But data quality relies on more than just the data being collected. To maintain good quality data, cleaning and maintenance is essential. And if you have a collection of data that you want to maintain, here are some data cleansing best practices that you should follow.

1. Create a data quality strategy
The first step to handling your data cleansing is by creating a strategy that best suits your needs. The strategy you create should depend on the type of data that you need to maintain, the size of your data set, how frequently you need it appended and the resources that you have available to accomplish your data cleansing.

2. Standardize data formats
The format of your data can also affect the way you are maintaining your data. To help you become more efficient in recording and keeping your data in good quality, see to it that you apply a standard data format across all records in your database. If you are collecting phone numbers, see to it that the data field will only accept numbers. If it’s dates, see to it that you have a standard method of recording dates. Same goes with all other records. This way, there will be less issues with wrong data inputs in your database.

3. Schedule regular data discovery sessions
As part of your data cleansing, see to it that you have a regular schedule for checking your records. A regular data discovery session will help you eliminate erroneous or duplicate records more frequently, so that they won’t pile up and become an even bigger problem in the future.

4. Clearly identify goals of data
Your data entry and quality team should all be aware of the reason why you are keeping the data you are collecting. They should know what it is or will be used for. This way, they will give more value to recording each piece of data more accurately, helping eliminate any problems that may arise from poor quality data brought about by erroneous data entry.

5. Have a quality team validate data accuracy
If your database is being updated manually by your team’s data entry specialists, you should also make sure that you have a quality team to check the accuracy of the data entered in your database. Your quality team can help you maintain good data hygiene and help you avoid any costly mistakes brought about by poor quality data.

6. Identify and eliminate duplicates
Another important step in keeping your data clean is checking, not just inaccuracies, but also duplicates. Duplicate records can also affect your data’s quality as it can add up to your records and make you derive incorrect numbers of data sets. For example, if a customer’s records were recorded differently three ways, then there are two additional records that are unnecessary and incorrect. If the same thing happens to hundreds or thousands of your customer records and you decide to send out marketing emails to all of those, then these duplicate records will only add up to your work and eat up essential marketing resources.

7. Label files accordingly
The way you handle and store your files can also affect the quality of your data. To help you avoid committing mistakes when adding new records to your files, see to it that they are properly labeled. This can help you save time in looking for files that you need to update or add records to. Properly labeling your files can also make it easier for you to store your records, without getting lost in your library of information.

8. Check special characters in data
Some records will require special characters, while some don’t. So if you want to avoid entry of special characters in your database, you have two ways. One is to totally disallow the use of any type of special characters and the other one is to allow it, but only on specific fields. If you have decided to allow special characters, see to it that you check how they will appear in your records as these special characters might look differently once they are in your database. Maintaining good quality data requires a lot of work, but it is an investment that is worth taking, especially for businesses. Data cleaning will need a team of people who are dedicated to helping you make the most of your data. If you think this is something that you should outsource, Assivo can handle your data cleansing needs for you. Schedule a consultation with us and let us help you set up a data cleansing solution that is perfect for your business.

Data cleaning will need a team of people who are dedicated to helping you make the most of your data. If you think this is something that you should outsource, Assivo can handle your data cleansing needs for you. Schedule a consultation with us and let us help you set up a data cleansing solution that is perfect for your business.