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Choosing the best data quality tools can mean the difference for your organization's data. With great data quality tools, you can improve workflows, save time, and enhance decision-making. With the wrong tool, you could end up in a situation worse than where you started.
In this blog, we'll cover the five best data quality tools available to businesses, comparing and contrasting their benefits so you can pick the right one for your organization. Let's get started.
What is data quality?
Data quality measures the usability of your data based on six key dimensions: completeness, validity, timeliness, uniqueness, accuracy, and consistency.
Learn more about data quality and its importance.
5 best data quality tools on the market
Learning about the background and key features of a data quality tool can help you make your decision. Below, you'll find a list of the top five data quality tools.
1. Ataccama data quality software
Ataccama has been a driving force in data-driven success since 2007, providing businesses with the trusted, high-quality data they need to thrive. The comprehensive data quality and catalog suite empowers users to effortlessly discover, manage, and perfect their data all within a single, unified platform.
With Ataccama DQC, you can seamlessly:
- Establish a comprehensive data catalog. Gain a clear understanding of your data landscape by documenting and organizing your data assets.
- Ensure data accuracy and consistency. Utilize powerful data quality rules and automated workflows to identify and rectify data inconsistencies, ensuring your data is reliable and trustworthy.
- Proactively prevent data issues. Implement robust data quality checks and validation rules to catch errors before they impact your business.
- Govern your data effectively. Define and enforce data policies and procedures to maintain data integrity and compliance.
All of this is powered by advanced AI, enabling intelligent automation and effortless data management. Ataccama DQC provides a user-friendly interface and a comprehensive suite of capabilities that surpass the competition, giving you the tools you need to unlock the true potential of your data.
Key features:
- AI-powered data quality
- Data profiling & classification
- Data quality monitoring
- Anomaly detection
- Data observability
- Data cleansing & standardization
- Remediation
- Transformations
- DQ firewall for issues prevention
2. Informatica
Informatica is both a data quality and observability platform, focusing on proactive measures to ensure high-quality data. Their tool builds trust in an organization and has advanced automated capabilities thanks to AI. They also deliver an enriched customer experience by providing instant and accurate access to frequently updated information.
Key features:
- Automated and scalable data profiling
- Prebuilt AI-powered rules
- Boosted data observability
3. Talend
Talend offers data quality solutions that profile, clean, and standardize data across your systems. They also emphasize the trust and interconnectedness of data teams and provide machine learning-powered recommendations for addressing data quality issues.
Key features:
- Built-in "trust score" for evaluating data
- Automatic data enrichment
- Compliance as a priority
4. IBM InfoSphere QualityStage
IBM's InfoSphere offers capabilities for generating and monitoring data quality. It's explicitly designedto support data quality and governance initiatives by providing a consistent and accurate view of your organization's key data entities (e.g., customers, vendors, locations, products).
Key features:
- Data lake governance
- Unified platform
- Compatible with other IBM software
5. Precisely Trillium Quality
Precisely's Trillium quality delivers enterprise data quality built for business. It is a data quality tool that scales and supports changing business needs as your organization grows. It's deployable in batch or real-time, on-premises, or in the cloud.
Key features:
- Versatile deployment
- 360 customer view
- Global verification rules
Why are data quality tools important?
As mentioned earlier, data quality tools can allow your business to do even greater things with your data. Here are some other reasons they're important:
- Better decision-making. Once your decision-makers have reliable data delivered in a timely manner, they'll be able to use it for better-informed decisions to benefit your business.
- Increased speed of data teams. Data users spend most of their time preparing data and making it trustworthy for stakeholders. By addressing data quality, you can cut out this progress stopper and get started on the projects that matter most.
- Avoid bad data quality. Bad data quality can hinder decision-making, leading to incorrect conclusions and the slow delivery of information.
How to choose the best data quality tool
If you're still deciding which tool best suits your organization, let's review some criteria to help you make your decision.
1. Assess your organization's data quality needs
The volume and types of data your organization works with will heavily influence your tooling. Do you have large volumes of data? Is it growing? Do you need to focus on scalability? How much private data does your organization work with? Finding answers to these questions and internal auditing your data systems will help you pick the best tool.
2. Consider scalability and integration
As organizations grow, so do their data volumes. Whichever tool you choose needs to accommodate this growth and provide the flexibility necessary to process your assets.
Companies also need to consider integration: Is the tool compatible with their current systems? Building a solution from the ground up or adapting their existing systems to an incompatible tool will always be more costly and time-consuming.
3. Run a cost vs benefit analysis
Figure out the cost of the tool you want vs. the time and budget savings it will provide. Consider the ROI of your data quality solution.
4. Ask for customer reviews and case studies
Customer reviews and case studies are a great place to get information about a data quality vendor. Search for success stories that sound similar to your use case – your tool should have a history of achievement in your field.
What features does a data quality tool need?
Regardless of the solution you choose, there are some features that all of the best data quality tools have. Here they are:
1. Ease of use and integration
You need a data quality tool that is integratable with your existing technology. In addition, ease of use is an important characteristic. You don't want a tool that will take half a year to train your team to use. It needs to be relatively intuitive and user-friendly.
2. Accessible and scalable
Scalability is an important factor to consider when selecting a tool. As systems grow, entry portals multiply, and user roles diversify, your data quality tool will need to adjust access protocols and be capable of handling larger volumes of data and source systems.
3. Identifies inaccuracies and duplicates quickly
Your tool must be able to identify data quality management challenges rapidly. If it only creates more work for your end users to discover the information they're looking for, it can be more of a burden than a benefit.
4. Supports data cleansing
Data cleansing is one of the most important roles of data quality management: cleaning up dirty data so it's fit for purpose. The best data quality tools support cleansing, making the lives of data stewards easier so they don't have to correct each record manually.
5. Enables ongoing monitoring
Data systems aren't perfect. Even if you have adept data quality rules, unexpected outliers and hiccups can slip through the cracks. Ongoing data quality monitoring continuously scans your data for issues indefinitely so no unwanted surprises reach your downstream systems.
6. Promotes data security
A company's data is only as secure as the tools they use to work with it. You need a tool with access permissions and significant barriers against hackers to prevent data leakage and other security related mishaps.
7. Automated and customizable
All data quality tools have some form of automation in 2024. Automated processes like anomaly detection and data observability expedite the data quality process by recognizing issues and flagging them without human intervention.
No two companies are the same, so solutions must also be customizable. This way, they can effectively address your organization's unique needs.
Invest in the right data quality tool and reap the benefits!
Data quality tools are crucial for improving decision-making, increasing operational efficiency, and enhancing customer satisfaction by ensuring data is accurate, complete, and reliable. Knowing about the leading tools on the market, their key features and strengths, and what to look for in a data quality tool can help your company choice this essential tooling.
Don't hesitate to try our automated data quality software. We've been improving companies' data assets for nearly two decades.
