Part 2: This is the second of eight posts on how to measure data quality. This post describes why completeness is a good measure and how it’s used.
Completeness is the measure of whether data exists or not. For example, if you have an email field where only 50,000 are present from a total of 75,000 records, then the email is 66.6% complete. This the most basic measure in the Seven Dimensions of Data Quality. So why is it important?
Let’s use the email example to show what massive impact completeness can make. If you are performing an email campaign with a success rate of 1% lead to sales ratio. With an average order value of £250.00 per lead, 1% of 50,000 emails is 500 new customers, bringing in 500 x £250 worth of business, i.e. £125,000 of sales.
Now, increasing the number of emails from 50,000 to 60,000 is an increase of 10,000 emails. 1% of these can be sold to through the existing campaign, which is 100 x £250 or £25,000 of extra revenue. So, by capturing new emails a significant increase in revenues can be made.
However, this is not the whole story, if your customers spend on average £1,000 with you over their lifetime, then the extra 100 sales from the new emails will generate £100,000 over the lifetime of these customers.
Placing effort in capturing emails through training for users and/or acquiring emails through a telemarketing campaign can be a very rewarding exercise.
Completeness of key information is very important, missing data is more than just a cost issues it’s a massive lost opportunity issue.
The other dimensions are:
- Accuracy (Part 3 of 8)
- Consistency (Part 4 of 8)
- Conformity (Part 5 of 8)
- Currency (Part 6 of 8)
- Duplication (Part 7 of 8)
- Integrity (Part 8 of 8)