Data quality tools are essential for businesses to make strategic decisions, achieve business objectives, and solve critical challenges. TechTarget defines data quality as a health condition of data based on factors such as accuracy, consistency, reliability, completeness, and whether it’s real-time or not. If the data is bad, it can cost organizations an average of $12.9 million every year, according to a Gartner report. On the other hand, 80% of companies see a surge in revenue after implementing good and real-time data.
In this context, data quality tools play a pivotal role in determining the health of data. They examine the data to ensure it is good enough to make informed business decisions. Let’s discuss the top data quality tools, and their pros and cons, to help you make the right choice for your organization.
What are Data Quality Methods?
IBM describes a data quality method as a process organized by analytical functions to provide in-depth knowledge and best practices for data quality strategies. There are different types of data quality methods, which include:
Data Validation Through One or More Sources
The data validation process involves the implementation of predefined rules and knowledge to validate the integrity of data through single or multiple databases.
Conducting Data Quality Studies
This systematic process is about designing and executing data quality assessment studies to determine the health condition of the data.
Identification and Validation of Data condition
Identification of the data condition process is also known as case definition or case finding. In this process, data analysts use predetermined rules to identify and validate the data health condition.
Best Data Quality Tools
Data is the lifeblood of an organization and has a significant impact on business outcomes. That’s why it’s pivotal for companies to have the right set of data quality tools to determine the health of the data they are relying on. Here is a list of the top five data quality tools to consider for your data quality management process.
Ataccama ONE is an open-source data management platform that consolidates data governance, data quality, and data management in a single, AI-enabled platform across hybrid and cloud environments. Integrating Ataccama ONE enables data teams to accelerate their business decisions while maintaining data trust, security, and governance.
Include data discovery and profiling, metadata management, data catalog, and data quality management
- Exceptional data profiling abilities
- Easy to use and flexible
- Provide a fast data processing option
- Time-consuming configuration process
- Difficult to identify the cause of errors
- Tough to get technical support because of its open-source feature
Informatica provides multiple data quality services including data quality, master data management, big data quality, Axon data governance, and data as a Service. These help users develop a holistic view of data collected from disparate, duplicate, and conflicting sources.
Include data integration, address validation, data quality, data profiling, and data security functionality
- Offers a one-stop-solution with various services
- Automates a wide range of data quality tasks
- Lack of open-source version leads to a high maintenance fee, which is expensive
- Incompatible with other technologies because it doesn’t support the integration of code written in languages like Java, Python, and R
3. Innovative Systems
Innovative Systems provides Master Data Management (MDM) solutions for operational and analytical functions. Its Enlighten platform is an integrated data quality software that gives unified and customizable solutions to customers.
Include data profiling, data discovery and monitoring, and a holistic view of data that helps users extract meaningful insights
- Easy to operate and customized
- Preferable for data matching process
- Offers product support
- Interface for developing workflows is unclear
- Compatible with Windows only
Oracle’s Cloud Infrastructure Data Catalog is a metadata management service that enables companies to discover and control data using an organized stock of data assets.
Include an advanced, intuitive user interface (UI) that encompasses a simple, user-friendly dashboard, search and browse abilities, suggested actions, and shortcuts
- Cloud-based, hence accessible anywhere using any device
- Offers a one-stop-solution
- Restricted data export
- Complicated to navigate
Precisely is a data quality tool that helps users achieve data integrity with accuracy and consistency. It offers multiple data quality solutions such as Trillium for big data, Trillium data quality, and Trillium cloud, which help users make informed and quick business decisions with an in-depth understanding of data.
Includes data cleansing, data profiling, and data standardization
- Provides strong customer support
- Offers multiple solutions under one umbrella
- Complex to use
- Complex installation process and User Interface (UI)
How to Choose the Right Data Quality Tool
Given the multiple data quality tools available in the market, deciding on the right one can be difficult. But the choice is important as the right data quality tool can help businesses make informed decisions. Here are some important tips to help you with the selection.
- Identify the data-related challenges your business is facing
- Understand the type of data quality tools to help you solve those challenges
- Be aware of the pros and cons of data quality tools to support your decision
- Investigate the pricing plan and select the tool as per your allocated budget
Becoming Data-Savvy with Emeritus
Data empowers business decisions but if data is bad or irrelevant, it can lead you in the wrong direction. Hence, improving data quality using the above-mentioned tools is essential. To understand more about data-related tools and strategies, explore the various online courses on the Emeritus platform, offered in collaboration with leading global experts.
By Sweta Singh
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