Fun Fact: It's estimated that the amount of data created over the next three years will be more than all the data created over the last 30 years.1
If you don't have a solid data management process in place you'll find yourself in a mess that's almost impossible to get out of!
Why is data quality important?
Data quality measures how well data serves its intended purpose, its accuracy, and relevancy. High-quality data results in empowered, informed, and data-driven decisions that are the foundation of a successful inbound marketing strategy.
What is dirty data?
Dirty data comes in many forms — missing, inaccurate, outdated, or duplicate data is comparable to having no data. Empty fields, transposed letters or numbers, and data that isn't relevant for its intended use only provides you with an incomplete or inaccurate picture of leads and customers.
Dirty data significantly affects the performance of your business and, in some cases, proves catastrophic.
Characteristics of Data Quality
Data quality characteristics aren't a one-size-fits-all definition for every organization. Determining data quality requires examining its characteristics and weighing them according to what is most important to the organization and the use case.
- Accuracy and precision
- Completeness and comprehensiveness
- Relevance and timeliness
- Consistency and reliability
- Accessibility and availability
- Granularity and uniqueness
Quick wins for improving your data quality.
- Automate continued cleaning and scrubbing with HubSpot Operations Hub to ensure accuracy.
- Utilize HubSpot's bulk merge feature to eliminate duplicate records.
1. International Data Corporation (IDC)
