Best Practices For Database Cleansing

In today’s scenario where almost 70% of the business relies on data for growth, more than 90 percent of B2B customers complain about data inaccuracy.

Against this backdrop, it is essential to keep a close watch on the sanity of the database. You have to be confident about the quality of the data.

Astonishingly, a large percentage (almost half) of the marketers do not validate their data for accuracy and quality. Amongst the remaining half, a majority of people use incomplete or invalid prospect data.

It can give you a big picture about so-called “dirty data” that could stand your way and drive a wedge between your customer base whom you want to reach and your business prospects.

Data is vital to survive and grow in the harsh competition today. It is seen as the cause of all good and bad decisions. It is a factor that enables businesses to gain actionable insights. Incomplete and inefficient database hygiene practices are big hurdles.

You need to follow the best practices for database cleansing to have clean, validated, accurate, and standardized data. Let’s know about the best practices to deliver a superior customer experience.

Develop mechanisms to validate data accuracy

The more accurate your data, the more qualitative it is. Effective results can be obtained when high-quality data and tools seamlessly merge several data sets. You need to find out the best tools amongst various available in the market

Appropriate data hygiene tools such as list import can validate data accuracy better. It is possible to do it without using any tools as well, but it will need a lot of effort and human intervention. Being an entrepreneur, you do not have the bandwidth for it.

You can outsource the task to some data validation expert that can verify it.

Develop A Data Standardization Protocol At The Entry Point

Experts say that a lot of improvement can be made by restricting the ‘dirty data’ at the entry point itself. It will save efforts in cleansing the database later. Do not let unhealthy data enter your system. It will maintain good data hygiene.

Check all your critical data at the entry point using standardized data entry protocol. It will guarantee standardized information input.

It also makes the task of checking duplicates easy. One good idea is to make an SOP (Standard Operating Procedure) for data entry. When you follow the SOP, it will allow only quality data into the CRM.

The task of making an SOP becomes easy when you hire a qualified and skilled technician.

Filter Out All Duplicates

Duplicates are the biggest hurdles in data accuracy. Not only that, they waste your data handling efforts also.

Duplicates are costly. You have to pay too much in database maintenance, campaign spending, and so on. They also prevent you from having a one-point customer window, which is very essential in today’s business world.

Your brand reputation gets impacted, and it leaves a bad taste to your customer.

Duplicates cause trouble in data reporting also.

Therefore, all possible efforts need to be maintained to check data duplication. It is the fundamental thing.

Develop a Data Quality Plan

You need to set expectations for the data. For that, sit and develop the basic data quality key performance indicators or KPIs. What are those indicators, and how will you meet them?

How will you track the health of your database? How will be one-time data cleaning be done and maintain data hygiene on an ongoing basis?

You need to develop a mechanism for that.

First, you must know what are the most quality errors? Where do they occur? Search for incorrect data. Find out the root cause of data health issues. It requires a detailed plan for ensuring the good health of the data.

Append Data

Once you have a sufficient set of data for each record, now it is time to append the data and make it complete.

Let’s understand it by an example. You may have the first name, last name, email address, and business address for the contact record of your customers. There could be some better database that gives some other relevant data, e.g., title, phone number, annual income, etc.

You can append the other data to make your existing database complete. When you append the data, you retain all the existing details and have some additional information.

Validate the data once again to ensure its accuracy and completeness.

Conclusion

Data cleansing enables you to detect and remove fatal data errors and inconsistencies, whether you work with a single data source or multiple. By implementing data cleansing strategies and tools, the efforts of manual inspection can be reduced and processes can be streamlined better.

Companies that are better equipped to think about the health and quality of the data can achieve better performance and higher ROI on the database.