Behind the Scenes: Viewing Checks

If Our Customers Ask, We Listen!

“Give the lady what she wants.” Marshall Field, an American entrepreneur and the founder of Marshall Field and Company, coined this famous customer-centric phrase. It highlights the importance of serving the customer and their direct needs. This is an important part of product development in a world that is iterative and with customers’ needs constantly changing. Regular feedback meetings with our customers means that we are continuously evolving and improving our platform to better serve their needs. Who else can better help improve your product, than the users themselves.

About the Company

Recently, at a customer meeting, a conversation began about our current interface. After using the tool, the customer shared some suggestions to show checks and anomalies in a clearer way as their team seemed to be overwhelmed in places. Qualytics inferred hundreds of quality checks from her data automatically. While she was impressed, she also didn’t know where to begin and ensure she had complete quality check coverage of her data.

The Goals

Aggregate valid, accurate data from 3rd party sources

Catch anomalies – and attend to the information before sending it downstream

Create an accurate profile based off 3rd party data

Company Profile

Industry: Hospital & Health Care

Company size: 51-200 employees

Specialties: Healthcare Data, Data Aggregation

Feedback

This customer explained within the product interface, the number of checks in a list was overwhelming. This high number is a result of Qualytics generating several checks per field in the database or file store. (Do you mean not everyone likes to be overwhelmed with data?) In typical usage, this might mean inferring hundreds of checks per data store. It’s no surprise that this amount of checks became overwhelming to our users and made it difficult for them to know where to begin or understand where there is good test coverage.

Previously, in order to view checks, customers would go into the selected datastore, navigate to the checks tab, and then filter the table and field to the relevant checks. 

In the above example, we’ve focused on the S3 Demo Qualytics datastore looking at the gender field in the extended_warrenty table. We see there are three checks associated with this filter search. That’s easy enough, but it lacked complimentary information about the profile. It was also hard to quickly find fields without sufficient check coverage.

Solution

Using the same example, customers now go into the selected datastore, navigate to profile, and then click on the relevant field. In this example, we focused on gender. After clicking the gender field, metrics about gender drop down. Clicking on checks displays the three checks associated with the gender field on the right-hand side. This allows for our users to easily see what rule is being applied as well as details around the context of that check. Additionally, we introduced stronger visuals to help our customers easily see what is going on with their data.

Taking this into consideration, we designed a way to show checks (and anomalies) in context of profile information. This gives users better awareness of overall coverage and a heightened ability to concentrate on one field at a time.

Takeaway

We’re thankful for our customers’ feedback as it helps our enterprise data quality solution address what our customers value the most. We are constantly looking for new ways to bring value to our customers and look forward to what the future holds.

What is Behind the Scenes?

In October, we started a Behind the Scenes initiative at Qualytics to share monthly updates on some of our product features. We wanted to give insight into our amazing team of hardworking characters and provide customers, followers, and other interested parties a highlight on what we are working on. Get to know us along with our products.

Qualytics is the complete solution to instill trust and confidence in your enterprise data ecosystem. It seamlessly connects to your databases, warehouses, and source systems, proactively improving data quality through anomaly detection, signaling, workflow and enrichment. Check out our resource page or follow us on social media to make sure you do not miss our next event, blog, or webinar.

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