What is Data Quality?

What is Data Quality?

Data Quality is the measure of how well data serves the purposes for which it is intended. High-quality data is accurate, complete, consistent, timely, and relevant. Poor-quality data contains errors, gaps, duplicates, outdated information, or inconsistencies that undermine its usefulness.

A simple way to think about it: if you cannot trust your data, you cannot trust any decision, report, or AI model built on top of it.

The Dimensions of Data Quality

Data quality is typically assessed across several dimensions:

• Accuracy: Does the data correctly represent reality? Is the customer's email address valid? Is the transaction amount correct?
• Completeness: Are all required fields populated? Are there missing records for certain time periods or regions?
• Consistency: Does the same data agree across different systems? If a customer's address is updated in the CRM, does it match in the billing system?
• Timeliness: Is the data current enough for its intended use? Yesterday's inventory levels are fine for weekly reporting but not for real-time order fulfilment.
• Uniqueness: Are there duplicate records? Multiple entries for the same customer inflate counts and distort analytics.
• Validity: Does the data conform to defined formats and business rules? Is the date in the expected format? Are product codes valid?

Why Data Quality Fails

Data quality problems rarely have a single cause. Common sources include:

• Manual data entry errors: Typos, inconsistent formatting, and incorrect selections in forms are among the most common sources of bad data.
• System integration issues: When data moves between systems through ETL processes, mapping errors, missing fields, or format mismatches can introduce quality problems.
• Lack of standards: Without clear rules for how data should be entered and maintained, different teams create data in different ways, leading to inconsistency.
• Data decay: Customer information changes over time. People move, change phone numbers, switch jobs. Data that was accurate when collected becomes stale.
• Mergers and migrations: When companies merge or migrate to new systems, data consolidation often introduces duplicates and inconsistencies.

Data Quality in the Southeast Asian Context

Businesses operating across ASEAN markets face heightened data quality challenges:

• Name formatting: Southeast Asian naming conventions vary significantly by country and culture. Indonesian names may not have family names, Thai names include royal titles, and Chinese names may appear in different orders depending on the system.
• Address standardisation: Address formats differ dramatically across markets, and many areas in Southeast Asia lack standardised postal addressing.
• Multi-language data: Product descriptions, customer communications, and operational data in multiple languages and scripts create consistency challenges.
• Marketplace data quality: Data from third-party marketplaces like Shopee, Lazada, and Tokopedia may have different quality standards than your internal systems.
• Mobile-first data: Southeast Asia's mobile-first internet usage means much data is collected through mobile interfaces, which can have higher error rates due to small screens and auto-correct issues.

Measuring Data Quality

You cannot improve what you do not measure. Practical approaches include:

• Data profiling: Automated analysis of datasets to identify patterns, anomalies, missing values, and statistical distributions.
• Quality scorecards: Define metrics for each data quality dimension and track them over time. For example: percentage of customer records with valid email addresses, percentage of transactions with complete address data.
• Automated validation rules: Implement checks in your data pipelines that flag or reject records that fail defined quality criteria.
• Regular audits: Periodically sample data and manually verify accuracy against source documents or real-world conditions.

Improving Data Quality

1. Establish data ownership: Every critical dataset should have a clear owner responsible for its quality.
2. Define quality standards: Document what good data looks like for each dataset, including required fields, valid formats, and acceptable value ranges.
3. Automate validation: Build quality checks into data entry forms, APIs, and ETL pipelines to catch problems at the point of creation.
4. Implement master data management: Create single, authoritative records for key entities like customers, products, and suppliers.
5. Train your team: Ensure everyone who creates or modifies data understands why quality matters and how to maintain it.
6. Monitor continuously: Track data quality metrics over time and investigate trends before small issues become systemic problems.