Cleaning Your Data: Best Practices for Data Cleansing
In today’s information-driven world, data plays a very important role when it comes to making decisions. The right set of data can help us make an informed decision and stop us from committing costly mistakes. The same thing goes for businesses. In fact, data can be one of the most important resources any business can ever acquire. With high-quality data on hand, a company can properly scale business, build a solid customer database, and increase return of investment.
But data quality relies on more than just the data being collected. To maintain good quality data, cleaning and maintenance is essential. If you have a collection of data that you want to maintain, here are some data cleansing best practices to follow.
1. Create a data quality strategy
The first step to handling data cleansing is to create a strategy that best suits your needs. The strategy 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 maintain it. To help you become more efficient in recording and keeping your data in good quality, apply a standard data format across all records in your database. Consider field validation and field format requirements for different data types such as phone number fields that only accept numbers. This way, there will be fewer issues with incorrect data input in your database.
3. Schedule regular data discovery sessions
As part of your data cleansing, develop a regular schedule for checking your records. Starting out with a data discovery session will 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 reasons why you are keeping the data you are collecting. They should know what it is or how you intend to use it. 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 data entry specialists, implement a quality team or review process to check the accuracy of the data entered in your database. Your quality team can help you maintain good data hygiene and avoid costly mistakes brought about by poor quality data.
6. Identify and eliminate duplicates
Another important step in keeping your data clean is checking for duplicates. Duplicate records can also affect your data’s quality as it can add up to your records and make you derive incorrect numbers from data sets. For example, if a customer’s account record was entered three different ways, then there are two additional records that are unnecessary and incorrect. If the same thing happens to hundreds or thousands of 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 use 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, make sure they are properly labeled. This will save time looking for files that you need to update. Properly labeling files can also make it easier to store your records without getting lost in your library of information.
8. Check special characters in data
Some records will require special characters. If you want to avoid entry of special characters in your database, you have two options. One is to completely prevent the entry of any special characters and the other one is to allow the characters but only in specific fields. If you decided to allow special characters, check how they will appear in your records since they might look differently once they are in your database.
Maintaining quality data requires a lot of effort, but it is an investment that is worth making. Data cleaning will require a team of people 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.
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