Top Data Quality Tools – and How to Choose Them in 2024

Updated September 28th, 2024

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As data volumes grow at an exponential rate, businesses are finding it harder to manage data and ensure their data is consistently high quality. The result is a data trust gap that threatens the future of data-driven solutions, such as AI applications.
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Data quality tools can help by automating processes and rules that ensure the accuracy and integrity of data. But this isn’t as simple as hey, let’s buy a data quality tool!: first, you must understand the data quality issues your organization is experiencing. This knowledge will then guide your decisions around tooling investments.

In this article, we’ll review some of the challenges with data quality, how to identify what data quality tools you might need, and the essential capabilities and features you might need.

Table of contents #

  1. What data quality tools do
  2. Data quality tools: Capabilities & Features
  3. Types of data quality tools
  4. The best data quality tool outcomes
  5. Data quality tools: Related reads

What data quality tools do #

Data quality tools ensure the integrity, accuracy, and usefulness of data. They increase confidence and trust in data, which encourages driving business decisions as well as creating new data products for consumers.

Data quality tools can also introduce features like alerting and other capabilities that make it easier to catch data quality errors early. This enables data engineers to find and fix potential issues before they negatively impact data consumers (e.g., an unexpected null value that breaks a BI report).

Using data quality tools, organizations can track the overall level of data quality across the organization. They can use this data to increase overall trust in data, as well as identify lingering data quality issues and make targeted improvements.

What you need from data quality tools #


Before choosing the best data quality tool, you must first identify your organization’s specific data quality challenges and craft a strategy to address them.

At Atlan, we recommend our customers follow a three-step approach in resolving data quality issues:

Awareness. Gather data — via user tickets, reports, data pipeline error logs, etc. — on the issues you’re seeing. Segment the results by data product (e.g., tables, reports) to narrow in on the most problematic assets. Publish this data publicly (e.g., in a report or even a Slack channel) and assign severity ratings to each issue.

Cure. Gather a cross-disciplinary team and define Service Level Agreements (SLAs) for data. These can include metrics such as total number of data-related incidents, time to incident resolution, number of data tests passed/failed, time since last successful refresh, etc. — whichever data quality metrics are most important in your organization.

Prevention. Use tools such as data contracts to explicitly define the obligations that a data producer promises to fulfill for a data consumer. Leverage automated tools to streamline data management and improve quality at scale, reducing or removing the injection of new data quality issues due to human error.

Data quality tools: Capabilities and Features #

Once you know where you are in your data quality process and understand the issues you need to fix or improve, you can narrow down the tools you will need.

To assess data quality tools, you need to consider both capabilities and features.

  • Capabilities represent “what” the tool does — the business tasks you can perform with a tool to improve and maintain data quality. Examples of data quality tool capabilities include data monitoring and alerting, profiling, cleansing, validation, and standardization.
  • Features are “how” the tool functions; the specific functionalities or techniques within the tool that allow it to achieve its overarching capabilities.

Let’s look first at the capabilities of data quality tools, and then learn the specific features that enable these capabilities.

Data quality tool capabilities #


Monitoring and alerting. A data quality tool’s reporting capabilities provide a status overview of your current data quality initiatives. Monitoring and alerting capabilities act on data to enable a proactive approach to data quality, notifying data engineers when potential problems or errors are detected.

There’s no worse experience for a data consumer than seeing that their report didn’t refresh because the underlying data pipeline broke, or seeing an error because a text value in the latest data set was misformatted.

With monitoring and alerting capabilities, data engineers can detect such problems and resolve them before they hamper the flow of business. Engineers can define rules for common data formats and specify data contracts detailing the expected values for given fields.

Root cause and impact analysis. Detecting errors won’t do you much good if you can’t find and solve the problem at its source.

Data pipelines are complex beasts, combining data from multiple sources culled from across the company. An error in a common source table can break multiple downstream reports and applications.

Data engineers can use root cause analysis data quality tools to not only get an alert on an issue but trace the issue back to its source. This way, engineers can solve data issues once, at the root, rather than implementing quick fixes in every affected app.

Impact analysis detects when a new data pipeline code check-in could potentially be a breaking change. This data quality capability prevents injecting any new errors while preserving the existing quality of data.

Recommendation and resolution. AI has provided more opportunities to automate data quality, moving beyond error detection to then provide suggestions for a fix or to improve data quality.

AI data quality capabilities can improve data quality on multiple fronts by, for example:

  • Assisting data consumers in constructing and optimizing SQL queries
  • Suggesting new data rules based on perceived patterns
  • Auto-remediating commonly known data issues

Data quality tool features #


Data catalog. A data catalog serves as the single source of truth for all data in an organization. That makes it a foundational tool for data quality: the data catalog is the one location where anyone in the org can discover, access, use, and verify the quality of data, no matter where it originates.

A data catalog can also enrich data with metadata. Metadata adds additional context that both users and automated tools can leverage to drive usage and improve data quality. For example, metadata can include quality metrics such as the freshness of a data set and the results of recent data test runs.

Data lineage. Data lineage visualizes the journey your data takes as it moves throughout the company. With lineage tools, anyone can trace data back to its source. Table lineage shows this movement at a data object level, while column-level lineage details it down to individual fields.

Data consumers benefit from lineage because they can verify the origin of data, which helps to close the trust gap. Lineage also enables both root cause analysis and impact analysis, giving engineers the information they need to improve data quality at its foundations.

As an example of how powerful data lineage can be, consider impact analysis. Suppose a data engineer checks a new transformation into a dbt data model and raises a pull request. Using impact analysis, you can automatically run code, triggered by the PR, that uses data lineage to check if the change will break downstream users. The data engineer can then work with the impacted teams to prevent any potential work stoppages.

Rule definition and alerting. With rule definition and alerting, data engineers and data domain owners can create rules and policies that aid in automated anomaly detection. For instance, they can define rules ensuring to detect conditions like:

  • Correct formatting of text fields
  • Missing primary keys or unexpected null values
  • Duplicate records
  • Calculations on business-critical values (e.g., quarterly sales projections)

A high-quality rule definition feature will have built-in checks and policies that engineers can apply to data — but it should also provide an easy-to-use UX for creating and implementing custom policies, too.

These checks can run continuously, raising Jira tickets or sending Slack messages when it detects potential new errors in data. Rule definition and alerting features enable data producers and consumers to improve and maintain data quality even as data volumes continually increase.

Augmented data quality and rules recommendations. Augmented data quality is an emerging category of data quality tools that uses AI and Machine Learning to detect potential new rules based on data patterns. For example, an augmented data quality tool might use statistical detection to create an alert for historically out-of-bound values.

AI tools can also help improve data quality by generating suggestions for documentation and enriched metadata. Better data documentation and metadata make data more comprehensible to users, which can then reduce usage errors (and urgent queries to the data engineering team).

Reporting. Reporting features provide out-of-the-box metrics that the organization can use to track data quality over time. These will contain reports on metrics such as the number of errors detected, the number of high-quality vs. low-quality tables across the organization, and average time to data error resolution, among others.

Data utilization reports can provide insight into who’s using what data. A usage increase is evidence that data quality improvements are working and that user trust in data is increasing.

Organizations can also use data usage reports to find so-called dark data - data that costs money to clean and maintain but that provides little to no business value. Dark data is sometimes a signal that a data set is too low-quality or too poorly documented for anyone to use.

Types of data quality tools #

Data quality tools can differ along a number of vectors. One is whether the tool is open-source or commercial.

Some open-source data tools, such as dbt and Great Expectations, have seen widespread adoption for tasks such as data modeling and testing. Commercial data quality tools boost open-source tools capabilities by adding advanced features like data lineage visualization and traversal, and augmented data quality, and pairing those with enterprise-level scalability and support.

Another differentiator is where data quality tools exist in your data stack. For example, some may work early in the ELT (Extract, Transfor, and Load) phase, detecting and correcting issues during data import. Others may run data checks as programmatic or orchestrated scans after the import, ensuring accuracy for data generated from legacy data pipelines.

The best data quality tool outcomes #

Data quality tools provide critical support for creating accurate, timely, and consistent data at scale. To choose the best data quality tool, however, you must first have a good understanding of your organization’s specific data quality issues. This knowledge then helps you define the capabilities and features you need to seek in a data quality solution.

Used wisely, data quality tools can help narrow the data trust gap, increase data usage, and reduce the time to market for new data-driven solutions, such as AI applications. Finally, a full-featured data quality tool that makes data more comprehensible to business users means fewer Jira tickets (and a happier data engineering team!).

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