5 Best Data Cleansing Tools: How to Choose?

Data is ingested at rapid speeds within your company. Collecting, storing, and utilising the data for business insights and use cases takes time and costs money. So, there’s no doubt about the immense value of investing in data cleansing tools to ensure that the data you’re storing and using is accurate and relevant.

We’re going to look at how data cleaning techniques can be automated so you can expedite your data cleansing steps. We’ll share five of the best data cleansing tools to help execute your data cleansing strategy seamlessly.

Coming Up

1. What is Data Cleansing?

2. Why is Data Cleansing Important?

3. How Does Data Cleansing Work?

4. What is Data Cleansing vs Data Validation?

5. What are the Best Data Cleansing Tool Features?

6. What are the Top Data Cleansing Tools?

7. How to Compare Data Cleansing Tools?

8. The Bottom Line

What is Data Cleansing?

Data cleansing is a critical process that identifies and rectifies inaccurate or incomplete data. Data cleansing, also called data cleaning and data scrubbing, makes sure that the data being utilised is reliable and up-to-date.

This is especially necessary when combining multiple data sources. Data may be mislabeled, unformatted, or duplicated. Through the data cleansing process, you’ll remove the duplicates and fill in the missing holes to complete your datasets.

Some common issues with data you may have experienced or are currently experiencing includes misplaced entries, missing values, and even typographic errors (hence why data cleansing tools are so sought after).

By using data cleansing tools like SolveXia, you can skip the risk of having errors from manual data entries plus save lots of time.

Why is Data Cleansing Important?

Data cleansing isn’t a recommendation, it’s a must! If you are sticking with outdated, incorrect, or irrelevant data to make decisions, then no matter how good your analytical processes are, your decisions will be skewed because they aren’t based on the facts.

With data cleansing, you’re able to:

• Make better decisions based on solid data quality
• Maximise efficiency and productivity
• Reap a competitive edge as you can better meet customers’ needs and anticipate them

Poor data costs businesses money. In fact, a study found that “dirty data” can cost a company up to 12% of its overall revenue. Investing in good data cleansing tools can save you that money and more.

How Does Data Cleansing Work?

Although data cleansing may look slightly different for each organisation, it tends to go through the same six main steps. These data cleaning steps include:

1. Removing Duplicates

Duplicates, also called dupes, often occur when you combine data from multiple sources or if a customer submits their information multiple times on company forms. Repeat data can slow processing power and cost more storage.

2. Remove Irrelevant Data

Removing irrelevant data doesn’t mean it’s gone forever. It just removes it from the process at hand if it won’t answer the relevant question or need.

3. Locate Outliers

Data that is vastly different from the majority of the data can create skewed results. Outliers may be included or excluded depending on what statistical method is used.

4. Fix Incomplete Data

Data with missing values can provide incomplete results. Figure out if any missing data fields are needed for the task or process.

5. Correct Structural Errors

Structural errors refer to capitalization, typography, and other inconsistencies that could interfere with analysis.

6. Validation

Validation is a check to see that all data is consistent, formatted the same, and relevant. This happens at the time of data entry.

What is Data Cleansing vs Data Validation?

Although the two terms, data cleansing and data validation, get used interchangeably sometimes, they actually are two different processes. Here’s what you need to know so you don’t confuse the two:

• Data cleaning (or cleansing): As explained above, data cleansing is all the steps it takes to make sure that data is free of errors, inconsistencies, and unstructured records.
  
  
• Data validation: At the time of data entry, data validation refers to the set to make sure formatting is in order and all data is updated. Since it happens at the time of data entry, it removes the need for data cleansing thereafter.

It’s easier to think of data validation as the first step to manage data. If data has already existed or is being connected between sources, data cleansing is necessary. That’s where data cleansing tools come in.

What are the Best Data Cleansing Tool Features?

Speaking of data cleansing tools, let’s touch on the features that are worth exploring and ensuring you have in the tool of your choice.

Consider this your data cleaning checklist when searching for a tool. It should have the abilities to manage:

1. Data elimination: The software is able to map the data against the profile you create in order to remove duplicates, incorrect information, and old data.

2. Data harmonisation: Like standardisation, data harmonisation pulls together data from disparate sources and formats them the same way. This makes it easier to analyse and review data, whether performed manually or automatically.

3. Data enhancement: High-powered data cleansing tools will offer a data enhancement option, which connects information between tools. As the software scans the data, it will pull any relevant information to make data more complete.

4. Data profiling: Data profiling goes through your data to locate patterns, identify missing values, and find data characteristics worth noting. This way, when the software works with the data, it can notify you of exemptions that are out of character from the data profile.

What are the Top Data Cleansing Tools?

Now that you have a good idea of what data cleansing tools are capable of, let’s compare and contrast the following top 5 data cleansing tools available to businesses today.

1. SolveXia

SolveXia is a low-code automation and data management software designed to help businesses achieve operational excellence and maximise their performance.

Along with automating hundreds of business processes (such as: account reconciliation, rebate management, expense management, regulatory reporting, APRA reporting, and more) to save your team time, money, and mistakes.

SolveXia can connect and collect data from disparate sources using its integration tools. Once data is moved into its centralised and secure repository, it can be automatically cleansed for immediate use.

With SolveXia, you’ll also be able to perform advanced analytics and gain deep insights so that you can rest assured every business decision made thereafter is based on relevant, complete, accurate, and timely data.

2. OpenRefine

OpenRefine was previously called Google Refine. It is an open-source tool, which makes it free to use. OpenRefine allows you to transform data that comes in different formats and structure it. Although it does visually look a lot like Excel (a spreadsheet), it serves as a relational database. Given this, data analysts can use the open-source database to dive deeply into analytics.

3. RingLead

RingLead is a data orchestration platform made for CRM and marketing automation. As such, it isn’t considered a data cleansing tool by itself. It performs data cleansing, as well as additional data processing functions like data enrichment and discovery, to name a few.

4. Talend

Talend is a data cleansing tool for data evaluation, formatting, and cleansing. Talend can integrate with pre-existing tools. It can be deployed across cloud and hybrid environments. The platform automatically cleans data in real-time and ensures data trust with its Talend Trust Score.

5. Oracle Enterprise Data Quality

Oracle Enterprise Data Quality is a data cleansing tool that also integrates with business applications. Features include: real-time and batch matching, standardisation, and verification.

Oracle’s EDQ suite is aimed to provide organisations with “fit-for-purpose” data. It provides data profiling, audits, and dashboards.

How to Compare Data Cleansing Tools?

On your journey to find the best data cleansing tools, keep in mind what to consider when you’re exploring.

Here are things to keep in mind:

• Compatibility: How does the data cleansing tool integrate with your existing systems and platforms?
  
  
• Security: Since you’re dealing with data, security is of utmost importance. However, systems will have to cross-reference one another, so you want to make sure that sensitive data is still protected.

• ‍Deployment: Decide whether you want cloud-based products or on-premise products.

The Bottom Line

When you begin to use a data cleansing tool, you’ll reap the immediate upsides of doing so. You can trust that your data is relevant, up-to-date, and ready-to-use. You can also leverage all your data from disparate tools to paint a full picture of your customers, business, and finances.

As such, the insights and analytics you gain access to can be fully trusted and invaluable. Data cleansing tools make it possible to streamline processes and reduce errors.

To learn more about how a tool like SolveXia works and the additional solutions it can provide for your business, feel free to request a demo.