– And How To Meet Them

In the world of data processing and management, data cleansing is one thing that should not be ignored. This, because no matter how careful an operator of data is in the process, mistakes happen. The need to cleanse or update data will arise. Some inconsistencies rear ugly head and often, data cleansing becomes a resort for any serious organization.

Organizations are advised to see data cleansing as one of the maintenance ethics in data management. We must endeavour to appreciate the good tidings that come with data cleansing. However, we must acknowledge the pitfalls that come sometimes from cleansing data.

Some Of These Problems Encountered Can Be Countered By The Operator. And This Is Achieved Only When The Operator Has Prior Knowledge About Possible Data Cleansing Challenges. Which Are :

High volume of data

There is only so much a system can take in when it comes to information and data. Sometimes, during data cleansing, applications that are used to clean data produce more voluminous data. These applications may turn into data errors and hinder the smooth sailing of data processing.

So, an operator must ensure to look out for high volume of data even during data cleansing.

Missing values

Here is another problem commonly found in the process of data entry. Things that a layman would want to overlook are missing values, omission and so on. A bunch of these things can pose a huge hindrance to the smooth sailing of data entry.

When a space for an information has been provided for an operator to fill – let us say age, the operator omitting or deciding not to fill this may not look so much like a big deal until it actually becomes one. So, missing values can be quite the thing to fuss about.


Another thing that will always pose a threat to data cleansing and data processing is misspelling. There has always been an agenda to fuel correctness when it comes to filling out information. News flash; things are not about to change overnight.

It will only speak well to avoid misspelling if the operator wants to avoid data issues at all cost.

Misspelling will not be if there were no typing errors; it becomes even trickier when information like addresses are in question. This, because most addresses are not dictionary corrected. This may cause a lot of misunderstanding if there is a need for the said information in the future.

Misfielded value

Something may be right but not right for a particular movement. This is usually how a misfielded value is perceived and rightly so. Some formats have been designed but on closer look, these formats are okay but not necessary for the type of information required.

There is displacement of value and although, it is not often something operators pay close attention to. Sometimes it translates to; there is still time to discuss an issue in data cleansing that should get more attention.


No matter how unbelievable a data is, there is something that should not be compromised. It is quite important that there is an unquestionable consistency when it comes to information about identity.

Irregularities in data can inspire a lot of questions that could be avoided. It is gross for an operator to go back and forth just to check for irregularities. It would also be such a pain if an operator had to continuously confirm the validity of a data.

As far as data processing is concerned, it is very noble to get the story straight at first and stick to a particular one.

Lexical errors

The specified format of a data collection should not contradict the data items. In cases where this is reality, confusion sets in.

There is a possibility that there is temptation to give information beyond what has been asked or outside range. A good data entry should rest in the assurance that it should never be heard that such temptations are given chances to thrive. Else, problems abound during data processing.

Lexica error is a typical case of drug misuse in medicine. As expected, there are grave consequences for lexical error just as there are for drug misuse.


It is not every time that information are repeated – this is because, whenever they are, problems abound. Duplication often occurs when an operator gives no complete attention to all that is contained in an information. When there are no records of the information that have been processed. Whatever causes duplication, it simply is the repetition of an information multiple representation of data in a format. Duplication can be a great error.

So a data cleansing expert must watch out specifically for an error like duplication for it can easily be avoided.


Abbreviations are good when all the people who are likely to come across them are familiar with the usage. It may take a level of familiarity to make this happen. And the process alone is both time and effort consuming. Some people are accustomed to writing abbreviations. Hence, the problem.

So, abbreviation makes your work as a service provider or data entry operator speak less to the decoder as much. By this, communication is stifled and as we have come to know, lack of communication can pass as not so relevant to this generation.

Using of initials can also count for abbreviation. All of these can contribute negatively to the effective processing of data.

Wrong reference

Here is another pitfall of data cleansing that is seldom talked about. Although it lacks popularity, wrong reference can also pose a great threat to data entry or data cleansing. If anything, wrong reference is the cause of data mismatch.

All of these are pitfalls. Some of which are more widely known than others. All of which need to be taken seriously for data cleansing services. There are things an operator must ensure never become too much a problem for solution to be possible. These things have been mentioned above.

Joydeep SinghaGrowth Expert
Founder at AskDataEntry – India’s leading online virtual administration services provider for businesses and individuals. He is a seasoned marketing professional who is an expert in digital marketing and entrepreneurship.
Published On: July 30th, 2020 / Categories: Data Cleansing, Outsourcing / Tags: /

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