Several factors, like consistency, accuracy, and validity, contribute to data quality. When left unchecked, businesses that utilize inconsistent, inaccurate, or invalidated data can lead to poor decision-making, missed opportunities, or lower profits. New technologies also critically rely on high-quality data, such as artificial intelligence (AI) or machine learning, to generate reliable outputs.
While enterprises today are collecting more data than ever, most of this data is messy and full of problems. Adopting the best data quality tools to diagnose and correct flaws in the data allows businesses to maximize the utility of this crucial resource and gain a competitive advantage.
What are data quality tools?
Data quality software tools make monitoring and improving data quality easier and more efficient. They analyze sets of information, identify flaws, and correct them based on established guidelines to eliminate low-quality data that compromises its utility. Because most enterprise datasets have been structured in a tabular format until recently, the majority of data quality tools are designed for such tabular data, often analyzing it column by column.
Today, companies are seeing a massive influx of unstructured data (such as images, video, and text) powering next-generation AI models and comprehensive analytics. What has not changed is that this unstructured data remains messy and full of problems, necessitating new forms of software capable of improving the quality of unstructured data.
A consideration beyond structured vs. unstructured data to consider when looking into data quality software is whether the tool is rules-based or AI-based. Rules-based data quality tools require a team to think of possible data problems ahead of time and codify rules that will flag them in data pipelines (for instance, that a particular column in a table should never contain negative numbers).
A new class of data quality tools uses AI and statistical machine learning to automatically flag common data problems without a team having considered their existence. Rules-based and AI-based data quality tools can be complementary, the former catching domain-specific problems a team is specifically worried about and the latter catching more general problems the team did not think (or could not afford) to check for in their large datasets.
Why is ensuring data quality critical for the modern enterprise?
Data quality ensures the information that organizations use is consistent, accurate, and validated. Businesses can make better-informed decisions when using high-quality data, leading to an increase in profit or productivity. They can also minimize operational errors that result in profit loss caused by poor-quality data. The modern customer experience hinges on serving the correct information and the effective use of AI in products, capabilities that depend on a foundation of high-quality data.
Unfortunately, this foundation is crumbling throughout many organizations, whether they realize it or not. Problems inevitably creep into datasets through data entry mistakes, broken data pipelines, improper data sources, corrupted measurements, and suboptimal human feedback/annotations. Many such problems lurk within most of the massive datasets being collected today, limiting the value of this core business resource. Good data quality tools are thus more valuable than ever before.
Outlined below are the top 5 best data quality tools in 2023:
1 Cleanlab Studio
(Photo: Cleanlab)
Cleanlab offers one of the few data quality tools that can automatically find and fix issues in both unstructured and structured datasets. Their Cleanlab Studio platform can analyze almost any image, text, or tabular (CSV, JSON, SQL) dataset and can be run in any easy no-code manner. It takes only a few clicks to diagnose a variety of problems in almost any dataset. The platform is powered by a new type of AI system that automatically analyzes the data and can auto-correct certain types of issues.
Cleanlab is a rapidly growing startup founded by three PhD graduates from the Massachusetts Institute of Technology, where they also teach the first course focused on data-centric AI. The new type of data-centric AI software Cleanlab has invented is capable of automatically detecting common data problems such as mislabeling, outliers, near duplicates, data drift, low-quality images or text, as well as other types of data problems that require understanding the information in a dataset.
Cleanlab Studio thus helps improve data quality in a different manner than rules-based data quality tools, which cannot detect such data problems. Featured in CB Insights' list of top 50 Generative AI companies, Cleanlab is backed by Databricks, and users of its software include 50 of the top Fortune 500 companies.
(Photo: Cleanlab)
2 Informatica
(Photo: Screenshot from Informatica Executive Brief)
Informatica Data Quality empowers businesses to perform more reliable data analytics, improve operational efficiency, and enrich their customer experience. This tool is part of an intelligent data management cloud (IDMC) designed to help businesses handle the complex challenges of dispersed and fragmented data. Informatica enables companies to share, deliver, and democratize data across lines of businesses and other enterprises on a foundation of governance and privacy. It provides users with a 360-degree view of business data and trusted insights, showing the relationship between customers, products, and suppliers across the business.
Informatica's data quality tool is primarily intended for structured tabular datasets and is a rules-based approach. The platform includes prebuilt rules and accelerators that can be reused across data from any source. Other capabilities include fast data profiling via continual analysis, data standardization, address verification, and a data observability suite (monitoring, tracking, and alerting). As of 2022, Informatica owned the largest share of the Data Quality market.
(Photo: Screenshot from Informatica website)
3 SAS Data Quality
(Photo: Screenshot from SAS website)
SAS Data Quality standardizes and improves new and existing data so leaders can make better decisions using trusted data. Businesses use the platform to improve and monitor the health and value of relational databases and tables to achieve compliance and accurate analytics. This tool is made for all types of organizations and allows users to directly update and tweak data, as well as generate visualizations and reports.
Like Informatica, the SAS data quality tool is only for structured tabular datasets and is based on a rules-based approach to catch data problems. It helps profile and identify data problems, preview data, and set up processes to ensure data remains high-quality over time. Other key features include data normalization and de-duplication, entity resolution, foundational master data management, business glossary and lineage to relate business and technical metadata, visualization and reporting, data integration, and data remediation.
Via in-database technologies, SAS Data Quality can also accelerate critical data quality and analytical processes - certain operations can be executed directly in the database. SAS has been listed as a leader in Gartner's 2022 Magic Quadrant for Data Quality Solutions.
(Photo: Screenshot from SAS website)
4 Deequ
(Photo: Screenshot from Github)
Deequ is another rules-based data quality tool intended for structured tabular datasets. Unlike Informatica and SAS, Deequ seamlessly handles data stored in Spark Dataframes and is fully open-source (Amazon Web Services also offers a managed cloud service built on top of Deequ called AWS Glue Data Quality). Deequ is used internally throughout Amazon for verifying the quality of many large production datasets and serves as a unit-testing framework for large-scale data. It tests data to find errors early to ensure its quality before it gets fed to consuming systems or machine learning algorithms.
Issues that Deequ can help mitigate are preventing missing values from causing system failures, machine learning prediction degradation due to data distribution drift, and incorrect business decisions made based on wrongly aggregated data. Usable via Java and Python, Deequ calculates data quality metrics on every new version of a dataset (based on completeness, maximum, or correlation). It also verifies rules (user-defined constraints) are not being violated, for instance, that a certain column only takes values within a specific range.
Other excellent features offered by this tool include anomaly detection on data quality metrics, incremental metrics computation for growing datasets, as well as automated constraint suggestion based on data profiling heuristics. Given the immense popularity of Spark pipelines for big data, Deequ has received over 3000 stars on Github and is being adopted by many teams.
(Photo: Screenshot from AWS blog)
5 OpenRefine
(Photo: Screenshot from OpenRefine website)
OpenRefine is another open-source data quality tool for structured tabular datasets. Unlike many other data quality tools, OpenRefine is intended for messy data to not only report data quality issues but also to help understand the data, clean it up, and transform it into a suitable format. This tool was created by a startup that was acquired by Google, which has been driving further development, along with a thriving user community. OpenRefine has received 10,000 stars on Github and a WikidataCon Award.
Based on Java with an accompanying web interface, OpenRefine offers a comprehensive suite of capabilities. Users can explore big datasets via facets and filtered views, on which cleaning operations can be applied. These can highlight patterns and trends in a dataset, making it easier to gather insights. If creating a facet on a column is an ideal way to look for inconsistencies in the data, clustering is the appropriate method to fix them.
OpenRefine clustering uses comparison methods to find the entries that are similar but not exact, then shares the results for quick resolution, eliminating the need for manual editing of single cells or text facets. The data editing capabilities allow infinite undo/redo so that a proper data version lineage remains available. OpenRefine can also match datasets with external sources. This is useful for reconciling information to fix errors or variations, linking data to an existing dataset, or normalizing manually entered subject headings against authoritative values. Overall, OpenRefine is a user-friendly tool with a straightforward interface to help improve data quality.
(Photo: Screenshot from OpenRefine website)
Conclusion
Data is the lifeblood of organizations. Without the availability of quality information, businesses make poor decisions, and AI or Analytics efforts inevitably fall short. Leading data-driven businesses all leverage multiple technologies to ensure data quality. The costs of data quality software are negligible compared to the benefits it can provide from cost reduction, engineering time savings, improved customer experience, and a boost in business operations.
There are other valuable data quality tools worth mentioning, such as Talend, Alteryx, IBM InfoSphere, Oracle Data Quality, BigEye, Great Expectations, HoloClean, and Melissa. Countless other tools exist for data observability and validation, normalization, standardization, and correction of informational inaccuracies. The above list covers the easiest-to-use and most useful solutions out there. Eliminate problems within datasets with the top 5 best data quality tools in 2023.
