8 Data Cleansing Best Practices
Data cleansing best practices can help mitigate some of the data quality issues and reduce the waste of resources on poorly targeted campaigns or messages sent to incorrect addresses.
It is very likely that your business is collecting and storing some kind of bad data where sometimes it is not even in your control. A lot of people deliberately provide false information while signing up for some online service which leads to businesses having bad or incorrect data.
However, even if this happens you can be proactive in maintaining the condition of your data. Data cleansing best practices can be used to get actionable data which can ultimately become a lead and key asset to your business. This means that you have access to the data which is correct and complete and can be relied upon while making important business decisions.
Businesses always tend to have a few important data left out. A lot of necessary data are most probably present in different places, both internal as well as external. You can, therefore, gather the data around and look closely at it by following data cleansing best practices.
We have gathered around 8 data cleansing best practices to make sure you get high-quality data to get in touch with your target customers and prospects. These qualifiers are:
You cannot just start cleaning your data from anywhere, you need to strategize it. You should first identify where most of the incorrect data and errors occur. An extensive data cleaning strategy will have an effect on different departments which makes it necessary to keep the communication open and emphasize the fact that better intelligence will save a lot of money for everyone.
Validating your data in real time while cleaning up the existing database simultaneously is compulsory. It is a proven fact that investing in a team of data solution providers has really high returns. With their data cleansing tools, they can clean the important information such as list imports, verification of addresses, etc. Only when the data quality is high the marketing can be effective and the experts can merge different data sets seamlessly.
3. Eliminate Duplicate Records
The process of actively identifying errors such as duplicate records and removing them is known as de-cleansing. You can save the time of your team and incorporate this practice to identify duplicate entries effectively.
Having data with complete information is ideal where all the fields are filled in, leaving no room for incomplete data. Since empty fields are really hard to fill in owing to the fact that you cannot exactly guess the information that wasn’t captured when the data was recorded initially. For example, you call Mr. X and ask for a few information but you forget to note down all of the notes of the call and then remember about the same a few weeks later; you will, most probably, not be able to recollect all the information that Mr. X had shared with you.
Uniformity is all about keeping all the information about your database in line with each other. Meaning, they should always be consistent throughout. For example, if you are maintaining a database for people working as per the Pacific Standard Time Zone, keep all the times as PST or if you are working on maintaining a database dealing with a person’s weight, either use ‘kg’ or ‘lbs’.
By standardizing, we mean standardizing the data at the entry point itself. Checking all the data, even if it is not important, at the point of entry can be very helpful. By doing so, you can make sure that all the information is standardized when entering your database, which makes it easier to catch duplicate entries. Data modeling professionals can help your business cope up with such a requirement very easily.
7. Appending Data
Imagine you have only the name, email and business address of a contact record. What if you could get their phone number, title, annual revenue, and their location? To avoid violating GDPR you should understand not only the business address of a company but also the location of each contact at the company. Having incomplete data for each record is considered as white space. Some software companies capture this information directly from the first party sites since people disclose this information themselves, thereby making it accurate. These software tools can also clean as well as compile the data and provides complete information for business analytics and intelligence.
8. Target Prospects and Customers
This is one of the most important data cleansing best practices. While cleansing the data you need to make sure that only the information of customers and prospects who look like a potential lead are to be stored and corrected and the irrelevant data is to be eliminated. For this, you need to identify your target prospects and customers and then go ahead with cleaning data. A clean data lets you target the potential lead in a more effective manner.
The benefits of Data cleansing helps to develop and strengthen your customer segmentation and make sure you have a single customer view. Since clean and accurate data renders more efficient results than using old, unattended data, it is very crucial for your business to take data cleansing best practices into consideration, if you want higher productivity and revenue rate. Data cleansing will help you realize that poor data quality always leads to loss of revenue, productivity and time.
You should always remember that data cleansing best practices lead to better analytics. This also explains the reason why data cleansing is on the rise in the market.