Data Cleansing Guide: What is Data Cleansing and Why is it Important?

Data Cleansing Guide: What is Data Cleansing and Why is it Important?

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 organisations of any size today.

The fundamental problem with data quality is fairly straightforward. If our data is of low quality, then the decisions organisations make, based on that data, will be ineffective. That is why data hygiene and data cleansing are critical to ensure an acceptable level of data integrity.

In this two-part guide, we will 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)?

First, we will cover what we mean by data cleansing. 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. Data Clarity have a number of tools that can help automate this process – you can read more here.

In the ‘Information Age’, we are being overwhelmed by data. IBM estimates that the amount of data organisations collect will double every year, and this challenge is only growing.

Data is driving critical decisions in our economy and our lives, and this trend is only increasing. It is therefore crucial to ensure good data cleaning methods and guarantee that the decisions being made in your organisation are the best possible.

Why is Data Cleaning Important?

The reason data cleaning is important is to ensure that we achieve high data integrity. Data integrity is vital because it is the only way of 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 reflect the truth 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 data might also be inaccurate.

  1. 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?

  1. 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 number 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.

  1. 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.

  1. 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.

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?

Data Analytics Sophistication Value Chart

5 Benefits of a Great Data Cleaning Process

  1. It greatly improves your decision-making capabilities.

This one is a no brainer and we have already discussed it in this article. It is 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.

  1. 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 is accurate, a business can be far more efficient at acquiring new customers and even re-targeting past customers. This is a guiding principle behind Customer Relationship Management (CRM) software and analytics platforms.

  1. 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 organisation’s resources, especially if the organisation is highly data centric. Cleaning and scrubbing data after it is captured can be very time consuming and expensive without the proper tools and processes to do it efficiently.

  1. 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-analysing 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.

  1. 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. In Part Two of this guide, we will discuss how you can implement your data strategy to maximise your return on investment.

As you start to implement a data cleaning strategy, you may find that you require expert guidance to ensure successful implementation. Our data experts would love to help you and your business with your digital transformation efforts. Contact us today to discover the true power of your data.

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