6 Data Cleansing Best Practices for a Healthier Database

Posted by Thanh Nguyen on

Data cleansing is one of the most important steps in the data preparation process. As companies are increasingly dependent on data to make crucial decisions, inferior data can leads to inefficiency, missed opportunities, or even financial losses. Thus, ensuring a “clean” database is one of the biggest challenge in today's organisations.

Learn more: Make compliance an opportunity not a burden

Data Cleansing Best Practices

What is data cleansing? And why do you need it?

What is data cleansing?

Data cleansing, also known as data scrubbing or data cleaning, is the first step of data preparation. Data cleansing can be simply defined as the act of finding out and correcting or removing incorrect, incomplete, inaccurate, or irrelevant data in the data set. Data cleansing can be software-assisted or done manually.

What are the different types of data problems?

There are a variety of problems that can occur to data when businesses retrieve data from the internet or other sources, combine data from different data sets, receive data from customers or other departments, etc. Some common issues are:

  • Duplicate data: When there are 2 or more identical records.
  • Conflicting data: When there is conflicting information in the same record. For example, the same customer who have different phone numbers in different records.
  • Incomplete data: Data that have missing attributes.
  • Invalid data: Data that does not conform to standards.

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The danger of “bad data”

Nowadays, data has become one of the most important assets to most businesses around the globe. As more people start to rely on their data to make impactful decisions, poor data can damage a firm’s bottom line. In fact, according to Forbes, poor quality data is taking as much as 12% of revenue from businesses, and in the United States alone, dirty data costs the economy an approximate sum of $3.1 trillion a year.

Poor quality data does not just do damage to companies financially, it is also a large contributor to time-inefficiency, as data analysts spend over half of their time managing and cleansing data. This extra time will add up and slow down the company as a whole.

Moreover, bad data can affect many other functions of the firms as well. For example, the lack of data on customers’ preferences can lead to ineffective marketing campaigns, or unreliable customers’ information can create problems for Sales.

Read more: Rise of Chief Data Officer (CDO) to Solve the Data Issues

Benefits of data cleansing

Data cleansing can have a variety of benefits, such as:

1. More accurate insights and reliable predictions

With better data to be processed, data information will be more reliable. This will provide the company’s with insights into multiple fields and helps to make more accurate predictions.

2. Increase productivity and effectiveness

Dirty data can create bottlenecks in various functions as well as issues and work-to-be-done. By eliminating this bottleneck, employees can do their jobs quicker and more effectively.

3. Decrease the overall cost and increase revenue

Research shows that dirty data can contribute up to 12% of losses in a company’s revenue. If data cleansing is done well, this loss can be minimised, and the business can enjoy higher total revenue.

4. Increase customers’ satisfaction

More accurate data can help firms understand their customers better, which, in turn, will lead to better overall customer experiences.

Read more: Your Guide to Data Cleansing Tools

Data cleansing best practices

There are various techniques and practices to keep a nice and clean database. Here are some best practices for cleansing your data.

1. Develop a data quality strategy

  • Set expectations for your data.
  • Create data quality key performance indicators (KPIs) - What are they, and how will you meet them? How will you track the health of your data? How will you maintain data hygiene on an ongoing basis?
  • Find out where most data quality errors occur.
  • Identify incorrect data.
  • Understand the root cause of the data problem.
  • Develop a plan for ensuring the health of your data.

Read more: 7 Factors to Consider When Building a Healthy Data Culture

2. Correct data at the point of entry

To keep a clean database, it is important to have clean and standardised data to ensure all important attributes are free of issues and mistakes at the point of entry. This can help save time and effort for your team before going any further.

A standard operating procedure for entering data should be created and enforced by everyone in the team. This will ensure that only high-quality data can be entered into the system.

3. Validate the accuracy of your data

In this step, we need to validate the data to make sure it meets all of the requirements, which can be done manually with a small data set. However, with larger and more complex data sets, the manual method is extremely time-consuming, labour intensive, and ineffective as people are prone to make mistakes. Therefore, data quality control tools are made to help with this issue.

Check out our post here about data cleansing tools to learn more about tools that can help you solve this issue.

4. Manage duplicates

Duplicates are harmful and are a waste of time and effort. They interfere with various functions of the company, such as Marketing, Sales, and Customers Support, slow down the firm operating process, and damage company-customer relationships.

Read more: 5 Use Cases of Data Lakes that You Probably Did Not Know

Companies must avoid them as best as they can. And after removing all duplicate data at the entrance, it is important to consider the following:

  • Standardising: Converting data to one single format to process and analyse.
  • Normalising: Ensuring that all data is recorded consistently. 
  • Merging: When data is scattered across multiple datasets, merging is the act of combining relevant parts of those datasets to create a new file. 
  • Aggregating: Sorting data and expressing it in a summary form.
  • Filtering: Narrowing down a dataset to only include the information that users want.
  • Scaling: Transforming data so that it fits within a specific scale such as 0-100 or 0-1.
  • Removing: Removing duplicate and outlier data points to prevent a bad fit in linear regression.

5. Append missing data

Append is a process of filling in missing information in the required field of the records, such as phone number, email address, last and first name, home address, etc. But finding the missing information can be tricky. To do this step effectively, it is recommended that firms should use a reliable third-party data source to help fill in the gaps.

6. Promote the use of clean data across the organisation

After everything is done, you need to communicate with everybody across the organisation about the importance of clean data. Ensure that employees, regardless of their functions, understand and maintain the practice of clean data.

At TRG, we solve business problems, take a consultative approach to every client engagement, and find actionable solutions that will help your organisation achieve the best business outcomes. Talk to us and explore the various Digital Advisory services we have in store for you today!

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Topics: Business Intelligence, Analytics

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 Rick Yvanovich
 /Founder & CEO/

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