What is Dirty CRM Data and How to Fix It?

crm data

CRM provides the businesses with data-driven solutions for their problems and it is necessary to question the data before processing it and taking decisions accordingly.

According to a report published by IBM, the “dirty data” phenomenon cost $3.1 trillion annually to just US economy.

An individual business could potentially be losing 12% of its revenue to dirty data on annual basis.

The same dirty data phenomenon could affect your CRM in a pretty negative way.

For example, just the miscalculations of customers could prove fatal for your overall business.

We need to ponder upon this phenomenon and how to fix this problem before it lands your business in deep waters even though you have spent a lot on customer relationship management resources.

Dirty Data

The success of a business depends upon the decisions being made by the management every day. The decisions are influenced by the data being provided to the management.

It is quite unfortunate that a big chunk of information comes in raw and requires a lot of processing in order to make it strategy-grade information.

Dirty data is basically unprocessed raw information that is deeply flawed and it could become a dead-weight for a CRM as well as a company making decisions upon it.

Types of Dirty Data

Dirty Data has different types but it can generally be defined as inaccurate information.

The fraudulent data could result in developing strategies that wouldn’t benefit your organization in the future. Let’s have a look at different types of dirty data:

Data handling in CRM

Fraudulent Data: It is false data either collected by human beings or by bots in your CRM and it is unhealthy for your overall team’s performance.

Invalid Data: This is the data being entered in the wrong fields in the CRM. Such kind of data can crash your software, or there could be a problem in information processing in the system.

Duplicate Data: This is the portion of data which refers to the customer records which are duplicated and you would find several addresses or accounts under the same names which create quite a number of issues.

Old Data: The data which is no longer useful as it has not been updated timely is also a type of dirty data.

Incomplete Data: The data which lacks the proper detail and relevant information for a single or more than one data fields comes under the banner of incomplete data.

How to Keep the Data Clean?

No matter what is your business it is exposed to dirty data at some point and you need to be aware of that. It could be a human error, floating data during data migration, or invalid information.

Even valid data becomes dirty data after a while if it is not updated on regular basis.

As it could be a really worrying situation for your business if dirty data enters your system but there are several ways to stop this from happening beforehand.

We would like to tell you about all of the suggested steps needed to be taken to gather clean data instead of raw information.

  • Data quality assurance needs to be your top priority in the organization. Create a policy to ensure data cleanliness.
  • Start data quality checking today and ensure that every person in your company starts checking the existing data.
  • Identify the points in the workflow where you have higher chances of inaccurate data entering the system.
  • Create a proper strategy to screen out inaccurate data manually as well as automatically.
  • Try to automate data entry in order to avoid human error.
  • Data quality assurance needs to be a continuous process in your workflow instead of just a one-time commitment.

Conclusion

Data is slowly turning into one of the most important piece of information that we could possibly have attained. Its usage and demand is increasing day by day, and with the business that we are indulging in its our duty to make sure that the ‘dirty data’ phenomenon is lessened as the data is being handled.


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