$15M Lost Annually To Poor Data Quality

Apr 22

Many organizations today are plagued by poor data quality management. This, in turn, is followed by huge burns in pockets. According to Gartner, poor data quality is knocking companies to the ground – to the tune of $15 million as the average annual financial cost. Wasted resources and expenditures for operational inefficiencies, missed sales and untaken chances come complete with poor quality data.

We get it, it's a tough call. So if you're still struggling with bad data, we're here to shed light on data quality and top practices to make your data sets serve the goals. Let's reliably deliver insight into your company.

Establish meaningful metrics

By setting up a program of data quality metrics and measuring religiously companies can raise awareness of how critical data quality is for the organization. As for the exact metrics, your mileage can vary. The golden rule here is to make them applicable to the goals and business targets you are aiming for with your data. Thus, your metrics can target the accuracy, completeness, or validity of your data. You can also assess the number of redundant entries or format-incompatible data.

Trust but validate

Data does not have a long shelf-life. Therefore, all data needs to be validated, i.e. checked for accuracy, clarity, and details. When moving and merging data it’s vital to check the conformity of diverse data sets to business rules. Otherwise, you risk basing decisions on imperfect and inconsistent data. Example validation checks may include:

Ensuring that age is entered as a whole number
Ensuring that the email address includes the @ symbol
Ensuring that usernames include only letters and numbers, etc.

This requires having the right tools and the right processes. On this note, Soda.io makes it easy to test and validate your data against the business rules that are vital to your company.

Implement a single source of truth

In its essence, a single source of truth refers to the data practice when all business-critical data is stored in one place. An SSOT ensures that all team players base their decisions on the same data via a single reference point. Instead of being a specific software, it’s more of a state of mind for your company. An SSOT can be anything from a simple doc to a sophisticated data information architecture your organization leverages.

A pro tip (or two): In today’s remote-first environment, it’s important to check that an SSOT is accessible to all team players. Also, grant independent access to the team if you’re collaborating with folks in another time zone.

The End

Getting in front of data quality is both terrifying and exciting. Probably, that is the main reason why most companies don’t give data quality its due. But bad data is not a norm. If you are looking to reduce the number of mistakes, budget dollars, and unwise business decisions, you should definitely go the extra mile with your data sets.

Join us to thank today's sponsor, Soda.io. Soda helps you get ahead of silent (yet fatal) data quality issues by implementing automated monitoring, using testing and validation, along with examining data fitness. Meet them at SODA LIVE on May 12th, 2021 @ 4 - 6:30 PM EST to discover how to deliver trusted data.