The data that was being used for marketing a generation ago, including the information like responses to direct mail campaign, point-of-sale transaction data, coupon redemption numbers, etc., was very limited compared to the data available today. Now, with data, marketing teams have experienced enormous innovation. But, just having and processing data doesn’t necessarily lead to better business growth and profitability.

Data is a very critical ingredient in today’s sales, marketing, customer success mix and making informed business decisions, but just having data, improvement in these functions aren’t guaranteed. It’s the insight that the marketing and sales professionals get from quality data that enables them to make informed decisions. The results of using quality data include better customer engagement; optimize sales and marketing performance, and better customer retention among many others.

There are always two major aspects of the improvement of data quality. The first one is data cleansing that is a one-off process for handling the errors within the database, ensuring retrospective anomalies are identified and removed. The second one is data maintenance that is a process of continuous improvements and periodic checks which describes the continuous correction and verification process.

Collecting data is not the goal; it’s just the means to a specified end. The actual opportunity is prospect and customer intimacy, how well we get to know them, and how we utilize this data to improve their satisfaction level and overall experience.

When you think about data, you can just compare it with the caring of your health. TO be more specific, data cleaning is a lot like brushing your teeth. Most people brush their teeth twice a day to stop germs taking hold. Otherwise, the sugar that we consume will gnaw away at the enamel and cause them to set in our teeth. And the longer one goes without brushing their teeth, the more

vulnerable our teeth get. In the same way, the database must be continuously cared for, cleaned, and maintained.

Data cleaning is tedious, and it’s even harder to maintain the accuracy and consistency of the database after the database has been cleaned. Although, if you follow the data cleaning best practices, you will definitely see the desired result for your company. As already explained maintenance of clean data is the most critical and challenging aspects of data cleansing. Even a small typing mistake can result in a myriad of problems and several hours of manually cleaning data that could have been avoided very easily. But, error in a database is inevitable that is why following a standard process is highly recommended. Data cleansing can help your company to easily identify the areas of bad data and needs your attention.

The data cleansing process can help you to identify duplicates and rectify other problems as well that are within your database for the initial planning stage to the final stage of data maintenance. And remember, that data cleansing is a continuous process and you should periodically implement data cleansing on your database using the steps (described under the heading “Data Cleaning Process” of this blog)   to get the most out of your data.

High-quality data is needed to make informed business decisions. However, most of the time, the quality of a database turns out to be low which are caused by the errors, inconsistencies, missing data among some other reasons.

Data inconsistency results because of several reasons including typo, manual wrong entry, missing information, and the presence of redundant data in different representations. And not correcting these data can result in major problems during the subsequent downstream of data processing that can further result in wrong business decisions that are very costly for any organization. So, it is very critical for companies to ensure that they have an effective data cleansing process in place.

Most common challenges of data cleansing process

Data cleansing also know as data scrubbing is the process of identifying and removing errors and inconsistencies from the database to improve and maintain the quality of data. And the need for data cleansing increases when several data sources are integrated. The process of making data consistent and accurate is filled with many complex problems, some of which are described below;

High volume data

Applications like data warehouses continuously receive a plethora of data from multiple data source and they carry a significant amount of bad data within them. In such a scenario, the task of the data cleansing process becomes formidable as well as significant at the same time.

Lexical errors

This error happens in a database because of name discrepancies among the specified format and the data items. For example, a specific database records attribute for name, sex, age, and height. So, when someone does not enter an intermediate value (like age or name) then this data for following attributes changes field.


Misspelling happens mostly because of typing error (typo). Misspellings can be easily identified and rectified for common words and grammatical mistakes. However, since a database contains a plethora of data that is unique, it is quite tough to identify spelling mistakes at input-level. And the spelling mistakes in a database like name and address are always hard to identify and rectify.

Misfielded value

Misfielded value happens when the input value is right as per the format but the input information doesn’t belong to that data field. For example, in the data field of the city, the information entered is Canada.

Domain format errors

A domain format error happens when the input information for a specific data field is correct but doesn’t comply with the standard domain format of that specific data field. For example, a specific “NAME” data field requires the first name and last name separated by a comma but the input information is entered without a comma. In this scenario, the information entered is correct but it doesn’t comply with the data field of domain format.

Missing value

Missing value happens when the omissions occur during the collection of data. They simply signify the unavailability if input information values during the process of data entry. Dummy value, as well as a null value, is included in missing input information values. For example, 000-0000 and 999-9999 in the phone number data field.


Irregularities deal with non-uniform use of input information values. For example, during the entry of salary information of employees, the salary is entered with different currencies. This type of information needs subjective interpretation and may usually result in incorrect results.


A duplication problem represents a scenario where the same information value is represented several times as a result of an error during the data entry process. Example, there can be two information records of the same individual with everything else the same with just a small difference in the name without the use of a middle name in one of the information value entered. So, no data is wrong in this case but the individual gets represented twice as a result of failure to check for duplicate information.


It happens when any real-world element is represented by two different information values in a specific data field. For example, a database consisting of personal information there are two information records for the same individual with two different dates of birth, but the other information values are the same.

Integrity Constraint Violations

It describes the information values which doesn’t meet the integrity information value constraints. And it happens when an input information value is outside the vicinity of allowed information values that represents a specific data attribute.

Cryptic values and abbreviations

Cryptic values and abbreviations occur as a result of its use in a data field. For example, the input information value entered for a college name just using its initials instead of its full name. This type of errors increases the chances of data duplication and decrease the sorting ability.

Wrong references

Errors that are related to the incorrect results that restrict data validation and as a result data mismatch occurs. Example, in the data field of the department, someone enters incorrect information value for the related department. This results in a mismatch during the subsequent process of data validation.

Embedded Values

This type of mistakes happens when several information values are entered within the same data field. It restricts the ability of data indexing and data sorting. For example, when the information values for name, title, and company are entered only within the name data field.

Violated attribute dependencies

This type of error occurs when the information value of a secondary attribute doesn’t match with its primary attribute. For example, when the information value for the listed city doesn’t lie within the mentioned country or when the entered information value of a zip-code doesn’t coincide with the mentioned city.

Why is data cleansing important?

Outdated, duplicate or inaccurate data doesn’t drive optimal decisions. The data about your customers and markets constantly decay. So, if you use data that was recorded during last year, you may not get a complete 360-degree view of your customers and markets. When the data is inaccurate, the leads become harder to track, nurture, and the insight may be incomplete or incorrect. The data which you use for your campaigns and making important business decisions must be up-to-date, complete, and accurate as much as possible, and should not have any duplicate information values. Meanwhile, good data enables you to make an informed business decision and get better results from your campaigns.

Data decay is inevitable. Example, the job changes, promotions, company mergers & acquisitions, keeps happening continuously. And the process of data cleansing helps companies to keep their data up-to-date and at the same time removes any risks that could have occurred from using bad data. And by ensuring their data is most accurate and updated as much as possible, companies are better positioned to improve their efficiency, maintain good customer relationships, and get useful data-driven insights.

Apart from these, under the new general data protection regulation (GDPR) which came into effect from May 2018, data cleansing and data management have become very important. Under the guidelines of GDPR, companies (if applicable) has to get explicit permission from their prospect and customers and then continuously work on removing any data that is not necessary where the elements like data breach notification and subject access request will have to make contact with the data subjects in a limited time period, so accurate contact data are very critical.

How is data cleansing implemented?

Data cleansing process is implemented on the data that is currently not being maintained and not on the data that is already being maintained. The data cleansing process tries to identify and remove or rectify data that is not correct. The core goal of the data cleansing process is to achieve accurate, complete, and consistent data. The process uses statistical analysis tools to read and audit the data based on a predefined list of constraints. And the data that doesn’t meet the list of constraints are placed in a separate workflow for exceptional data handling. Data cleansing result in quality data. And when you have quality data, you can easily process and analyze it that may give you insights that will enable your company to make informed business decisions. High-quality data is very critical to your business intelligence efforts, data analytics, and overall better operational efficiency.

A closer look at the systems handling data

Companies invest in several types of data systems. These types of data systems can be categorized into two main categories;

  • Data systems where data is created by the users.
  • Data systems that analyze existing but the data are created automatically like, through a customer journey funnel on a website or customer engagements with a campaign.

In the first category are platforms that act as data-production sites like customer relationship management (CRM), enterprise resource planning (ERP), and human capital management (HCM) platforms. CRM has a component of prospect and customer data. Sales representatives enter prospect and customer data into the CRM, which then generates useful reports to track their KPIs, identify trends, and forecasting.

In the second category are analytics tools like Google Analytics, or some other embedded systems, or business intelligence (BI) systems. These platforms observe, provide analysis, and gather insights using the data that is already available in these systems. These platforms have the ability to pull data and then cross-reference this data with different systems to present the results in a visual dashboard.

The data cleansing process

The data cleansing process can easily help you to target the areas where data is poor and requires attention. The important thing to remember here is that the data cleansing process is a continuous process like going in a circle. Make a small investment in the beginning and then continuously keep on investing in the process to improve data quality.


Identify the data that your company needs to run successful campaigns and make an informed decision, and also the data that your sales, marketing, and customer success teams needs in order to be highly efficient. Then identify the set of data which is very important for making your marketing efforts most effective. When you look at your data, you should focus on priority data and start small. The data fields you want to identify should be unique to your organization and should be the information you are specifically looking for and may include data fields like Title, E-mail address, Phone number, Company, revenue, industry, etc. And then create and add specific data validation criteria to standardize and cleanse your existing data and at the same time automate this process for the future. Example, ensure that the zip codes and state codes match with each other, ensure that the address format is the same for your entire database, etc.

Analyze to Cleanse

Prioritize the data that your organization needs. In order to identify the data that you lack and the data that need and the data that is not relevant to your organization and needs to be deleted, it is very critical to go through the data you already have. And you will also need to find a set of resources to manage and manually cleanse exceptions to your criteria. The volume of manual intervention is directly related to the volume of acceptable data quality levels you have. Finally, when you have created a list of criteria or rules, it will be very simple to actually start the data cleansing process.

Implement Automation

Start to cleanse your data; you should start to standardize and cleanse the flow of new data when it enters the system by developing workflows or scripts. These can be processed in real-time or in batches like daily, weekly, monthly, etc, based on the total volume of data you are working with.

Append Missing Data

This step is very critical especially for the type of data that cannot be automatically rectified. For example, E-mail addresses, Phone numbers, Company size, Industry, etc. It is very critical to find the right way to get a hold of the missing data, whether it is from 3rd-party sources, reaching out to the contacts or the good old-fashioned way.


Include periodic reviews in the process to identify the critical issues before they result in a major problem. Monitor your database as a whole, as well as individual units. And at the same time keep a close eye on your bounce rates and response rates. It is very critical to know that where your contacts are currently working; so when any of your contacts don’t respond to any of your campaigns for more than 3 months, it may be a good idea dig a little deeper to ensure that your contacts are working at their mentioned companies. The goal of this cycle is to bring the whole process in a full circle.

Review your plan from the beginning and then reevaluate. Do the criteria or rule you implemented are still relevant to your overall business strategy? Can your priorities be modified or changed? Pin-pointing these critical changes will enable you to effectively work throughout the cycle. Implement

changes that help your process to be more effective and at the same time carry-out periodic reviews to ensure that your data cleansing process is working seamlessly.

Common challenges encountered during data cleansing process

Data cleansing is very important for every organization that uses data. And it is very critical that the correct data is used, cleaned, and analyzed makes informed business decisions and enable their teams like sales, marketing, customer success, etc, to be effective. But the data cleansing process is bound to encounter problems and one has to identify a way to resolve those problems. Following are the most common problem encountered by data cleansing process;

Error in correction and loss of information

The most critical problem encountered by the data cleansing process still remains the correction of data values to remove the invalid and duplicate entries. And in many scenarios, the available data on such anomalies is insufficient and limited to determine the required corrections or transformations, leaving the removal of those entries as a primary solution. However, the removal of data results in a loss of information that can be quite costly when there’s a large volume of removed data.

Data cleansing in virtually integrated environments

In virtually integrated sources, the data cleansing has to be carried out each time the data is accessed that significantly improves the response time and reduces efficiency.

Data-cleansing framework

In many scenarios, it is impossible to derive a full data cleansing graph to instruct the process in advance. And this makes the data cleansing process as an iterative process involving significant exploration and interaction that may need a framework in the type of a collection of various methods for error identification and removal in addition to data auditing. And it can be easily integrated with other data processing stages like maintenance and integration.

Maintenance of cleansed data                                      

Data cleansing is a time-consuming and costly process. So after implementing the data cleansing process and successfully achieving a database that is free of errors, you should avoid the re-cleaning of your database in its entirety when some of the values in your database changes. Instead, the data cleansing process should only be repeated on the data values that have changed. This means that the data cleansing lineage should be kept that requires effective data collection and management strategies.


Any successful organization without good data is moving in the direction of perilous future. And being deprived of your most valuable assets- the data that you need to make informed business decisions; you will have to navigate without truly knowing about your prospects and customers. So, data cleansing and maintenance cannot be ignored.

Categories: Data

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