All Blog Posts

The Key Organizational Shift Accelerating Data Quality

September 13, 2022

The 4 Cs of Data Quality

Nothing quite sums up the importance of data quality like the well-known phrase “garbage in, garbage out.” Without good data, you cannot have good analysis; data quality is required for sound decision-making.

As a guide, it’s crucial to keep in mind the 4 Cs of data quality: the consistency, conformity, completeness and currency of the data. Consistency means ensuring a clear picture of data consistency—meaning, is it statistically valid? Is it internally coherent? Are there extreme values, outliers or anomalies? Conformity refers to acceptable standards and patterns that the data must adhere to or ensuring there are no mismatched values. Completeness indicates that all necessary data has been included and there are no missing values. And finally, currency, or validating that the data is up-to-date and has been refreshed regularly.

Making Data Quality a Collaborative Effort

The truth is, the best way to get ahead of data quality issues is to identify them early and often. However, this is difficult for business users to do when they’re shipping their data requirements off to IT, and then waiting to receive them back before being able to assess and define new requirements. Or when they’re trying to scroll through columns upon columns of Excel pages or code—these time-consuming tasks and cycles don’t lend well to efficient data quality.

Instead of scaling up on IT resources, many of today’s organizations are shifting the responsibility of data quality toward business users and at the same time are outfitting business users with modern data preparation platforms built to automate and accelerate the onerous data quality process. For one, this is a more efficient approach—instead of a small task force chasing down issues of data quality, there are more eyes on the data—but it also leads to better curation for the end analysis. IT will still curate the best stuff, make sure it is sanctioned and re-used (this ensures a single version of the truth and increases efficiency). But, with business context and ownership over the finishing steps in cleansing and preparation, these users can ultimately decide what’s level of data quality is acceptable, what needs refining, and when to move on to analysis.

Alteryx Designer Cloud for Data Quality

Alteryx Designer Cloud is widely recognized as the leader in data preparation. Its visually-driven platform not only allows users to easily spot data quality issues, but also allows anyone in the organization to speak the same language, which improves transparency about how particular data sets have been transformed to order to maintain consistent data quality.

Alteryx’s ongoing commitment to data quality has manifested in several key features of our data preparation platform. For example, Cluster Clean allows users to quickly explore multiple clustering options and catch new data quality problems. It’s also resilient to new data, easily incorporating new values without being tied down to whatever clustering method happened to work best the first time. Another data quality issue that Designer Cloud can quickly address is issues of mismatched formatting. Instead of using manual regular expressions and complex conditions to remedy mismatched values, Designer Cloud identifies the patterns, and by interacting with those patterns, Pattern Clean will predict the best way to resolve them.

We’d love to chat with you about how we can help your organization improve and accelerate the process of ensuring data quality by giving business users increased ownership over data preparation. Schedule a demo of Designer Cloud today.