What is Data Cleaning and Why Does it Matter?

Today’s world is run by data and there are no signs of it slowing down. If you run any kind of app or business process, data is your bread and butter, but not just any data. For good business outcomes, you need superb data quality and this is where data cleaning comes in.

Data cleaning, otherwise known as data scrubbing or data cleansing, is the first step you need to take to become a data-driven business. Today, we will show you what data cleaning is, why it matters, and how you can do it too.

What is data cleaning?

Data cleaning is properly structuring your available data so that it can be properly analyzed or visualized later on. Data cleaning involves a bunch of different processes, depending on your specific dataset. 

It could involve removing duplicates and outliers, filling in missing data, fixing up corrupted data files, cleaning up general device clutter, and more.

There are a lot of tools out there for cleaning data, but using tools like WinPure for data cleansing has helped lots of different kinds of businesses.

Data cleaning is necessary if you need accurate data analysis, especially so if your data comes from a variety of sources.

Why is data cleaning important?

Let’s imagine a scenario where you need to cook a beef stew. The recipe says to chop up your onions and dice your meat, but you decide to put in entire onions and big chunks of beef in your pot. The result? It may resemble beef stew but you’ll never win any chef awards.

The same goes for data cleaning. Think of an entire onion as your raw data - you need to chop it up for it to make sense in the final product.

With data cleaning, you’re making sure that each of your data pieces is formatted correctly and in the same way. 

This entails the same format, the same data fields, no errors or typos, no duplicate data points, no irrelevant data entries, and more.

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Data cleaning vs. data transformation

Data cleaning and transformation may seem similar but they’re two completely different terms. With data cleaning, you have the same data format as a starting point. For example, Facebook ad data - but some of it has missing values, so it needs to be cleaned.

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With data transformation, you’re starting with entirely different data formats. You need to transform the data from one format to another for an app or process to be able to use. 

Think of converting a Word document to a PDF using a PDF maker - that’s a very basic example of data transformation. Otherwise known as data wrangling, transformation is necessary if you work with a variety of data formats.

How to clean your data

The data cleaning process can seem very time-consuming, but we’re going to show you some basic step-by-step instructions and shortcuts that you can use to get started easily.

Getting familiar with Excel

Microsoft Excel is a staple for many data scientists because despite being decades old, it can do marvels for data cleaning in the most common spreadsheets.

You can use it to automatically format rows and columns, deduplicate values, replace text and numbers, and much more. Most tools you’re going to end up using put out files that Excel can read, too.

If you’re not big on Excel, AI models such as ChatGPT can help you out with this entire process without crunching numbers and finding complex formulas.

By the way, we also have an Excel data connector that you can use to pull data from Excel into Luzmo and create dashboards.

Removing duplicates and errors

One of the basics of accuracy in data analytics is to avoid duplicate data entries. For example, if you’re importing customer data from LinkedIn ads and Facebook ads and you have one and the same contact twice - this is duplicate data.

Errors don’t necessarily have to mean mistakes. For example, you could have irrelevant data. Let’s say you’re trying to upload a list of leads to your CRM for overseeing client relationships and you find a retail business in Asia, but you sell to SMBs in the United States. 

Naturally, you’ll want to remove this entry for better data analysis and visualization.

You can still do your basic data cleaning in Excel at this point. However, if you’re big on data cleaning tools, there are lots of contenders, such as Trifacta, Talend, OpenRefine, and others.

Fixing structural errors

One of the basic issues of dirty data is inconsistencies in structure. For example:

• Typos (e.g. smal vs small)
• Capitalization and lower-case letters (e.g. making sure that december gets changed to December)
• Punctuation differences (N/A vs. NA)

This may seem like a small step but can make a massive difference in how your data is analyzed and visualized. Once again, you can do most of this process in Excel.

Removing outliers

Let’s say you run an app and you want to create a dashboard. For example, you’re putting together data from customer use of your app and most customers use it for an hour per day. And then comes one customer who uses it for 8 hours daily - this is a unique case and an outlier.

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Outliers can skew your data analysis, visualization, and decision making. You need to manually look into cases with significant spikes (in either direction) to make sure these are genuine data points and not user errors.

Unfortunately, there is no way to automate this part of the process but even an app such as Excel can show you this type of inconsistent data.

Take care of missing data

There are cases when you may have incomplete data, which may make it hard to do data analysis. For example, if you’re collecting customer data, you may be missing phone numbers or did not find email addresses for some records. There are two ways you can handle this situation.

One, you can dismiss this data and delete the entry, but this would not result in high-quality data as you could delete crucial information that would lead to better data analysis.

Two, you could manually add the missing fields or use other tools like email finder. This is more time-consuming but results in better-quality data.

Validating data

This is the last step and it’s time to do some QA and validate your data. Once you clean the data and plug it into your favorite data analysis or visualization tool, you can see the results.

Does the data make sense? Did you have a theory before analysis and visualization that you can approve or disprove? Are there data discrepancies and inconsistencies that don’t make sense?

At this point, only you can know if the data makes sense. If you’ve started out with clean data, you now have a case for making better business decisions, predicting future behavior, or identifying patterns.

Standardizing data formats

There are times when the easiest things are the hardest things that you overlook on a daily basis.

When dealing with data from various sources, consistency in format is crucial. For example, dates can appear as "MM/DD/YYYY" in one dataset and "DD-MM-YYYY" in another. This standardization prevents misinterpretations and errors during analysis. To avoid these common issues, it's important to understand the various chart types and data formats used across datasets.

There are three basic steps you can take here: 

• First of all, identify inconsistent formats. Scan your data for variations in date, time, currency, or numerical formats.
• Use formatting functions to their full capabilities. In Excel, use TEXT or DATEVALUE functions to convert formats. For example, =TEXT(A1, "DD-MM-YYYY") converts a date to the desired format.
• Automate with scripts. Automation is a must-have for data nowadays. For larger datasets, use Python libraries like pandas to automate format standardization with pd.to_datetime().

Consistent formats allow you to make sure that all data points are treated uniformly, preventing analytical discrepancies and avoiding bad data visualization outcomes.

Creating and using data dictionaries

A so-called data dictionary provides a detailed description of each variable in your dataset, including data type, possible values, and meaning. Having a solid data dictionary in hand, you can quickly prevent misunderstandings and errors in data handling.

You should:

• document each variable. For example, create a spreadsheet or document listing each variable, its description, data type (e.g., string, integer, numeric), and valid range or categories (e.g., open source, real-worls examples, tutorials).
• map missing values. Define how missing values should be represented (e.g., "N/A", "NULL", or specific placeholder values).
• update regularly: Keep the dictionary updated whenever new variables are added or data structures change. For example, for machine learning and algorithms, you can use a different prefix than for syntax errors. This is particularly important when integrating with data warehouse tools.

A well-maintained data dictionary ensures consistency and clarity in data management, especially when working in teams or with large datasets.

Using conditional formatting to spot anomalies

Conditional formatting highlights cells that meet specific criteria, such as values outside a given range or text inconsistencies. This visual approach makes it easier to spot outliers and anomalies in your data.

Steps:

• Set up rules: In Excel, use the ‘Conditional Formatting’ tool to set rules for highlighting cells. For example, highlight values greater than a specific number to spot unusually high entries.
• Use color coding: Apply different colors to categorize data, such as red for errors, green for acceptable values, and yellow for potential issues.
• Review and correct: Manually review highlighted cells to understand and rectify anomalies.

Visual cues help quickly identify and address data inconsistencies, reducing the risk of overlooking critical errors.

Leveraging data profiling

One effective method for optimizing data cleaning is data profiling. 

This process involves examining your dataset to get an overview of its structure, patterns, and content before diving into the cleaning steps. Data analysts use profiling to identify anomalies, outliers, and duplicate records, ensuring they work with a cleaner, more organized dataset from the beginning. 

Profiling is particularly useful in big data scenarios, where understanding the scope and quality of data can be challenging without this preliminary assessment. Tools like SQL-based platforms or specialized data profiling software can automate and optimize this process, making data collection more efficient and accurate. 

Using data normalization

Another essential technique in data preparation is normalization. This process adjusts values from different scales to a common scale, making them comparable. For instance, if your dataset includes sales figures recorded in various units of measure (e.g., dollars, euros), normalizing these values to a single currency unit prevents skewed analysis. 

Normalization is especially critical in real-world scenarios involving big data, where inconsistent units can distort insights and decision-making. It also facilitates a smoother workflow by reducing the need for constant adjustments during analysis, allowing data analysts to focus on more complex tasks.

Why clean data matters for business dashboards

At Luzmo, we help SaaS businesses create embedded analytics dashboards for their product within hours or days rather than weeks or months. Good, clean data is the basis for each of the dashboards our customers use.

We have customers who use a variety of data sources and who handle different types of data. For example, customers in the HR world, carbon emissions SaaS apps, online food delivery apps, and others.

They need dashboards for themselves and their customers to power their business intelligence and provide more value to the people they serve. To achieve this, they start with clean, well-structured data.

As a result, businesses like Sentiance can create beautiful and informative dashboards like this:

Remember, data analysis and visualization with dirty data is like cooking with the wrong ingredients.

Ready to create your first dashboard?

You don’t need a degree in data science to create beautiful dashboards. And as we’ve learned above, data cleaning is not such a huge deal. With the right quality of data, you can start creating your first dashboards, and we’re here to help.

With Luzmo, your SaaS team can create embedded analytics dashboards that are accurate, easy to use, and fit right into your product.

Ready to get started? Grab your free trial today!

FAQ

What are examples of data cleaning?

Data cleaning includes removing duplicates, correcting errors, handling missing values, and standardizing formats. For example, deleting duplicate customer records, correcting misspelled names, and converting date formats to ensure consistent data quality.

Is data cleaning in ETL?

Yes, data cleaning is a crucial step in the ETL (Extract, Transform, Load) process. It ensures data accuracy and quality by removing errors and inconsistencies before loading it into a data warehouse or analytics system.

What are the best methods for data cleaning?

Best methods include removing duplicates, correcting structural errors, handling missing data, and standardizing formats. Techniques like normalization, outlier detection, and automation tools like OpenRefine or Python libraries streamline the data cleaning process.

What is the difference between data cleaning and data validation?

Data cleaning involves correcting errors and inconsistencies in data, while data validation ensures data meets specific standards and rules. Cleaning focuses on fixing data, whereas validation checks if the data is accurate and complete.