When we talk about bad data, it’s easy to get into the weeds of the topic and forget the main issues at hand; defining which types of data is bad and how to keep that data from negatively affecting your business metrics. Sure, this definition may seem simplistic. But when it comes to data quality, we strive for simplicity to make implementing your data quality process as intuitive and automated as possible.
What is Bad Data?
No matter the industry, bad data can be defined in just a few general classification. The vast majority of data quality professionals would break data quality issues in these terms:
Incomplete data - A data record that is missing the necessary values that render the record at least partially useless.
Invalid data - A data record that is complete, but has values that are not within pre-established parameters.
Inconsistent data - Data that is not entered in the same way as the other data records.
Unique data - A data record unlike any other previously measured data record.
Legacy Data - Defining data as too old can also be very important in order to utilize data that is still relevant to the organization, and excluding data that was collected before certain variables were established.
How Do We Deal with Bad Data?
Our data quality solution addresses these issues head-on, but allowing users to clearly define bad data based on the criteria above, as well additional criteria as needed. By setting parameters (called Rules in Naveego), users can define bad data to their exact specifications and address these issues with the help of automated alerts. This allows users to view bad data and take action before it makes its way into your dashboard.
Most importantly, facing data quality issues isn’t an overly time-consuming proposition. Naveego’s intuitive data quality solution can be installed and running in minutes. If you’re ready to take charge of defining and addressing your data quality issues, we can help.