Data Cleansing Tips for B2B Marketers: A Few Best Practices

Data cleansing is as important as collecting business data. 

Maintaining B2B data quality is a task every B2B marketing team has to constantly streamline. All of this involves spend a significant amount of time to track, maintain and organize contact lists, profiles, customer sales data, demographics and other forms of data. 

As per a research, 47% of new data records have at least one major error. And, we are talking about the fresh records, forget about pointing old errors old databases might have. 

Irrespective of the quality of the data gathered, data requires validation and, data cleansing becomes a mandatory step for B2B marketers. There are various tools that help in handling various facets of data cleaning tasks. Have a look at some notable steps and tips:

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• Creating a data quality plan

For any project in your organization, creating a structure and plan is necessary. 27% of today’s data aggregators are not sure of what their database should look like. It is critical for you to craft a quality data plan to have a realistic baseline of data hygiene. 

• Data entry management with the help of AI and RPA

Check the problem right at the source and it is going to help you prevent questionable data from entering or staying in your system. With the use of RPA, AI and machine learning, data entry can be streamlined in real time. You can also create a Standard Operating Procedure (SOP) to get quality data in your database. 

• Detection and processing of outliers 

Outliers are special cases and they must be handled very carefully during data cleansing processes. To prepare data sets for machine learning model, you must detect outliers, analyze and process them. Detect outliers with the help of data visualization methods, linear regression, and others. 

• Constantly monitoring and cleaning data

The more bad-data you have, the higher will be your bounce rates, you’ll have lower clicks and lower conversions. So, it is time your channel and invest your efforts towards automated systems that help to append, verify and validate data. Empower your systems with the latest technologies to reduce data irregularities. 

• Using authentic sources to append data 

The rate of data decay can range between 30% – 70%. In such cases, it is essential to fill gaps with the help of cleansing data from time to time. Keep your datasets updated and append data from authentic sources only. 

Here are a few top data cleansing tools to help validate your data: 

• Xplenty

An advanced data pipeline platform offering ELT, ETL, along with replication functionality, which is easy to set-up with no code graphic interface. Using ETL, any data aggregator can clean the data and transform it completely before moving to any data lake, or data warehouse. 

• Tibco Clarity

It is an interactive platform for data cleansing. Powered by a visual interface, it helps to streamline data quality improvements, data discovery and transformation. Any type of raw data can be run through the solution to prepare it for use in further applications. 

• DemandTools

The data cleaning is tool is specially designed for Microsoft Dynamics 365 CRM and Salesforce CRM data cleansing. With the help of this tool, data cleansing, discovery and maintenance become easy. This module is dedicated to improve the quality of data by fixing and stopping duplicate records and managing lead conversions.

• RingLead

It is known as a comprehensive data orchestration platform, specialized in marketing automation data and CRM. It offers end-to-end automation solutions and data cleaning is one of the features. The data quality features are de-duplication, normalization, linking leads to accounts, data enrichment and data discovery. 

• SAS Data Quality

SAS Data Quality, is designed to clean data wherever it is. With this tool, there is no need to transfer the data to another place and you can run the cleaning prompt right where the data is located. Along with de-duplication, correction and data remediation, some additional functionalities of this tool are data governance, master data management, data quality monitoring, data visualization and integration. 

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Future of Data Cleaning

Hyper automation is the future of B2B data management. As the growth of AI and ML tools accelerates, the focus of data cleansing is also going to change. The aim should be develop and deploy robust data cleaning processes that prevent any questionable data from entering your system in the first place.