May 24, 2016
~by Katie Favreau, Jennifer Martell & Paul Harris
We know good data when we see it, and we also know when it’s not so good. That’s why we decided to give you some insight to some of our top “Bad Data Habits” that we see frequently in global databases. Here are some of the our top bad habits.
- Trying to store international addresses in a database meant for USA.
This habit is common on data capture forms that were designed without consideration for global address systems. All address systems are not the same and when you have a required “state” field in a country that doesn’t have any states, you’re bound to end up with a some data problems. Do your homework and know your market. If the database you’re creating will reach a global customer, you should consider how the data will be entered and stored.
See the example below of a good global data capture form.
- Proper fielding when data entering is essential.
The best example here is not requiring a “country” field. If a contact has only a street address and name without an identified country, that makes the address not correctable. Our earth has many people and without some essential data points, it’s like finding a needle in a really big haystack.
- Excel corrupting data.
Excel is without a doubt a powerful tool. However, sometimes it’s not your friend. Simple commas can corrupt your data fields if you’re not careful. We’ve seen excel do some funky things in the past and we recommend you are very careful when storing data in excel. The more data you have, the more chances you have for excel to corrupt it.
See the video below for an example of how excel can corrupt your data with its autocorrection rules.
- Placing contact names and business names in the same field.
This is a no-no that needs to be nipped in the bud fast. These data points should always be in separate fields. Here is an example, First Name: Paul, Last Name: Harris, Company Name: Global-Z International. You shouldn’t have “Paul Harris Global-Z International” all in one field.
- Not checking for dupes when new data is entered.
Duplicate data becomes unmanageable fast. A quick check before a new record is entered in a database will prevent much anger and confusion in the long run. Nothing slows you down like doing a look-up on the name “John Smith” only to find you have 13 different “John Smith” records and all of them have conflicting information populated in the fields.