6 qualities of good data

Having accurate data is one of the most important things an organization can do to succeed. Data can be used to clarify past events, create predictions for future events and to recommend steps that can drive members toward a desired goal.

But if your data is outdated, wrong, exclusive, or not recorded right, your organization could be following a strategic plan not fit for success. That’s why it is so important to make sure your data is up-to-date. Low-quality data may in-fact be worse than no data at all.

As business analytics expert and Sidecar course presenter Thad Lurie says, low-quality data is a real problem for most organizations because:

• 15% of email users change their email address one or more times a year
• 20% of all postal addresses change every year
• 18% of all telephone numbers change every year
• 21% of all CEOs change every year
• 25-33% of email addresses become outdated every year
• 60% of people change job titles within their organizations each year

So how do we address this issue?

Stay up-to-date! Offer surveys once or twice a year to figure out how inclusive your organization is, try to avoid manual data entry, have a strong organization plan for the data and have a clear and concise style guide for data entries. 

Keep these six dimensions of data quality in mind when examining your organization's data:

• Completeness indicates whether the data gathered is sufficient to draw conclusions. This can be assessed by ensuring there is no missing information in any data set.
• Consistency ensures data across all the systems in an organization is synchronized and reflects the same information. An example of consistent data includes recording the enrollment date in the same date format as in a member’s information spreadsheet.
• Accuracy implies whether the data represents what it should. This can be measured against source data and validated against user-defined association rules/bi-laws.
• Timeliness means the data is available when expected to facilitate data-driven decision making.
• Uniqueness involves making sure there are no duplicates present in the data. For example, the lack of unique data can result in multiple emails being sent to a single member due to duplicate records.
• Validity measures whether data meets the standards or criteria set by the business user.

Want to learn more about data through our Introduction to Business Analytics course? Sidecar members have the opportunity to access this course and more than 15 others through Sidecar Academy. 

Not a member? That’s okay! Sign up here.