness, and uniqueness, derived from automated data quality rule execution. Data quality scores are used in data catalog platforms, data observability tools, and data governance dashboards to prioritize remediation efforts and track quality improvement over time.

Standard Abbreviation

data_qual_scr

Category

operations

Production DDL — FACT_OPERATIONAL_EVENT

FACT_OPERATIONAL_EVENT.sql
CREATE OR REPLACE TABLE FACT_OPERATIONAL_EVENT (
    oprtnl_evnt_key  INTEGER       NOT NULL  -- surrogate key,
    evnt_id          VARCHAR(50)   NOT NULL  -- source event identifier,
    evnt_type_cd     VARCHAR(50)   NOT NULL  -- event type code,
    evnt_dt          DATE          NOT NULL  -- event date,
    evnt_ts          TIMESTAMP               -- event timestamp,
    fac_id           VARCHAR(20)             -- facility identifier,
    dept_cd          VARCHAR(20)             -- department code,
    unit_cd          VARCHAR(20)             -- unit code,
    mbr_id           VARCHAR(20)             -- member identifier,
    prvdr_npi        VARCHAR(10)             -- provider NPI,
    evnt_sts_cd      VARCHAR(20)             -- event status code,
    evnt_sts_dt      DATE                    -- status date,
    capacity_cnt     INTEGER                 -- capacity count,
    utilztn_pct      DECIMAL(5,2)            -- utilization percentage,
    rec_creat_dt     TIMESTAMP     NOT NULL  -- record created date,
    rec_updt_dt      TIMESTAMP     NOT NULL  -- record updated date
);

Standard Snowflake DDL for the canonical operations table. Convert to BigQuery or Databricks →

Why This Term Matters

Healthcare data terminology is foundational for any data engineer working in this industry. Precise understanding of standard terms enables accurate schema design, reduces downstream data quality issues, and ensures pipelines meet the regulatory and interoperability requirements imposed by HIPAA, HL7 FHIR, and CMS reporting frameworks. Without this foundation, even technically well-built pipelines produce data that fails validation when it reaches payers or regulators.

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