Data Cleansing Best Practices & Strategy Plan [2018 Guide]

Data is one of the most critical assets in the 21st century. Unfortunately, ensuring that data is accurate and actionable is one of the biggest challenges for organizations today.

If our data is of low quality, then our decisions based on that data will be ineffective. That's why data hygiene and data cleansing are critical to ensure an acceptable level of data integrity.

In this guide, I'll discuss how to develop an effective data cleansing strategy as well as data cleansing best practices that you can implement right now.

What is Data Cleansing (Cleaning)?

Here's a concise data cleansing definition: data cleansing, or cleaning, is simply the process of identifying and fixing any issues with a data set.

The objective of data cleaning is to fix any data that is incorrect, inaccurate, incomplete, incorrectly formatted, duplicated, or even irrelevant to the objective of the data set.

This is typically accomplished by replacing, modifying, or even deleting any data that falls into one of these categories.

In the Information Age, we are being overwhelmed by data. In addition, data is driving critical decisions in our economy and our lives, and this trend will only increase.

Therefore, it’s crucial to have good data cleaning methods to ensure that the decisions being made in our organizations are the best possible.

So what are the best practices in data cleaning today? We'll discuss them below. But first, let's clear up a couple of misconceptions.

What is the difference between data screening and data cleaning? Data screening is focused on catching errors during data input while data cleaning is typically associated with fixing data after the data is captured. However, there's overlap in these terms.

What is data scrubbing? Data scrubbing and data cleaning are basically the same thing. However, practitioners in data have their own preferred uses of the terms. In addition, another term for data cleansing is data massaging. Data hygiene is also a common term associated with a data cleaning process.

Why is Data Cleaning Important?

What is data cleaning and why is it important? We answered the first question above. The reason data cleaning is important is to ensure that we achieve high data integrity.

But why is data integrity important?

Because the integrity of data is critical for ensuring that we have high quality data to make decisions upon.

Since our decisions are typically based on data sets, if the data is of poor quality, our decisions will be too. Thus data integrity is critical as it allows us to have high quality data, leading to better quality decisions.

What defines high quality data? The answer is a data set that is accurate, consistent, valid, complete, and uniform. These factors are pretty standard, but let’s quickly discuss what each one means.

1. Data Needs to Be Accurate

Is the data a true reflection of what is being measured? In other words, does the data match the trueness of the situation?

How do we know if the data is accurate? The easiest way to tell is if we can we check its correctness compared to another source.

Ensuring data accuracy is one of the biggest challenges in data cleaning. The reason is because to ensure accuracy, we need to compare the data to another source. If another source doesn't exist or that source is inaccurate, then the our data might also be inaccurate.

2. Data Needs to Be Consistent

Is the data consistent across multiple data sets? For example, is a customer’s phone number the same across multiple data sets that we manage? Can we easily authenticate and compare our data across all of our data sets? Do we do this on a regular basis?

3. Data Should Be Valid

Does the data meet particular rules or constraints that are defined? For example, can a data entry operator input a phone # in an address field? Another example would be if we can validate addresses through the USPS API when data is being captured, to see if they're correct.

4. Data Should Be Complete

Is the data complete or are there missing elements? Incompleteness is a factor that data cleaning cannot fix. You cannot add facts that are unknown. However, you can implement ways to retrieve that data from other sources if it is missing.

5. Data Should Be Uniform

What standard units were used when capturing the data? It’s important to ensure that all values are in the same units. For example, if height is being captured, are all units in inches, feet, cm, or meters? It’s critical that the data is uniform. If you do not know what units were used, it can be challenging to clean data after the fact.

In essence, data cleaning is critical to ensure that the data you are making decisions based on is of the highest quality.

The bottom line is that higher quality data leads to higher quality decisions! Can you afford to make bad decisions on low quality data?

Benefits of a Great Data Cleaning Process

Next, let’s discuss some of the benefits of good data cleaning techniques.

1. It greatly improves your decision making capabilities.

This one is a no brainer. In addition, it’s one of the biggest benefits of data cleaning.

Data that is cleaned and that has high quality can support better analytics and business intelligence. Consequently, this can ensure better decision making and execution towards objectives. This is one of the most significant benefits of a implementing a sophisticated data cleansing process.

2. It drives faster customer acquisition.

Businesses can significantly boost their customer acquisition efforts by ensuring they have high quality data.

This can be accomplished through an effective data cleansing strategy. For example, by cleaning data and ensuring it’s accurate, a business can be far more efficient at acquiring new customers and even re-targeting past customers. This is a guiding principle behind CRM, or Customer Relationship Management, software.

3. It saves valuable resources.

Removing duplicate and inaccurate data from databases can help business save valuable resources. These resources include both storage space and processing time. Duplicate and inaccurate data can significantly drain an organization’s resources, especially if the organization is highly data-centric. Cleaning and scrubbing data after it's captured can be very time consuming and expensive.

4. It boosts productivity.

Having clean data helps employees make the best use of their work hours. If you are using low quality data, employees can end up spending a significant amount of time cleaning data and re-analyzing it due to mistakes. In addition, employees can be making incorrect decisions because the data is of low quality. This can cause significant inefficiencies at best and catastrophic mistakes at worst.

In addition, the ability to make competent and timely decisions can significantly boost the morale of employees, allowing them to be more efficient and confident in their decisions. This leads to greater productivity overall.

5. It can increase revenue.

In business, effective processes are very important. Spending a lot of time cleaning data can be very expensive.

Businesses that work on improving the quality of their data through an effective data cleaning strategy can drastically improve their response rates to customers. Consequently, this leads to more productivity, happier customers, and much better decisions.

How to Implement a Data Cleansing Strategy Plan

We’ve discussed what data cleaning is as well as some of the potential benefits. Are you convinced yet that you need a solid data cleaning strategy plan?

Below, we'll walk you through the steps for developing a solid execution plan.

When you’re creating a data cleaning strategy plan, it’s important to look at the big picture as well as your unique situation. What are your goals and expectations? What are your current struggles? How will you execute the plan?

An effective strategy will depend on your unique situation. However, let's walk through the steps. The data cleansing strategy documentation below is a great starting point.

Data Cleansing Best Practices & Techniques

Let's discuss some data cleansing techniques and best practices. Overall, the steps below are a great way to develop your own data quality strategy. These steps also include data hygiene best practices​.

1. Implement a Data Quality Strategy Plan

So what are the best practices for data cleaning? The first step is to create a data cleaning plan and strategy. This can sound overwhelming at first. However, start at the highest level. Ask your key stakeholders the following questions and let the answers illuminate the path forward:

Questions to Ask:

  • What benefits could we see by using higher quality data?
  • Can we calculate the ROI of investing in data quality improvements?
  • What types of data do we capture on a regular basis?
  • What types of data do we base important business decisions on?
  • How are these data sets captured?
  • Who captures this data?
  • What standards for data capture do we currently use, if any?
  • Do we catch errors and issues during data capture?
  • How can we standardize the data that we capture so that it’s cleaner?
  • Where do most of the errors in our data occur?
  • How do we clean our data, overall?
  • What methods do we use to validate our data?
  • How do we append, or combine, our data from multiple sources?
  • Are there opportunities to append, or combine, our data sets in unique ways that would empower better decisions?
  • What automation do we currently use for data? What automation would greatly improve our data systems?
  • How do we test and monitor our data quality?
  • How do we assess the accuracy of our business decisions?
  • Who’s accountable for our data quality?

By asking these questions, you will start to see the current state of your processes. You will also start to see what can be improved. With these answers, you can put together an overall plan and strategy.

It’s important to also identify your goals and objectives before you move forward. Are your expectations realistic? Is it worth the cost? Of all the data cleaning best practices, this step is probably the most critical.

2. Standardize Data at the Point of Entry

It’s important to create uniform data standards at the point of data entry. In other words, create standards for how data is initially captured.

Screening data in this way can greatly improve its initial quality. In addition, it’s far easier to clean data that is already of decent quality vs. trying to clean data that is very low quality. Therefore, the highest ROI for data improvements can typically be found at the data entry point.

Implementing changes can be challenging for organizations that already have an embedded and highly active data entry process. However, effective communication and enforcing data standards can help achieve uniformity across the organization.

For example, standardizing contact data when it’s initially captured can be accomplished by identifying errors at their first occurrence. Software makes this much easier. When any data is entered into a system, ensure that the data meets the required standards.

Data Entry Standards Document

One of the best practices for data cleansing is to create a Data Entry Standards Document (DES) and share it across the organization. Moreover, update new employee training to incorporate these standards and re-train existing employees as needed. In addition, implement software or other checks to ensure compliance with the DES.

At the point of data entry, the objective should be to identify inconsistencies, inaccuracies, and duplicate records. You can alert the operator or even implement software that resolves these issues automatically.

Automation through scripting

A great approach is to develop a set of utility functions, tools, and/or scripts that do the hard work. There are some incredible options available.

For example, using regex functions to search and replace incorrect text. Another example is blanking out all values that don’t meet a certain requirement.

Scripts and tools like these should have target rules to clean and format data to meet the required standards. There are a lot of experts in this field that can do this for you or even help educate you on the options.

3. Validate the Accuracy of Data

Now that we have set standards for data that is being captured, the next step is to validate its accuracy. In essence, we need to validate the data to make sure that it meets the required standards. If it doesn’t, we need to alert the operator or even fix it on the spot.

One purpose of data validation is to assess the accuracy and consistency of the data being captured. Accuracy and consistency can only be measured by comparing the data to another accurate source. This source needs to be correct, otherwise, we have no way to know that that the new data is also accurate.

By implementing data validation techniques on the front end when data is being initially captured, we can greatly improve the overall quality of our data sets. However, this can be complicated and challenging depending on the situation.

In addition, for large, messy datasets, reaching 100% validation is next to impossible. Therefore, it’s important to have realistic goals. Moreover, you should consider a cost to benefit analysis when developing your goals for data validation.

Data validation can also take place after initial data is captured. This is a great strategy in situations that you cannot perform validation in real time. If you are dealing with a large data set, develop a script or approach that can validate a small data set at a time. This is much easier to scale up vs. trying to fix an entire data set at the same time. It can also allow batch processing.

Additionally, an effective validation strategy will include the ability to remove duplicates, identify errors and update obsolete records in data sets that are already captured.

Don't Be Afraid to Hire Experts

Investing in data solution providers might be a great fit for your needs. Instead of trying to figure everything out yourself, you can hire the expertise that you need. These expert providers can help guide you through the process of finding or developing effective data cleaning tools and software.

I would also be happy to chat with you about your situation and offer any advice that I can. You can reach out to me on LinkedIn or even schedule a quick phone call with me by clicking here.

4. Append Missing Data

After your data has been standardized and validated, you can append missing data. This simply means cross referencing multiple data sources and combining known data into a final data set that is far more useful and valuable to you.

This step is important in order to provide more complete information for business intelligence and analytics. In essence, it can put the different puzzle pieces together for your business.

5. Implement Automation

Once you’ve implemented data standards at the point of data entry, executed an effective data validation process, are appending data to increase the overall value and usability of your data sets, then it’s time to streamline the process even more. You can do this through automation.

Automation is one of the best ways to reduce human error. In addition, it can save a significant amount of time, saving you a lot of money. One example of automation would be automated database scrubbing. There are automation experts out that can guide you through the best way to do this based on your situation.

However, it’s important to remember that automation should never be the first step. It’s critical to have a proven process in place before you try to automate everything.

6. Train Your Folks

Additionally, one should train their workforce on the importance of clean data as well as the how the data processes work. By sharing this information, employees will be better informed and more enthusiastic about helping the processes succeed. In addition, they can even offer ideas on how to improve the system.

7. Monitor the Data Cleaning System.

Once automation has been achieved, it’s important to monitor the entire process. Identify some key metrics to assess the health and effectiveness of the system.

Also identify ways to sample test data randomly to ensure that it’s meeting your standards. Finally, you can also implement some test cases to see what decisions would be derived from various sample data sets to ensure that they are correct. Back testing is a great way to achieve this.

Data cleaning should be an endless loop. Consistent monitoring keeps this loop stabilized.

Implement periodic checks on your data cleaning process based on the situation. These can be weekly, monthly or even daily, depending on your needs and the availability of resources.

Finally, watch for changing situations in the process that require adjustments in processes or automation.

How to Measure the Success of a Data Cleaning System

Once we have implemented a new data cleaning system, how can we measure if it’s successful?

Here are some great ways to measure the success of a data cleaning system:

  • Does the system detect/identify and remove or even correct major errors and inconsistencies?
  • Does the system successfully use tools, scripts, and automation to reduce manual inspection of data?
  • Is the system improving the overall quality of data?
  • Are better decisions being made since the system was introduced?
  • Is the system saving time and money, while improving data quality?


In conclusion, data cleaning is vital to the success of any data centric business activities. In this guide, we discussed what data cleaning is, why it’s important, and how to create a successful data cleaning strategy plan and system. We also discussed the best practices in data cleansing systems.

Now it's your turn. What have been your biggest challenges with data cleaning? What other data scrubbing best practices do you use?

We’d love to hear your opinion in the comments below. If you would like to get this same content in a data cleansing strategy document download, please let me know.

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About the Author: Pete Thompson

Pete Thompson

Hi, I'm Pete Thompson, the founder of

I'm a data expert and would love to help you in any way that I can.

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