Data Quality

Data quality is the cornerstone of every successful data initiative. Without trustworthy data, even the most advanced analytics, machine-learning models, or dashboards will produce misleading insights and poor business decisions.

What Is Data Quality?

Data quality measures how well data serves its intended use. High-quality data is:

• Accurate – values correctly represent the real-world entities they describe.
• Complete – all required fields and records are present.
• Timely – data is up to date and available when needed.
• Consistent – no conflicting values across datasets or time periods.
• Unique – no unintended duplicates.
• Valid – conforms to allowed formats, ranges, and business rules.

Why Does Data Quality Matter?

Poor data quality costs organizations billions of dollars annually in re-work, incorrect decisions, regulatory fines, and lost customer trust. Teams that invest in data quality experience:

• More reliable analytics and ML outputs
• Higher developer productivity (less debugging bad data)
• Faster time-to-insight for decision makers
• Regulatory compliance and audit readiness

Dimensions of Data Quality

1. Accuracy

Accuracy evaluates whether each data value correctly reflects the real-world event or object. Verification can involve referential integrity checks, cross-system reconciliation, or manual sampling.

2. Completeness

Completeness measures missing values at multiple levels: field, record, and dataset. Null checks, row counts, and optional vs. required columns help surface gaps.

3. Timeliness

Timeliness considers refresh latency. Daily sales dashboards lose value if data arrives two days late. Service-level agreements (SLAs) define acceptable delays.

4. Consistency

Consistency asks whether two or more datasets that should agree actually do. Common consistency rules include type alignment, foreign-key enforcement, and cross-source comparisons.

5. Uniqueness

Uniqueness—or deduplication—ensures a real-world entity appears only once unless duplication is intentional (e.g., history tables).

6. Validity

Validity enforces domains, formats, and business constraints, such as ZIP codes having five digits or dates not being in the future for historical tables.

Practical Examples

Email Marketing List

Low-quality email addresses (misspellings, invalid domains) lead to bounces and spam flags. Applying regex validation and domain whitelisting improves accuracy and validity.

Financial Reporting

If revenue is recorded in EUR in one system and converted to USD in another without consistent FX rates, quarterly reports will misalign. Currency standardization and consistency checks solve this.

Operational Analytics with Galaxy

Engineers writing SQL in Galaxy can embed unit tests directly in queries to assert row counts or value ranges. The AI copilot suggests constraints (e.g., amount >= 0) and flags anomalies during code review, promoting higher data quality before dashboards break.

Best Practices for Ensuring Data Quality

• Define SLAs and ownership – every dataset needs a clear steward.
• Automate validation – integrate checks into ETL/ELT pipelines using tools such as dbt tests or Great Expectations.
• Monitor in production – use dashboards and alerts to catch drift.
• Implement data catalogs – document sources, lineage, and quality metrics; Galaxy’s upcoming catalog will auto-surface query endorsements.
• Adopt schema evolution policies – block or version breaking changes.
• Promote collaboration – share trusted SQL in Galaxy Collections so teams query from a single source of truth.

Common Misconceptions

“Data quality is the data team’s job alone.”

Reality: Quality is everyone’s responsibility, from engineers authoring ingestion code to analysts creating dashboards.

“Once cleaned, data stays clean.”

Reality: New sources, schema drift, or upstream bugs can re-introduce errors. Continuous monitoring is mandatory.

“Adding more data fixes quality issues.”

Reality: Volume does not equal quality. More bad data amplifies noise.

Working SQL Example

The following snippet illustrates how a Galaxy user might check for duplicate customer IDs and negative order amounts in PostgreSQL:

-- Assert uniqueness of customer_id
disable triggers all;
with dupes as (
select customer_id, count(*) as cnt
from sales.orders
group by customer_id
having count(*) > 1
)
select * from dupes;

-- Flag invalid amounts
select order_id, amount
from sales.orders
where amount < 0;

enable triggers all;

The AI copilot can auto-write these checks and suggest turning them into reusable tests stored in a Galaxy Collection.

Integrating Data Quality into the Development Lifecycle

Ingestion

Validate schema and run basic record-level checks before loading into the warehouse.

Transformation

Use dbt tests, SQL assertions, or Galaxy-embedded comments like -- EXPECT amount >= 0 to enforce constraints.

Consumption

Expose data quality metrics (row count, null ratio) alongside BI dashboards so stakeholders trust insights.

Real-World Case Study

A SaaS company noticed churn predictions were off by 20%. Investigation showed canceled_at timestamps were sometimes NULL due to an API bug. After adding completeness checks and blocking NULL inserts, prediction accuracy improved to within 2% of actual churn.

Actionable Takeaways

• Start small: pick one critical metric and add two automated tests.
• Track and publish data quality KPIs to drive accountability.
• Leverage developer-friendly tools (Galaxy, dbt, Great Expectations) to embed checks where engineers already work.