Your 7-step checklist for conducting data quality checks

What are data quality checks?

Data quality checks are tests that find and fix errors in your data, so it stays accurate, complete, consistent, and reliable.

• They spot issues like duplicates, gaps, and outliers.
• They use profiling, cleansing, and validation to clean data.
• They protect the choices you make from bad information.

Data is the lifeblood of modern business. At best, data quality checks make sure it drives smart choices in every task.

But poor data can lead you astray. So you may base big decisions on facts you cannot trust.

Picture this. You use marketing data to study buying habits and shape campaigns. If it holds errors and duplicates, your campaigns may miss the mark.

Gartner found that firms spend an average of $12.9 million annually on bad data. What’s more, data engineers spend much effort fixing it.

This is where data quality checks help. So they clean your data and keep its quality high.

Below is a seven-step checklist for data quality checks. As a result, you can keep top-quality data that moves your business forward.

How do data quality checks work?

Data quality checks test your data and find what drags it down. So they fix issues with accuracy, completeness, consistency, and reliability.

Many issues creep in as data moves through your systems. Some come by mistake, and some come by nature.

These checks use a few core methods to keep data clean. For example, they use data profiling, data cleansing, and data validation.

When do you need data quality checks: 7 common issues

An MIT Sloan study found that bad data costs about 15% to 20% of company expenses. So it pays to find the causes and stop them early.

Here are seven cases where data quality checks matter most:

1. Data inconsistencies

Inconsistent data shows up when sources clash or when entry varies by hand. So the same fact may look different in two places.

Quality checks find and fix these clashes. As a result, your data stays reliable and correct.

2. Missing or incomplete data

Gaps in data can block clear analysis. So they slow down good choices. By running checks, you can find and fill these gaps. As a result, your data stays complete and usable.

3. Duplicate information

Duplicate entries from many sources cause confusion. So they lead to errors and waste.

Data quality checks help you merge duplicate records. As a result, your data grows more accurate with less clutter.

4. Outliers and anomalies

Outliers and anomalies in data can skew your analysis and reports.

Data quality checks help you spot these odd points. So you can fix them and keep your data sound. Some teams even use machine learning to flag them faster.

5. Data validation failures

Data validation checks that data follows set rules. However, a few things can trip it up:

• Recent changes made by hand
• Duplicate data from another source
• New details that clash with old ones

Quality checks flag these failures. So you can fix them and keep your data reliable.

6. Data integrity issues

Data integrity keeps your data consistent across its whole life. So it stays sound from start to finish.

Data quality checks find and fix integrity gaps. As a result, you can trust your data at each stage.

7. Compliance requirements

Compliance matters for firms that hold sensitive data. For example, you could face fines for mishandling health or financial data under HIPAA or PCI certification.

Data quality checks help your data meet these standards. As a result, they save you from penalties and legal trouble.

7 steps in conducting data quality checks

Firms run quality checks in their own ways. Still, most follow a similar framework. So let’s walk through the seven-step checklist.

1. Define data quality goals

First, name the exact data quality goals you want. So map your business aims, data needs, and the quality level you require for smart choices.

2. Identify key elements

Next, find the data that matters most to your work. So you focus on what drives daily choices.

You can categorize your data by sensitivity, disclosure, and use. For example, common groups include:

• Customer information
• Financial data
• Inventory details
• Marketing campaigns
• Sales reports

3. Establish quality metrics

Then set metrics to measure your data quality. Usually, teams track accuracy, completeness, consistency, timeliness, and validity.

These metrics act as your benchmarks. As a result, you can judge how well your checks work.

4. Develop data profiling processes

Data profiling studies your data to learn its shape, quality, and links. So use it to spot patterns, odd points, and clashes.

5. Apply data cleansing techniques

Data cleansing fixes or removes errors and clashes in your data. So it uses steps like standardization, deduplication, and formatting. As a result, your data grows cleaner and more reliable.

6. Perform data validation

Data validation checks that data fits set rules and formats. So set up validation to confirm accuracy, integrity, and validity. Many firms handle this work with outsourced data entry teams.

7. Establish ongoing data quality monitoring

Data quality is an ongoing job. So set up regular checks to track quality over time. For example, run periodic audits to keep things consistent. Many teams fold this into their back office routine.

Maintaining top quality through consistent data quality checks

Steady data quality checks keep your data in top shape. So the checklist above helps your data stay accurate and trustworthy.

Remember, your data is a prized asset. As a result, checks are key to unlocking its full value. Still, good data takes steady effort to keep it sound and useful.

Frequently asked questions about data quality checks

What are data quality checks?

They are tests that find and fix errors in your data. For example, they catch duplicates, gaps, and outliers. So your data stays accurate and reliable.

Why are data quality checks important?

They protect your choices from bad data. So you avoid costly mistakes. They also help you meet rules like HIPAA and PCI.

How often should you run data quality checks?

Run them on a regular basis, not just once. For example, set up ongoing monitoring and audits. As a result, quality stays high over time.

What are the main data quality metrics?

Common metrics are accuracy, completeness, and consistency. They also include timeliness and validity. So these act as clear benchmarks.

What is the difference between data cleansing and data validation?

Data cleansing fixes or removes errors in your data. Data validation checks that data fits set rules. So the two steps work best together.

Key takeaways

• Data quality checks find and fix errors, so your data stays reliable.
• They tackle duplicates, gaps, outliers, and integrity issues.
• The seven-step checklist covers goals, metrics, profiling, and monitoring.
• Good checks help you meet rules like HIPAA and PCI.
• Data quality is ongoing, so it needs steady effort.