8 Ultimate Data Cleansing Tips for Effective B2B Databases

B2B Databases
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Data is a valuable asset for B2B databases marketers. Its quality directly impacts the insights gleaned from it and hence, the business operations depend on it. Corrupt data, missing entries, typing errors, or outdated information can lead to inefficient operations. Therefore, maintaining a clean and verified database is crucial as this forms the basis for launching successful sales and marketing campaigns. 

Let’s discuss the top data cleansing tips that can save your business from the impact of poor data quality, misdirected insights, and wrongly framed marketing efforts.

Plan A Data Cleansing Strategy

To begin with, craft a data quality plan that will work as a baseline for maintaining your data hygiene. As a data aggregator, you should set realistic parameters for maintaining a goal-oriented quality database. Such a plan will also guide data cleansing services experts and enable better data management. 

The process of data cleansing starts right from the point of entry. Firstly, the data sources are audited as per the specific business’s requirement and the database is examined to filter out weak links. One B2B marketer’s needs may differ from the other. Their data sources and expected data type/quality will also differ. Thus, robust planning becomes important. 

To create a data cleansing strategy, identify the key characteristics of data needed for your business, its KPIs, and metrics that will ensure the required standards. The following points can help you sketch a plan for your organization’s data cleansing:

  • Identifying your B2B marketing objectives and their data needs
  • Setting up metrics for measurement
  • Setting up KPI for regular data health monitoring
  • Identifying weak links and areas with dirty data
  • Identify types of databases required
  • Setting up limits to prevent over-cleansing
  • Setting up a timeline for review and audit

Automate Data Entry

Most of the datasets get corrupted at the entry point. Manual errors like missing entries, incomplete information, and outdated entries are a big concern for the marketing and sales teams.

So, a crucial data cleansing tip for B2B marketers is to catch the problem at the source. Identify the entry points of untidy data into your system. The cluttering of databases can be decreased by routinely sorting and enriching inbound data. In scenarios where manual data review or data entry doesn’t yield proper results, try using machine learning, RPA, and AI. 

By refining data, you can make sure that it includes only the relevant information. Marketing and customer service efforts can be improved with simple data enrichment techniques like contact validation and rectifying typos.

The AI and RPA tools can help you in the following ways:

  • AI and RPA can assist in maintaining accuracy and operate as per set data enrichment guidelines
  • It will audit data before it enters the master database as per standard.
  • It can rectify errors in real-time
  • It can merge data from multiple sources

Process the Odd Data Points

Detecting outliers (the data values with extremely low or high points in comparison to nearby data) is essential while preparing datasets for machine learning models. These special cases happen due to human or machine errors, data manipulation, mixing, execution, or extraction errors. It may also happen due to data mutation.

Methods like data visualization, linear regression, etc., help identify outliers. Once outliers in a dataset are found a careful analysis is required before processing them. Outliers can be detected by auditing and setting up filters. You can try to remove or change the found outliers, this will give you the confirmation before further processing. Analyze the underlying distribution before you process the data point. Generally, you can try to change the outlier value to make it match the dataset. 

Identify and Eliminate Duplicates

Analyze your database for duplicate information. Duplicate data wastes space in the database and results in inaccurate analysis. It also harms marketing efforts.

For example, John M and John Matthews are duplicate data entries with the same email contact. Now, if you send a marketing mail twice, it might annoy the customer and result in a loss of lead.

Often duplicate data have useful information, for instance, one data has a name and email, and the other has a name and phone number. Therefore, deduplication also needs caution. To meet such scenarios, data scrubbing services follow the process of merge/purge. Through this method, deduplication can be achieved without losing any relevant information.

Fill in Missing Values

Similar to duplicate values, missing values can jeopardize your crucial marketing and sales efforts. In the case of small datasets, you can manage missing values by eliminating respective cells. But, when it comes to large datasets, manually picking out these lists and columns can be a tedious job.

The following methods can help you with data imputation:

  • Data regression
  • Using machine learning-based automation processes
  • Audit Data for Pertinence

If your database is filled with data that cannot be utilized for processes, then it is of no use. Relevance analysis is the process of interpreting data and assigning context to it. The purpose of relevance analysis is to convert data into useful and actionable information by forming a connection with its usability.

Relevance analysis can be achieved by:

  • Automated platforms that analyze different segments of information in data quality metrics
  • Keeping useful, actionable data
  • Dropping off absolute data to save storage space

Systematic Cleansing and Supervision of B2B Databases

Quality issues rise exponentially in a steady stream of data. While manual error or team oversight poses a risk to any dataset, that issue amplifies when dealing with continuous incoming data from disparate sources. Even if you set standard operating procedures (SOPs) for data entry and management, inconspicuous errors can seep into your database. 

Poor data quality results in high bounce rates, low leads, even lower conversion, and a waste of time and effort. Therefore, data cleansing has to be a routine activity.

Regular monitoring and data cleansing can also be achieved with the help of the latest technologies. Machine learning-based AI can be used to find and remove identical entries, authenticate information, and validate databases. AI can utilize algorithms to identify loopholes in big databases and spot data biases.

Use Authentic Sources to Verify

Like other commodities, data has an age too; it will decay with time. The reason is changing situations around us, people switch companies resulting in profile and contact changes. Similarly, firms get merged and undergo management overhauling which also leads to data change. 

Cleansing and enhancing data values can help you deal with this decay. Such data can be appended by using third-party sources for accuracy and verification. 

You can append data through these tricks.

  • Verify and validate datasets regularly
  • Use verified authentic sources for data enrichment
  • Use automation technology to speed up the process of appending data

Clean Data Leads to Business Growth

These data cleansing tips for B2B marketers can help shape up your marketing and sales efforts, leading to better conversions. While AI and RPA can ease up data entry automation in real time, tools like job change notifiers, ping emails, etc., can assist in data validation.

Engaging data entry service providers, however, will allow you to avoid the complexities of data enrichment and instead concentrate on your core competencies while a rich database is built to support your operations.

Read more: New Approaches Of Generating Leads For B2B Companies

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