Tips & Tricks

Sustaining CRM B2B Data quality

Professional organizations evaluate the business acumen and skills of their vendors closely. Any evidence of using inaccurate data in a B2B transaction could be deal breaker.

Maintaining B2B data quality

  • A Master Template
To ensure that all relevant data is recorded and to aid data consistency, a standardized master template needs to be first developed with a user guide. In the template mandatory and optional fields are defined along with classification of critical and non-critical data.

This one-step goes a long way in maintaining data consistency, quality and value.

  • Validate data using external sources:
Some data can be cross-validated using records of the same businesses published by other  organizations like the US Postal Service or Yellow Pages. When more such external references are used to cross validate, data becomes more reliable and up to date.

  • Data governance = Control:
Access control is a critical part of data governance, especially closely controlling permissions for writing data into a database. Clear definitions of Data Stewardship Roles help in controlling data quality. Empowerment of data stewards to penalize non-compliant personnel helps enforce data governance rules.

A communication plan to report routine and exceptional events is vital to customer data quality.

Finally, Metadata is as critical as data. Data without clear descriptions explaining its meaning  is of little value.

  • Data Cleaning
Periodic data cleaning enhances data value. Deletion of duplicate records, purging of old records and merging of different data records with similar data into one, help in keeping the database compact, clean and manageable.

Several of these operations can be automated using data cleaning software.

  • Data monitoring metrics
Simple Measurements of certain dimensions of your CRM data can describe your data’s health.

One such measurements is Uniqueness  which indicates if there are duplicate data in your database. Higher the percentage of uniqueness, lower the number of duplicate records and cleaner the  database.

Accuracy is another useful measure. Data samples scientifically drawn from the database can be compared to external reference data such as data from Dun and Bradstreet. The percentage of accurate records will give you an idea of approximately how many bad records you can expect to find in your CRM database.

Consistency measures the compliance with data governance policies and processes. If you created a master for instance, randomly picking datasets entered by different individuals or departments and comparing them to the master template will indicate the extent of compliance to mandated norms.

Completeness can be assessed at different levels. The percentage of completed fields in the mandated fields is one important measure. The percentage of completed fields in the optional fields is a recommended measure. If the percentage of completeness of verified mandated fields is close to a 100%, then your database is valuable. If the optional fields are also almost fully populated, you know your data governance is effective.

Corporate and Businesses can be unforgiving when they observe inaccurate data used in marketing or sales activities. Clean data is vital to confidence building in your relationship with your customers.

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