Data has never been more important for organizations looking to make smarter decisions. But, as organizational data increases, data teams struggle to find a structured approach to managing their data.
Not only do many organisations today have more data than they know what to do with, but they also often struggle to identify the quality data in their possession, leading to many of their most valuable data assets going unused.
Wakefield Research’s annual Data Quality Survey found that more than half of organizations reported that 25% or more of revenue was subjected to data quality issues. An astounding 74% also reported business stakeholders identify issues first, up from 47% in 2022, suggesting that data quality remains among the biggest problems facing data teams in 2024.
With the challenges more evident than ever before, data quality tools are becoming increasingly popular for organizations looking for software solutions to their data quality issues.
What are data quality tools?
Data quality tools are software solutions designed to ensure the accuracy, consistency, and trustworthiness of data throughout its lifecycle. These tools help clean and organize your data to make it reliable for analysis, helping you understand its format, structure, and values.
Data quality tools work by first profiling the data examining its format, identifying missing values, and checking for outliers. Once they have a profile, the tool can clean up the data.
This cleansing process includes fixing typos, standardizing formats (like making sure all dates are formatted the same way), or removing duplicates. It also involves checking your data against predefined rules to make sure it meets certain standards, such as setting a rule that emails must contain "@" or that zip codes have five numbers.
Data quality tools help improve the quality of your data over time. They inspect every piece of data coming in, check for validity, and ensure it meets quality standards before letting it through.
This makes them especially important for businesses that rely on data-driven decision-making where Inaccurate or unreliable data can lead to bad decisions, missed opportunities, and wasted resources.
Benefits of data quality tools
Data quality tools offer a range of benefits that can significantly impact an organization's efficiency, decision-making, and overall success. These include:
1. Better decision-making
Data quality tools act as a safety net for your decision-making process by ensuring the information you rely on is accurate, reliable, and trustworthy. inaccurate data can skew your analysis and lead to biased conclusions, and data quality tools remove these biases by identifying and correcting errors. This ensures your decisions are based on a clear and unbiased picture of reality.
2. Increased efficiency
Many data quality tools automate the process of identifying and fixing common data errors. This includes tasks like finding missing values, correcting formatting inconsistencies, and eliminating duplicates. When data is clean and accurate, there's less need to fix errors or troubleshoot issues later on. This reduces rework for analysts and data scientists, allowing them to focus on more strategic tasks like data analysis and generating insights.
3. More confidence in data
Data quality tools constantly scan for inconsistencies, missing values, formatting errors, and duplicates. They also enforce consistent formats and definitions across your data. Imagine addresses – some might be written with "St." while others use "Street." Standardization ensures all addresses follow the same format, improving data integrity and making analysis more reliable.
Some data quality tools also track the origin and journey of your data – where it came from, what transformations it underwent, and how it's currently being used. This lineage provides a clear audit trail, increasing confidence in the data's traceability and reducing concerns about manipulation or errors introduced during processing.
4. Enhanced compliance management
Regulatory compliance often requires specific data points to be present and accurate. Data quality tools identify missing values, inconsistencies, and duplicates, ensuring your data reflects reality. This reduces the risk of non-compliance due to incomplete or inaccurate reporting by helping businesses create a compliance management system.
5. Increased revenue and lower costs
Clean data allows you to accurately identify your ideal customers and tailor marketing campaigns to their specific needs and interests. This leads to higher conversion rates and increased sales. For instance, with good data, e-commerce companies identify customers who previously bought a specific product and target them with relevant promotions, boosting sales of related items.
By analyzing high-quality organizational data, you gain insights into their preferences, pain points, and buying behaviour.
This information can be used to develop products and features that resonate with your target audience, leading to higher customer satisfaction and repeat business.
Best data quality tools and software solutions for 2024
There are a range of powerful data quality tools and software available today, each of which can help you make the most of your data assets. However, not all of these tools are made the same.
In this list, we're counting down ten of the best data quality tools available today, exploring the features and capabilities that make them so popular.
Ab Initio
Ab Initio isn't just a data quality tool - it's a comprehensive data management platform with a powerful data quality engine at its core. The platform is built to handle complex data structures and massive volumes of large organizations. It automatically analyses data to identify inconsistencies, missing values, and formatting errors and then provides tools to cleanse and correct these issues. You can also define data quality rules using a spreadsheet-like interface, even for complex situations and translate these spreadsheets into technical rules. This collaboration bridges the gap between business needs and technical implementation.
Ab Initio caters to large, complex organizations. It offers centralized controls and audibility for data quality across the entire data pipeline, ensuring no data quality issues slip through the cracks. The platform goes beyond just identifying data quality problems too, automating the process of creating tickets for identified issues and assigning them to the appropriate data owner. This combination of automation and unified data management makes Ab Initio one of the best data quality solutions for large organizations today.
SAS Data Quality
SAS Data Quality is a software solution specifically designed to improve the accuracy, consistency, and completeness of your data. Developed by SAS, a leader in data analytics, the platform provides comprehensive tools for analyzing data structure, identifying inconsistencies, and applying transformations to correct formatting errors, missing values, and outliers. It excels at identifying and merging duplicate records, while also standardizing data formats (e.g., dates, addresses) to ensure consistency across different sources. It also comes with a pre-built library of reference data called SAS Quality Knowledge Base (QKB), which helps rules automate common data quality tasks to save time and effort to focus on more complex data quality issues.
SAS Data Quality works seamlessly within both the SAS 9 programming environment and SAS Viya, allowing for it to be seamlessly integrated into existing SAS workflows to facilitate data quality implementation for various user preferences. The software also offers functionalities to standardize formats, correct errors, and remove duplicates. You can leverage the QKB or define custom rules to address specific data quality needs. This makes it a compelling choice for organizations already using SAS and seeking to enhance their data quality efforts.
DQLbabs Platform
DQLabs is a cutting-edge data quality tool that's rapidly gaining recognition as a top-tier data quality solution in the market. Unlike traditional data quality tools that focus on isolated checks and manual interventions, DQLabs adopts a holistic approach by combining data quality with data observability. This powerful combination enables organizations to not only identify and remediate data issues but also proactively monitor data health and prevent problems from occurring in the first place.
DQLabs is designed to deliver measurable business value by streamlining data-driven decision-making and improving operational efficiency. Leveraging AI and machine learning, the platform automates data profiling, anomaly detection, and root cause analysis, freeing up data teams to focus on higher-value activities while ensuring data accuracy and consistency. By combining data quality and observability, DQLabs also provides a holistic view of data health and enables proactive issue resolution. This combination of data observability, augmented data quality, and automation features makes it a leading choice for businesses seeking to harness the full potential of their data.
OpenRefine
OpenRefine is a free, open-source software application designed to help you work with messy data. It's a versatile tool that allows you to clean, transform, and extend datasets in various formats, including CSV, TSV, and Excel. OpenRefine excels at identifying and correcting data inconsistencies, such as typos, duplicates, missing values, and formatting errors, providing a user-friendly interface to perform these tasks efficiently. The tool also offers robust capabilities to transform data, including splitting, merging, and restructuring columns, creating new columns based on calculations, and standardizing data formats.
While OpenRefine might not have the same level of automation or advanced features as some commercial data quality tools, it remains a popular choice due to its flexibility and ease of use. The tool’s ability to handle diverse data formats and perform a wide range of cleaning and transformation tasks makes it highly adaptable to various data challenges, while its intuitive interface and interactive approach to data cleaning make it accessible to users with varying technical expertise. This makes OpenRefine a powerful and versatile data cleaning and transformation tool that continues to be a valuable asset in the data quality toolkit. While it may not be the best choice for all data quality challenges, its flexibility, user-friendliness, and cost-effectiveness make it a strong contender in 2024.
Precisely Data Integrity Suite
The Precisely Data Integrity Suite goes beyond basic data cleansing by offering a powerful suite of seven interoperable services that enable your business to build trust in its data. The modular, interoperable suite contains everything you need to deliver accurate, consistent, contextual data to your business – wherever and whenever it’s needed. Its data quality service provides the accurate, complete, and contextualized data you need to optimize downstream operations – moving from basic insights to in-depth analysis. It offers tools for data profiling, cleansing, standardization, and matching across various data sources, going beyond static data to address data quality in real-time and streamline data pipelines. You can also visualize data changes as they happen to guide rules design, reducing the amount of time spent creating data quality rules, and enable additional types of users to apply rules.
The Precisely Data Integrity Suite combines data quality, data governance, and data mastering functionalities to provide a holistic view of your data from discovery to analysis. This creates a unified platform for data discovery, data quality management, data governance, and self-service data preparation, eliminating the need for multiple-point solutions and streamlining data management processes. And with the ability to validate, geocode, and enrich your critical data assets, its data quality service makes it easy to maximize the value of your data.
Oracle Enterprise Data Quality
Enterprise Data Quality (EDQ) is Oracle’s total solution for data governance and data quality management. Part of the Oracle Fusion Middleware product range, the platform comes with several built-in transformations to profile data quickly and to start discovering patterns and quality issues. Its Quickstats profiler, for instance, analyses high-level completeness, duplication, and value frequency across many attributes, while its Analyses attribute values for their data types, and assesses data type consistency. EDQ is also fully extensible, offering extensions for Address Verification and Watchlist Screening, and can be integrated as batch processing or as real-time processing with other integration tools.
EDQ has solidified its position as a leading data quality solution due to its extensive feature set, scalability, and adaptability to diverse data environments. The platform provides a comprehensive environment for understanding, improving, and managing data quality across various business processes. There’s also a virtual machine for testing EDQ with a sample dataset, which can be used to profile, audit, standardize, normalize, and deduplicate data.
Talend Data Fabric
Talend Data Fabric is a comprehensive data integration and quality management platform that makes it easy for organizations to harness the full potential of their data. The platform proactively identifies and addresses data inconsistencies, inaccuracies, and redundancies through profiling, cleansing, and matching capabilities, providing real-time data monitoring and alerting for timely issue resolution. It also seamlessly connects diverse data sources, including databases, files, cloud applications, and big data platforms, and supports various integration patterns (ETL, ELT, API-led integration) to accommodate different data types and challenges.
Talend Data Fabric enables business users to easily explore, clean, and prepare data for analysis without requiring extensive technical expertise. The tool provides self-service data preparation tools and collaborative features, helping organizations establish and maintain data governance practices to ensure data compliance and security. This helps businesses improve data accuracy, gain valuable insights, and accelerate data-driven decision-making by combining data integration, data quality, data governance, and data preparation capabilities into a unified, all-in-one solution.
SAP Data Services
SAP Data Services is an industry-leading data quality tool that unlocks the true value of your data by creating a complete view of your information with access to data of any size and from any source. The platform seamlessly connects to a wide range of data sources and targets, including databases, flat files, spreadsheets, and cloud-based systems, offering powerful data transformation capabilities to cleanse data before loading it into the desired target system. You can unify critical data on-premise, in the cloud, or within Big Data by using intuitive tools that help integrate operational, analytical, machine-generated, and geographic data. The platform,m also includes advanced data profiling and cleansing features to identify and correct data inconsistencies, duplicates, and errors, providing data quality rules and metrics to allow data teams to assess data accuracy and completeness throughout the data lifecycle.
As part of the information management layer of the SAP business technology platform, SAP Data Services seamlessly connects to a wide range of data sources, including SAP and non-SAP systems, databases, and flat files. This allowed it to address the diverse needs of data management, from integration and cleansing to profiling and governance. This flexibility, paired with its powerful interactions and range of features for managing data quality, make it SAP Data Services one of the best data quality tools and solutions available in 2024.
Ataccama ONE
Ataccama ONE is a unified data management platform that brings together data governance, data quality, and master data management into a single, AI-powered solution. Designed to operate seamlessly across cloud and hybrid environments, the platform empowers both business and data teams to accelerate innovation while maintaining data trust and security. It comes with a pre-built data quality monitoring feature which allows users to identify and rectify data quality issues in real-time. You can also easily evaluate and monitor data quality from an integrated data catalog, allowing you to see all data quality metrics and trends such as anomalies in one place.
Ataccama ONE is considered one of the best data quality solutions in 2024 because it is comprehensive, easy to use, and affordable. The platform offers a wide range of features, including data profiling, data cleansing, data matching, data monitoring, and data governance, and is also backed by a team of experienced data quality experts who can provide organizations with the support they need to manage and monitor data quality issues and resolve them in real-time.
Informatica Cloud Data Quality
Informatica Cloud Data Quality offers an industry-leading range of data quality capabilities across cloud, multi-cloud and hybrid-cloud environments. Providing AI-driven insights and capabilities, the solution enables a fully integrated data quality process to help drive innovation and success across your organization, enabling faster data migrations and trusted insights for your data experts. You can profile data and perform iterative data analysis to find relationships and flag problems, integrate data cleansing and standardization, verification and de-duplicate capabilities, and even automate your critical tasks and simplify data anomaly detection to boost productivity.
A leader in the Gartner Magic Quadrant for Augmented Data Quality Solutions for the 16th time running, Informatica Data Quality is driven by AI and machine learning, metadata, convergence and integrations across data management offerings, providing industry-leading automations that streamline the entire data quality process. Its CLAIRE engine delivers metadata-driven artificial intelligence to Cloud Data Quality, enabling intelligent recommendations of data quality rules that are based on how similar data has been managed. CLAIRE can also automatically detect data similarity, taking thefirsy is the first step toward detecting and removing duplicate data. These powerful, AI-driven capabilities, paired with Informatica’s powerful, rich set of data quality transformations and universal connectivity, make Informatica Cloud Data Quality one of the best data quality tools available today.
