Guide to Data Cleansing Tools and Why You Need One

Data cleansing is one of the most critical steps in the data preparation process. As firms are increasingly dependent on information systems to make crucial decisions, poor quality data leads to inefficiency, missed opportunities, and financial losses. Thus, keeping a “neat and tidy” database is more important now than ever.

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

Data cleaning can be a very labour-intensive job, especially when it is done manually. According to research, data analysts are spending around 60% of their time cleansing data. Nevertheless, a large number of firms still use a human-centric data cleansing system. There are a few reasons for cleaning data manually, such as having only a small database or data sets that are too inconsistent and difficult to be managed automatically. Whatever the reasons might be, it is important to acknowledge that humans are prone to making errors, and having a human-centric data scrubbing practice can result in a highly faulty system.

Fortunately, there are data cleansing tools to aid our needs. Data cleansing tools are computer software created for one single purpose, that is to help organisations better manage their data. Using computer-based software can remove a large proportion of human-element in the data scrubbing process, which makes the process more accurate, effective, and efficient. Moreover, these programs can come in very handy when there is a large amount of data that need to be processed or when time is restricted.

Data cleansing tools can automatically make most of the changes that are deemed necessary, such as fixing typos. These types of software are fast, accurate, and interactive, which can be a game-changer for companies.

In today’s dynamic business environment, businesses rely heavily on timely insights to make key executive decisions. And as firms are more dependent on their data, “dirty” data has become a huge threat to companies’ bottom line.

Experian researchers found that U.S companies believe that, on average, 32% of their data is not trustworthy. With one-third of the data is unreliable, serious damage can be done to companies’ income. In fact, According to Forbes, poor quality data is taking as much as 12% of revenue from businesses. In the United States alone, dirty data costs the economy an approximate sum of $3.1 trillion a year.

Poor quality data not only damage companies financially, but it is also a large contributor to time-inefficiency. This extra time will add up and slow down the company as a whole.

Moreover, a survey conducted by Harvard Business Review points out that around 30% of executives are not confident using only their internal data for critical decisions. With such low confidence in the data sources, changes are crucial for companies.

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

1. More accurate insight and reliable predictions

With better data to be processed, the outcome will undoubtedly be better. This will directly impact the company’s insights into multiple fields and helps to make more accurate educational predictions.

2. Increase productivity and effectiveness

As mentioned above, dirty data can cause a bottleneck in a firm’s business process by creating extra time and work-to-be-done. By eliminating this bottleneck, employees can do their jobs quicker and more effectively.

3. Decrease overall cost and increase revenue

Researches show 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 a higher total income.

4. Increase customers’ satisfaction

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

There are various ways and techniques that can be implemented to clean data. Here is an example of how to clean your data. Please take note that different techniques might work differently and have different outcomes. Organisational structure, data types, and workflows should be considered thoroughly to make sure the optimal solution is chosen.

Typical technique and steps to cleanse data can include:

Step 1. Drop irrelevant data

Irrelevant data that has no value to your business should be filtered out and deleted.

Step 2. Terminate duplicate data

Duplicate data can create confusion and error in your data set. You can solve this problem by deleting or merging duplicate data.

Step 3. Determine and fix structural errors

Inconsistency and typos can cause an issue for your categorical data set. Cleansing these errors will improve the quality of your data.

Step 3. Manage outliers

Outliers can be errors of valid data and are very important for your data set and data points. Make sure that you take care of them carefully.

Step 4. Drop, impute, or flag missing data

Missing data can be imputed by interpreting known relevant data. However, in some cases, it is better to drop these missing data than imputing them. In the case of missing essential data or value, flagging is a way to highlight that critical data is missing.

Step 6. Standardise data

Standardise each value to a uniform format.

Step 7. Validate the data

The last step is to validate your cleansed data to make sure it is ready for migration or further processes.

There are numerous data cleansing tools out there, each of them offers certain benefits and functions. It is wise to consider your company’s workflow, framework, and the types of data that need to be cleansed to choose the right data cleansing tools.

Here is a list of some popular data cleansing tools and their keys benefits:

There are numerous data cleansing tools out there, each offers certain benefits and functions. It is wise to consider your company’s workflows, framework, and the type of data that needs to be cleansed to choose the right data cleansing tools.

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