The Concept of Dirty Data in B2B Marketing

Data that is incomplete, inaccurate and has several errors can be termed as dirty data. The errors also include language errors like typos, spelling errors, mistakes in punctuation and so on. Such data affects the database. Data that has been duplicated in the database is also considered as dirty data.

Dirty data results in several negative consequences in the B2B domain. In case of B2B marketing it affects the email communications, online presence of the company and the campaign performance. There are several long term effects on the business strategies and the business analytics as well. Business strategists need to look into prevention and elimination of dirty data.

Dirty data can result due to various reasons:



Outdated data


Updating data is an important aspect of database management. If the data is not updated at appropriate time intervals, it results in outdated data. This creates a very impact on the business management.

Erroneous data


This includes errors like typos, spellings, punctuation errors, broken links, and other errors. This happens when the data is not reviewed properly.

Incorrect facts


This includes use of incorrect phone numbers, titles, content and fake content like contact details like email addresses. Similarly factual data like numerical also need to be perfect. All these facts need to be verified.

Duplicate records


If the content is repeated or duplicated for some reason in the database, it becomes redundant. Redundancy also gives an impression of unprofessional approach. It is therefore essential to review the database content.

Non-integrated data


As database development occurs in phases and cycles, there are certain steps that need to be followed systematically to integrate newly developed data into the records. If this integration is not done in a correct way, this results in an erroneous and incomplete data.

Data without rules


Technical and business writing begins with the basic rule of developing correct and factual data that is developed on rules and regulations. Thus the data should follow the rules related to the technology or the domain.

Incorrectly formatted data


Whether it is a webpage, blog or documents uploaded on the website, the data needs to be perfect in terms of formatting and layout.

Misleading data


Information that is not written correctly or that may be unorganized can be misleading at times. The aim of any type of information should be to make it user friendly. The audience should be able to understand the data and use it appropriately as and when required.

Data inconsistency


If data is not verified or reviewed when checking in, it leads to data redundancy. Updated data may not replace the older one or may be repeated. This leads to inconsistency. Incomplete data is not only a type of data inconsistency but also represents incorrect data.

In case of marketing organizations that use automation platform the dirty data infiltrates into the entire database and affects the reputation of an organization. The negative effects of dirty data can be eliminated by implementing strategies for increasing data quality. This ensures validity, completeness and authenticity of data.

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