Data Cleaning Techniques & Best Practices

“Data, data, data” for business insights is like the “location, location, location” of real estate. When it comes down to it, the data flowing into your business can be the greatest asset and value to your decision-making processes, but the key is to understand how to use it. 

Data cleaning techniques are often the first step to be able to transform raw data into insights. While it can be a cumbersome process when handled manually, there are data cleaning tools that can automate the data cleaning steps. 

Let’s take a look at what data cleaning entails, as well as which data cleaning tools are worth exploring. 

Coming Up

What is Data Cleaning?

Data cleaning, also known as data cleansing and scrubbing, is the process of organizing and revising information into a dataset so that it can be used for analysis. The goal of data cleaning is to spot and remedy errors, duplicates, and inconsistencies. 

When data comes into your business from multiple sources, there is a risk of overlapping information. However, if you apply duplicate records and redundancies into an algorithm, you’ll end up with screwed results. So, data cleaning helps to protect the outcome and ensure accurate information is gleaned from raw data. 

What is Data Cleaning vs Data Transformation?

While data cleaning and data transformation are both needed to conduct analysis, they don’t mean the same thing. 

• Data cleaning: takes care of any data that doesn’t belong in your dataset. As stated, you’ll be able to remove duplicate entries, remedy any incomplete data, and remove inconsistencies. 

• Data transformation: is concerned with the data’s structure and formatting. When you’re getting data from various sources, it will likely be formatted based on where it comes from, meaning that it won’t be standardized or follow the same structure automatically. But, it’s of paramount importance that you can format it all the same in order to apply it for analysis. 

Typically, you’ll perform both of these actions, starting with data cleaning and then moving into transformation. 

How to Clean Data?

There are different data cleaning techniques that you can employ, depending on the data you store and what you are trying to accomplish. 

That being said, these are the steps that professionals are likely to follow when data cleansing: 

1. Remove Duplicate Entries

As mentioned, businesses collect data from many sources. This often leads to having the same records more than once that will show up in your systems or spreadsheets. Or, if you’re combining data from different departments, each department may focus on its own concerns, but the data can still show up as duplicate entries. 

The first step is to remove duplicate entries. At the same time, it’s helpful to delete irrelevant data, namely records that have nothing to do with your specific concern at the time. 

By doing so, you will be able to slim out your dataset to exactly what is necessary to answer whatever question you may have. 

2. Amend Structural Errors

Look for records that are inconsistently categorized, such as mismatching capitalization rules or naming conventions. Adjust as necessary so everything matches up.

3. Delete Outliers

Outliers are records that are far off from the bulk of the rest of the data. Sometimes, they can be indicative of a mistake or false entry. If this is the case, it’s best to remove the outlier so it doesn’t affect your results. 

4. Fill in Missing Data

Most algorithms won’t work properly if data is missing. For data that is incomplete, try to fill in the missing values. If you can’t fill them in correctly, you may have to remove them from the dataset. 

5. Validate Data

The final step is making sure that the data is credible and valid. Some questions to ask include: Does the data make sense? Does the data follow the rules according to its field? Is it possible to notice trends in the data? 

While there’s a lot to get done when it comes to data cleaning, it doesn’t have to be a complex and manual process. Instead, automation solutions can save you hours (and even days), freeing up your team’s time to focus on analytics and insights. 

Additionally, automation software makes it easy to connect all your existing systems and technologies, delivering a centralized repository for data accessibility and use. 

What are the Benefits of Data Cleaning?

If you’re looking to gain trustworthy insights from your raw data, data cleaning is a nonnegotiable process. By doing so, your business benefits from:

1. Better Data Quality

Data cleaning promotes reliable data, so by conducting data cleaning, you gain better data quality. 

2. Enhanced Compliance

Privacy and data security is regulated across sectors. With quality data control and cleansing practices, you can help to ensure compliance. 

3. Smarter Decision-Making

Most importantly, having data that’s accurate and ready to use will make it possible to identify trends, optimize processes, and make informed decisions with agility. 

What Errors Does Data Cleaning Fix?

Data cleaning fixes common errors that your data can suffer from, such as:

1. Inconsistencies

Inconsistencies occur when data shows up in different formats, such as with different terminologies, values, or units. 

2. Inaccuracies

Data with purely incorrect values, including wrong numbers or syntax errors, will cause erroneous conclusions. 

3. Duplications

When the same data shows up more than once, you’re dealing with a duplication. These redundancies affect the outcome, so they have to be addressed. 

4. Incompleteness

Incomplete data happens when there are blank fields or null values. 

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Data cleaning takes care of all of these potential problems. Rather than having to rely on a person to process large volumes of data from various sources, you can utilize automation software to assist. 

Finance automation software like SolveXia can reduce tedious work to clean and transform your raw data for analysis. Plus, you get to reduce errors by 90% or more! 

What are the Characteristics of Quality Data?

How can you decipher what makes data “good” or “bad”? When assessing data quality, there are five key characteristics to keep an eye out for, namely: 

1. Validity: For data to be valid, it should fall into the acceptable range of values and be in the expected format. 

2. Accuracy: Accurate data represents the true value. 

3. Consistency: Consistent data follows the same format across the dataset, including units and terminology in use. 

4. Uniformity: Uniform data appears in a standard format so it’s easy to compare and analyze. 

5. Completeness: Complete data consists of all the information, without any missing or null values. 

What are Data Cleaning Best Practices?

Given the array of data cleaning techniques, there are some best practices to remember so you can make sure your data is in top-notch form for analysis: 

1. Know the Objective

Set an objective for your data cleaning process. Your analyst or team should be in the know about what they are trying to accomplish so that they can find errors and be aware of what to look for. 

2. Define a Process

The process of data cleaning should be repeatable and consistent. In order to make this happen, be sure to clearly develop a plan and process with rules, criteria and guidelines. This documentation guides your team as to what action to take when they notice a discrepancy. 

3. Document Each Step

Maintain reports of what has been done to the data, especially for future reference to look back on if needed. 

4. Back Up Data

When dealing with data, one of the most critical steps you can take is to back it up. If there’s any risk or issue to your data, you can always restore and recover the data you need. 

5. Utilize Automation

Data cleaning tools and automation software can streamline the entire process of data cleaning, so you don’t have to worry about it. 

Rather than relying on key personnel that know how to program and code, anyone on your team can leverage low-code automation software to take care of business. Your business processes are streamlined and accurate, resulting in increased accuracy, scaling, and enhanced compliance. 

What are the Best Data Cleaning Tools?

Looking for the best data cleaning tools? Here are five of our favorite options: 

1. SolveXia

SolveXia is a low-code financial automation platform that can collect, cleanse, and analyze your data. 

At the same time, SolveXis automates key finance functions, including reconciliation, expense management, rebate reporting, regulatory reporting, and more. 

With SolveXia, you can remove key person dependencies, prevent bottlenecks, complete processes up to 85x faster with 90% less errors, and leverage your data to improve your business. 

2. OpenRefine

OpenRefine is an open-source tool for users to clean, transform, and extend data using web services. It was previously known as Google Refine. 

3. WinPure

If you’re seeking a cost-effective and dedicated data cleaning solution solely, WinPure can handle large datasets to remove duplicates and standardize data. 

4. RingLead

RingLead is a data orchestration platform that also provides an end-to-end CRM with marketing automation. Its data cleaning features can remove duplicate data, link leads, and execute normalization. 

5. Melissa Clean Suite

Melissa Clean Suite offers a data cleaning application for CRM and ERP platforms. It can be used for contact autocompletion, data verification, data enrichment, data appending, and deduplication. 

How to Choose a Data Cleaning Tool?

As with any new technology you wish to implement, it requires adequate research and consideration before selecting your tool of choice. 

When it comes to data cleaning tools, look out for:

• Advanced quality check functions
• Ease of use 
• List of features 
• Cloud-based accessibility 
• Integration capabilities 
• Visualization or coding required 
• Level of support 

Closing Thoughts

There’s no denying that data cleaning techniques are required for data analysis. Rather than having to manually manage your influx of data, you can save time, reduce errors, and remove key person dependencies by relying on data cleaning tools and automation software. 

Want to give it a try? Request a demo from a solution like SolveXia to see how you can automate your key finance functions, streamline data cleaning, and achieve more.