SAS Data Quality

What differentiates SAS Data Quality from other similar products?

The most challenging step in any analytics project is gathering data and transforming it so that it is business-ready. Sas data quality assisted us in profiling data, previewing and standardising it, and monitoring it in order to avoid data issues throughout the analytics phase.

What is your favorite aspect of this product?

I enjoy how the interface modifications allow users to perform more on one screen in a more effective manner. Viya handles tabs better than desktop SAS, and it's convenient that datasets don't need to be closed to be changed. There have also been other enhancements to the quality of life. You can save commonly used routines in snippets and transmit outputs in a number of formats and methods. These features were missing from the desktop version and help to modernise the SAS experience.

What do you dislike most about this product?

I didn't perceive any disadvantages to utilising SAS Viya, but I would prefer it if the software's creators made it available to a wider audience. I've been using SAS software for over 7 years, and they were the first to build such enterprise-level statistical analysis applications. However, when I changed professions in the same field, I noticed that few organisations were adopting SAS programmes.

What recommendations would you give to someone considering this product?

SAS allows me to finish all of my analyses in a reasonable period of time and in an organised manner without having to spend a lot of money on software. I appreciate how useful it is for mixture modelling.

What differentiates SAS Data Quality from other similar products?

SAS Data Quality stands out for its strong analytical foundation and deep integration with data science workflows. Unlike many tools that focus only on cleansing and matching, SAS ties data quality directly into broader analytics and machine learning pipelines. Its data profiling, parsing, and enrichment capabilities are highly customizable and backed by SAS’s decades of statistical expertise. Another key differentiator is its support for complex industry-specific rules and governance, which makes it ideal for regulated sectors like healthcare, finance, and insurance. Overall, it’s a powerful platform for organizations that treat data quality

What is your favorite aspect of this product?

My favorite aspect of SAS Data Quality is its powerful data profiling and parsing capabilities. It gives you a deep, detailed understanding of your data right from the start, helping you quickly spot inconsistencies, duplicates, and formatting issues. The fact that you can customize rules and workflows to fit very specific business needs is a huge plus. It’s especially valuable in complex environments where data quality isn’t just about cleanup—it’s about enabling accurate analytics and decision-making.

What do you dislike most about this product?

The biggest drawback of SAS Data Quality is its steep learning curve and complex interface. While it’s incredibly powerful, it’s not very beginner-friendly—especially for business users or those without a technical background. Setting up workflows or custom rules can feel overwhelming without proper training. Additionally, integration with modern cloud-native tools and platforms can require extra effort, which might slow down adoption in more agile or hybrid environments. A more intuitive UI and better out-of-the-box cloud connectivity would make a big difference.

What recommendations would you give to someone considering this product?

Ideal for Data-Driven Organizations: If your company heavily relies on analytics, forecasting, or regulatory reporting, SAS Data Quality is a great fit due to its deep profiling and rule-based cleansing. Be Ready for Training: The tool is powerful but not very intuitive at first. Invest in proper training or have experienced SAS professionals on your team to get the most out of it. Leverage Its Strengths: Use SAS's strong parsing, standardization, and matching tools, especially if your data spans multiple sources or formats. It’s particularly effective in industries with complex data structures.

What differentiates SAS Data Quality from other similar products?

SAS Data Quality differentiates itself through deep integration with the broader SAS ecosystem, offering advanced analytics, robust data governance, and industry-specific capabilities. It's built for enterprise-scale performance, supporting comprehensive data profiling, cleansing, matching, and monitoring across hybrid and cloud environments. This makes it a strong choice for organizations that need reliable, scalable, and intelligent data quality solutions.

What is your favorite aspect of this product?

My favorite aspect of SAS Data Quality is its seamless integration with advanced analytics and the broader SAS platform. This allows users not only to clean and manage data but also to directly leverage that improved data for powerful analytics, reporting, and AI-driven insights—all within a unified environment. It turns data quality from a back-office task into a strategic enabler.

What do you dislike most about this product?

What’s most commonly criticized about SAS Data Quality is its **complexity and steep learning curve**, especially for new users or organizations without existing SAS expertise. Its powerful capabilities often require significant setup, customization, and training, which can slow down adoption and increase dependency on skilled resources. Additionally, licensing costs can be high compared to more lightweight or open-source alternatives.

What recommendations would you give to someone considering this product?

If you're considering SAS Data Quality, here are key recommendations: Assess your organization's SAS maturity: It's best suited for teams already using SAS tools or planning to invest in the broader SAS ecosystem. Plan for training and support: Due to its complexity, allocate time and budget for staff training or consider engaging SAS consultants. Start with a clear use case: Focus on a specific, high-impact data quality issue (e.g., customer de-duplication or address standardization) to demonstrate value early. Evaluate scalability needs