What’s data quality management? Process and services

Artificial intelligence is only as good as the data it learns from. As adoption accelerates, organizations are discovering a hard truth: Poor data quality management costs an average of $12.9 million per year and derails AI initiatives before they start.

Data quality management ensures your systems can access reliable, high-integrity data — the foundation for keeping your information AI-ready, training accurate models, and automating systems.

Let’s explore what data quality management is, why it’s essential for training AI, and how to build a process that scales up with your business.

What’s data quality management?

Data quality management (DQM) ensures your data is accurate, complete, consistent, and reliable enough for business use. A modernized process combines policies, data quality management tools, and workflows to maintain data quality from the moment it’s created until it’s used in analysis. As a data engineer, struggling with poor data quality processes means you’re constantly troubleshooting failures instead of building data-backed AI systems.

For example, a production-grade DQM process automates data checks, enforces standards, and reduces the manual work of data cleanup. Your data ecosystem needs a systematic, automated approach to DQM to support analytics and AI use cases.

Why is data quality management important?

Organizations invest in data quality management solutions because the alternative is chaos. Imagine your stakeholders not being able to trust the numbers — it would completely derail the organization’s future decisions based on predictive analytics. Strong data governance builds enterprise value and a data-driven culture. 

Here’s why it matters now more than ever:

• Trustworthy analytics and reporting: Without quality data, your dashboards won’t help you make choices. DQM is the step between gathering data and making accurate decisions.
• Reduced operational and financial risk: Mis-targeted marketing campaigns and flawed financial forecasts can misalign stakeholder expectations with reality.
• Improved customer experience: Personalization and customer support run on data. Inaccurate or incomplete customer data can lead to frustrating, irrelevant experiences.
• Support for regulatory compliance: Regulations like GDPR and CCPA have strict requirements for data accuracy and handling.

6 data quality management dimensions

To improve data quality, you first have to measure it. These six dimensions are the standard for evaluating the health of your data.

1. Accuracy

Measures: Does the data correctly reflect the real-world object or event it describes?

‍Why it matters: Inaccurate data is misleading. For AI models, inaccurate training data leads to flawed predictions and broken automations.

2. Completeness

Measures: Are there any gaps in the data? Is any critical information missing?

‍Why it matters: Incomplete data makes proper analysis impossible. Just as a customer record without an email address can’t be used for a marketing campaign, a sales record without a transaction date can’t be used for financial reporting.

3. Consistency

Measures: Is the data consistent across different systems? For example, is “United States” represented as “USA,” “U.S.,” or “United States” in different tables?

‍Why it matters: If you can’t join your sales with your marketing data because the customer IDs are in different formats, you can’t calculate marketing ROI.

4. Timeliness

Measures: Is the data up-to-date? How fresh is it?

‍Why it matters: Some data needs to be leveraged almost immediately using real-time processing, like a stock trading algorithm or customer support interactions. 

5. Validity

Measures: Does the data conform to a specific format or set of rules? Is a date in a valid date format?

‍Why it matters: Invalid data can break your data management flows. For example, a data pipeline might fail if it encounters a string in a field that’s supposed to be an integer.

6. Uniqueness

Measures: Are there duplicate records in the dataset?

‍Why it matters: Duplicate records can inflate metrics and skew your analysis. For instance, if you have three records for the same customer, you may be flooding their inbox with redundant marketing emails and promotions.

Key components of data quality management services

Effective data quality consulting — whether it’s in-house or external — should help you find, fix, and prevent data quality issues using these core methods.

Data profiling

Think of data profiling as the discovery phase. These tools scan your datasets to understand their structure, content, and quality. They identify things like null values, outlier values, and frequency distributions, giving you a baseline understanding of data health.

Data cleansing

Once you’ve identified the problems, it’s time to fix them. Data cleansing, otherwise known as data hygiene, is the process of correcting or removing errors, duplicates, and inconsistencies. This can involve standardization (e.g. converting all state codes to a two-letter format) and enrichment (e.g. adding missing zipcodes).

Data validation rules

Validation defines what “good” data looks like. A validation rule might specify that a certain field cannot be null, that a number must fall within a certain range, or that a string must match a certain pattern. These rules can flag bad data for review or prevent it from entering a system entirely.

Data governance frameworks

A data governance framework defines who is responsible for data quality, what the standards are, and what the process is for resolving issues. It establishes clear accountability which is critical for maintaining quality over time — especially when balancing governance versus self-serve analytics. 

Data quality monitoring tools

Data quality often degrades over time as new data is added or systems change. Monitoring tools continuously track data quality metrics themselves, detect anomalies, and alert teams when issues arise.

Benefits of data quality management

Investing in DQM delivers tangible, data-backed benefits:

• Improved accuracy and reduced errors. Your data is only useful when it’s fast, readily available, and accurate. Using a fully managed data movement platform like Fivetran helps you achieve world-class data operations, ensuring consistent, reliable pipelines that integrate with your data quality tools.
• Faster decision-making: As your organization scales, data management becomes even more crucial. Your team needs to be focused on higher-leverage decisions rather than fixing broken data.
• Enhanced scalability and efficiency: Manual data cleanup significantly reduces productivity with data scientists spending much of their time on data preparation rather than actual analysis. The best data quality software solutions dramatically reduce the time data teams spend on manual preparation. 
• Cost savings: Avoid the high costs of bad data — including flawed marketing decisions or incorrect revenue projections.

Best practices for data quality management

Behind any data management in your organization is a strategy that shapes it. Here are some best practices based on the problems you may encounter.

Data ownership

Without clear ownership, quality issues get passed around until they’re ignored. Every important dataset needs a named owner who’s responsible for its quality — from source to final report. While the owner doesn’t fix every issue themselves, they act as the main point of contact to troubleshoot problems.

Remove manual data checks 

By the time a data engineer manually finds an issue, it’s likely already impacted downstream reports. To prevent this from happening, implement automated validation and monitoring instead of relying on manual checks. Use tools to instantly profile new data, run validation rules against it, and monitor for anomalies. With Fivetran’s automated, fully managed connectors and built-in support for schema changes, eliminating manual data checks becomes easier than ever.

Constantly change data quality parameters

New data sources are added, APIs change, and business requirements evolve; your static set of quality rules quickly becomes obsolete. Instead, continuously audit and refine your quality rules, keeping in mind that standards are a moving target. Set a regular cadence to review your DQM rules, retire those with no relevance, and add new ones based on recent issues.

How Fivetran supports the data quality process

Manual ingestion scripts are a major hassle, not to mention time consuming and slow.

But with Fivetran’s 700+ fully managed connectors, organizations eliminate the risk of data quality and speed issues — providing a reliable, automated foundation for data movement and integration pipelines. Your team won’t need to worry about schema drift issues and can focus on consistent, reliable data movement that integrates with data quality software and observability tools.

Try Fivetran today and see how your pipelines and data quality can create more time for your engineers.

FAQs

How is the quality of data management ensured?

Data quality is ensured through people, processes, and technology. That often means establishing data governance policies with clear ownership and implementing automated tools for profiling, cleansing, and monitoring.

What are the 5 Cs of data quality?

While there are many dimensions, a common framework is: 

1. Clean (free of errors)
2. Consistent (uniform across systems)
3. Conforming (follows standards)
4. Current (up-to-date)
5. Comprehensive (no missing data)

What’s the difference between data governance and data quality management?

Data governance is the overall strategy and framework for managing data assets including policies, roles, and standards. Data quality management is the tactical execution of that strategy, focused on the specific processes and technologies used to measure and improve quality.

What are some key metrics for data quality?

Your key metrics will often align with the dimensions of data quality. Examples include error rate (accuracy), percentage of null values (completeness), number of duplicate records (uniqueness), and data latency (timeliness). 

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