How to measure data quality: A comprehensive guide

The value of data is dependent on how accurate and up-to-date the information is. Bad data isn’t just counterproductive, it can also be costly. A Gartner report revealed that bad data costs companies an average of $12.9 million in losses every year. This is why consistently measuring data quality can make or break your business. Learn the best practices for assessing data and ensuring the incoming information is precise.

Metrics for measuring data quality

Measuring data quality, or data profiling as it’s also known, requires a set of metrics when gauging its reliability. Here are the top data quality metrics utilized by marketers for analytics purposes.

Precision

Data precision refers to the information’s accuracy. For example, if a customer incorrectly inputs his birthdate as 1979 instead of 1999, this could render the data inaccurate, as the customer could be categorized in the wrong demographic group.

Completeness

How complete is the data? A good example is the degree that customers fill out their profiles. How much of the information is left out? The more sections that are left blank the lower the data quality.

Integrity (also known as Validity)

Is the data inputted in the correct format? Even if the information is accurate, it may be invalid if it’s entered incorrectly. For example, entering a phone number as 555-5555 when the required format is 5555555 (no dash).

Consistency

Same data in different repositories must be consistent. Using the same phone number scenario, if the number is entered as 555-5555 in one repository and 5555555 in another, the system may fail to recognize the two number sets as being one and the same.

Timeliness

How up-to-date is the data? If a customer makes a purchase, and the information isn’t updated, it can lead to imprecise campaigns. For instance, you could end up recommending the same product the customer just purchased.

Factors that affect data quality

The quality of data mainly comes down to company procedures regarding data storage and handling. These variables can impact data reliability.

Data silos

Data silos can be problematic. Data can rest simultaneously in two locations, with the information slightly different in each location. It may also rest in only one location accessible to a select few authorized personnel, rendering the data largely unusable.

Data governance

Governance (or data quality rules) comprises a set of guidelines for storing, obtaining, and retrieving data. Most industries have their own national or international standards. HIPAA for the medical industry is a prime example. Lack of governance or its enforcement can erode data quality.

Lack of defined data

You need a clear definition of each data input. For example, you may have 100 active customers, according to data. However, what’s the definition of an “active customer?” Is it someone who makes a purchase in the last 30 days, 60 days, or someone who made multiple purchases within X amount of time?

Changing data sources 

It’s not unusual to change data sources, such as acquiring data from social media surveys, before switching to an email questionnaire. While seemingly minor, this can and does have an effect on data quality. It may affect the response rate or how users respond.

Best practices for data quality assessment

To get the maximum benefits of data quality measurement, you need a set of practices to ensure incoming information is accurate and usable. Here are trends you can follow for your data quality assessment practices.

Create a company culture that respects data management

Your staff needs to understand the importance of data quality and how it impacts day-to-day decision-making processes. Otherwise, they may get complacent with how they handle the data. They may file data in the wrong directory or make unauthorized copies for their own use. Create a training course that outlines the why, what, and how of data quality. Be clear about the procedures and assign a data steward that monitors employee handling of data.

Automate the data quality management process

Automating data quality is multi-beneficial. For starters, it eliminates human error, which can greatly contribute to poor data quality. Automated auditing can also detect errors, such as data duplicates, incorrect filing, naming, etc. Automated data quality is almost always better than data logged manually.

Establish a single source of truth (SSOT)

SSOT means that entire departments and teams make decisions based on a single data set. This ensures everyone is operating off the same information. If one person is looking at data set A and another at data set B, it can lead to miscommunication, disagreements, and campaigns based on faulty data. One of the first agendas in a campaign should be determining the data set(s) that are going to be the go-to information source for the project.

How Lytics can help you measure data quality

Your company needs a succinct way of organizing your data quality framework. Lytics provides a number of tools that automatically sort, recall, and filter data as it comes in in real-time. With Cloud Connect, create an SSOT to ensure everyone has access to a uniform data set. With the push of a button, integrate data from disparate sources and eliminate data silos altogether.