A Beginner’s Guide To Data Cleaning

Everybody involved in data collection and analysis knows that the quality of insights and analysis derived from data largely depends on the data quality. 

If the data is of good quality, the resulting analysis will also be accurate and reliable. As such, it is crucial to prioritize data cleaning to ensure quality data-driven decision-making within your organization. 

In this article, I’ll give you a brief overview of data cleaning, its importance, and the best practices you can follow to ensure that only clean data enters your decision-making pipeline.

Data cleaning, also known as data cleansing and data scrubbing, is an essential prerequisite for any data analytics, forecasting, projection, or similar decision-making process. 

Data cleaning refers to the process of identifying and correcting errors, duplicates, inconsistencies, incompleteness, and other data quality issues in a dataset. 

For example, when merging data from different sources, there is a high chance of encountering duplicate or mislabeled data. If not adequately addressed, these errors can result in unpredictable outcomes and algorithms, despite appearing correct.

Since the data cleaning process can vary depending on the dataset and the nature of data points, there is no single definitive method you need to follow. However, it is essential to establish a standard data cleaning procedure within the organization to ensure consistency and accuracy across different datasets.

Why is that more important than a single high-level data cleaning process that applies across the board?

The answer is simple:

Each department within the organization and each team within a department might deal with different datasets. For instance, the datasets used by the HR and financial control departments might be very different. The same goes for businesses within different industries – an engineering firm has very different data sets from a hospital. 

That’s why having a consistent approach to data cleaning helps to ensure that the process is carried out correctly every time without getting bogged down by the nature of data points, the collection methodologies, and the subsequent use cases of clean data.

Now that you know about the basics of data cleaning, it’s time to address a fundamental confusion about the idea. 

Data cleaning involves the removal of data that is not relevant or appropriate for the dataset. On the other hand, data transformation involves converting data from one format or structure to another. This process may also be referred to as data wrangling, munging, or mapping, which involves transforming and shaping raw data into a format that is suitable for storage and analysis.

In most cases, data transformation comes after data cleaning when the “clean” dataset is used as input for a process. At this point, the data is transformed into an appropriate format for the tools to kickstart the process. 

If the organization has a standardized toolset and processes, transforming data can happen as the final step of the data cleaning process because the users already know the data formats they need for further processing. 

I think we have discussed the theory of data cleaning long enough!

As I mentioned earlier, cleaning data is a highly subjective idea that depends upon several factors, including the nature of the dataset, how data is collected, and, most importantly, how the various departments will use clean data for further processing. 

That’s why, instead of coming up with a specific checklist for cleaning data, I’ll now discuss a framework you can use to develop a data cleaning process for your organization. I’ve tried to make the framework data and tool-agnostic so you can use it with all sorts of industries and scenarios. 

The framework below presents a high-level view of the data cleaning process. 

It assumes that you already have a raw dataset collected from various sources. The dataset is in a format you can manipulate easily to implement the steps of the data cleaning process. After cleaning the data, you can retain it in the current format or transform it into a different format.

With this prerequisite declaration out of the way, let’s go into the framework’s steps.

Start by removing unwanted data points from your dataset, including duplicates and irrelevant observations. 

Duplicate data points are common during data collection when combining datasets from various sources, scraping data, or receiving data from multiple departments or clients. Therefore, deduplication is a crucial step in the data cleaning process. 

Irrelevant observations refer to data points that do not align with the specific problem you are analyzing. For instance, if your goal is to analyze data related to baby diapers, removing observations related to adult diapers can make your analysis more efficient, minimize distractions from your primary target, and create a more manageable and high-performing dataset.

Note that while de-duplicating is always an essential starting point for cleaning data, removing irrelevant observations is a matter of perspective. The relevant data points in one scenario might not be relevant in others.

I suggest implementing a deduplication step at the start and then setting up an observation removal step that allows users to select the data points they wish to remove from the current analysis. 

Structural inaccuracies refer to inconsistencies in data labeling conventions, typos, or incorrect capitalization that can result from measuring or transferring data. These errors can lead to mislabeled categories or classes. 

A common structural inaccuracy is labeling “N/A” and “Not Applicable” as two separate categories in the dataset. While the two might appear distinct to automation tools, logically, they refer to the same idea. 

Removing structural inaccuracies is an important step in cleaning data because it leads to higher trust in data accuracy. 

A good starting point is to map out the data and come up with categories and conventions that can be applied to data collection, either as input filters or post-entry formatting.  

While you can automate the first two steps, you must pay close attention to this step. 

Let’s start with a simple definition of data outliers. 

Data outliers are observations in the dataset that appear to be “outside” of the main “body” of the data points. 

Data outliers can occur because of abnormal measurement/collection or a correctly measured observation that falls away from other observations. 

Two good examples are:

• A student scoring below the class average (correctly measured observation that falls away from the main collection) 

• An incorrectly entered product serial number (incorrectly measured observation that falls away from the main collection)

The most important thing to remember here is that data outliers are not necessarily BAD or INCORRECT. 

So, if there is a valid reason to remove an outlier, such as improper data entry, it can improve the performance of the data. However, outliers can also be crucial in proving a theory or discovering new insights and trends.

Now how should you consider data outliers when creating a data cleaning process?

The answer is that it depends on whether the outliers occur for an acceptable reason and whether you want to include them in the clean data. 

Similar to Step 1, where you need to think about what observations are irrelevant to your analysis, you also need to evaluate whether you need to include data outliers in the dataset that would be used for analysis. 

Datasets often have missing data that can disrupt analytics programs because they expect complete datasets with no missing values. 

Dealing with missing data is a serious challenge because it affects the accuracy of the decision making process. However, there isn’t much you can do about it because, often, there’s no way of filling in these gaps in the dataset. 

Gaps in a dataset pose a serious problem in data cleaning because, unlike data outliers and irrelevant observations, there’s no easy solution to plug these gaps so that the data set can be used in automated analysis. 

One way of dealing with the gaps is to remove the observations with missing values. While this fixes the problem, you might find that the dataset is now missing important information for decision making. So, before removing anything, consider the impact of the move. 

Another option is to “guess” missing values based on other observations. However, this approach can introduce bias and compromise the integrity of the data, as assumptions are being made rather than relying on actual observations.

A middle ground between these two approaches is to adjust the data in a way that allows for null values to “fill” the gaps. 

If you plan to go with this option, you need to optimize the data cleaning process to account for the null values. Similarly, your decision-making processes should recognize and give appropriate weightage to the null values. 

At the conclusion of the data cleaning process, it is essential to validate the data and ensure it is of high quality. 

At this stage, your data cleaning process should ask questions such as: 

• Does the data make sense?
• Does the data conform to the appropriate rules for its domain?
• Does it support or refute your working hypothesis or provide any new insights?
• Are there any trends in the data that can inform future hypotheses?

If the answers to these questions are not satisfactory, data quality may be problematic. 

If you don’t focus on improving data quality, you will end up drawing false conclusions from dirty or incorrect data. This leads to poor decision-making and negative business outcomes.

Establishing a culture of high-quality data within your organization and documenting the tools and standards necessary to achieve this is critical.

What’s Quality Data?

How can you label data as quality data?

Well, for an organization, quality data is data that has all the right attributes so that it’s relevant to analytics, trend discovery, and decision-making at all levels. 

Five key attributes determine the quality of data:

• Validity: The extent to which the data adheres to defined business rules or constraints.
• Accuracy: How closely the data represents the true values or facts it intends to capture.
• Completeness: The degree to which all necessary data has been collected and recorded.
• Consistency: The degree to which the data is consistent within the same dataset or across multiple datasets.
• Uniformity: The extent to which data is specified using the same unit of measurement or format.

Benefits of Data Cleaning

Clean data has numerous benefits, including increased productivity and the ability to make informed decisions based on high-quality information. 

Here are some advantages of having clean data:

• Elimination of errors when working with multiple sources of data.
• Happier clients and less-frustrated employees due to fewer errors.
• Ability to map different functions and understand what your data is intended to do.
• Better monitoring and reporting of errors, making it easier to identify the source of incorrect or corrupt data and prevent similar issues in the future.
• Use of data cleaning tools to streamline business practices and accelerate decision-making.

Now, how would you scale the data cleaning process and get the benefits mentioned above?

Fortunately, data cleaning is such an important idea that you can find a long list of tools that take care of the process and help you implement the framework within your organization. 

Top Tools For Cleaning Data

Choosing the right tool for the job is winning half the battle. The challenge in using tools for cleaning data is that there are just too many of them!

The following list of tools is my attempt to kickstart your data cleaning tool discovery process.    

OpenRefine

OpenRefine, previously known as Google Refine, is an open-source data tool well-known for its many benefits. 

Its main advantage over other tools is that it is free to use and can be customized to fit your specific needs. With OpenRefine, you can easily transform data between different formats and ensure that the data is clean and well-structured. Additionally, you can use it to parse data from online sources.

Although it looks similar to spreadsheet software like Excel, OpenRefine functions more like a relational database, making it ideal for data analysts who need more advanced features. 

A key benefit is that you can work with your data on your own machine, which enhances data security. However, if you need to link or extend your dataset, you can easily connect OpenRefine to external web services and cloud-based sources.

If needed, you can also upload your data to a central database. However, keep in mind that OpenRefine may require some technical knowledge, despite its ability to streamline many complex tasks, such as using clustering algorithms.

Some of the key benefits of OpenRefine include its free and open-source nature, support for over 15 languages, ability to work with data on your machine, and its ability to parse data from the internet.

Trifacta Wrangler  

Trifacta Wrangler is an interactive tool for data cleaning and transformation developed by the creators of Data Wrangler. It allows data analysts to efficiently clean and prepare diverse data using machine learning algorithms that suggest common transformations and aggregations. 

One of its key advantages is its focus on analyzing data rather than formatting, leading to faster and more accurate results.

Trifacta Wrangler is a connected desktop application that provides data transformation, analysis, and visualization capabilities. Its standout feature is its use of smart technology, including AI algorithms that can identify and remove outliers and automate data quality monitoring.

The tool’s UI allows for visual and intuitive data pipeline creation, saving time and streamlining workflows.

Trifacta Wrangler also offers additional features through its suite of products, such as Wrangler Pro, which supports larger datasets and cloud storage, and the enterprise version, which includes collaboration tools and centralized security management for working with sensitive data.

Trifacta Wrangler offers several benefits, including reduced formatting time, increased focus on data analysis, quick and accurate data preparation, and machine learning algorithm suggestions.

WinPure Clean & Match   

WinPure Clean & Match is a powerful tool that effectively cleans, standardizes, and removes duplicates from massive datasets. 

Unlike other tools, you can use it with databases, CRMs, spreadsheets, and other sources. In addition, it offers a user-friendly interface to clean, de-duplicate, and cross-match data, ensuring data security as it is locally installed. 

It is specifically designed for cleaning business and customer data, including CRM data and mailing lists. 

With its ability to interoperate with various databases and spreadsheets, it supports fuzzy matching and rule-based cleaning that you can program yourself. It is available in four languages: German, English, Portuguese, and Spanish. 

WinPure offers many advantages, including effective cleaning of large data sets and support for a local installation for enhanced data security. 

TIBCO Clarity 

TIBCO Clarity is a cloud-based data cleaning tool that offers a wide range of features to clean and analyze raw data from multiple sources. 

It supports various file formats, including XLS, JSON, and compressed files, as well as online repositories and data warehouses. In addition to data mapping, ETL, and de-duping, it also offers a unique feature called ‘transformation undo’ for greater control over data changes.

Although there’s no free version, TIBCO Clarity provides on-demand software services via the web, helping to validate and standardize data from disparate sources. This results in quality data leading to better decision-making processes and more accurate analysis.

Here are the advantages of TIBCO Clarity:

• It’s a cloud-based SaaS solution.

• Supports multiple file formats and data sources

• Provides a wide range of features for data cleaning and analysis

• Offers’ transformation undo’ feature.

• Helps with standardizing and validating data

• Leads to better decision-making processes and more accurate analysis.

IBM InfoSphere Quality Stage    

IBM InfoSphere Quality Stage is part of IBM’s suite of data management tools, with a focus on data quality and governance. 

With over 200 pre-built data quality rules, it’s designed to clean big data for business intelligence purposes, making data matching and deduplication faster and more efficient. It also supports tasks like data warehousing, master data management, and migration and offers a deep level of data profiling to explore the content, quality, and structure of data.

While it’s not the most user-friendly tool, it does provide a data quality scores feature that enables any user to understand a dataset’s integrity, making it useful for executive-level stakeholders. 

Using IBM InfoSphere Quality Stage, you can easily manage your database and build consistent views of your key units, such as customers, vendors, products, and locations. 

This tool is designed to support data quality, and it’s popular for delivering quality data for big data, business intelligence, data warehousing, and master data management.

Advantages of IBM InfoSphere Quality Stage include:

• Supports full data quality and information governance

• Easy cleansing and database management

• Useful for big data and business intelligence

Data Cleaning Best Practices

Keeping data clean is essential to extracting the most benefit from the data. But, as the data grows in volume and complexity, keeping the data clean and ensuring that all data goes through the data cleaning process can be quite challenging. 

To help you out, here are some general best practices that can help you maintain clean data:

Identify Business Use Cases for Improving Data Quality

Identify which areas of your business will benefit the most from higher data quality. Clear connections between data assets and business outcomes can guide the formation of a database hygiene practice. 

For example, if you find that your online store has obsolete product information and you only sell 40% of the products stored in your database, estimate how much your data cleaning efforts will improve your store’s performance.

Standardize Data Entry

Decide how data should be entered and formatted into systems so teams and departments can follow these standards across the organization. 

For example, inputting CRM data should include a standard way to enter a contact’s title and phone number, such as using dashes or no dashes or accepted abbreviations to describe industries.

Correct Data at the Source

Ensuring data accuracy at the source can save businesses many hours of labor and effort spent on correcting the data once it has made its way into other systems and downstream processes. 

You can significantly improve data quality by checking if the data is correct upon entry.

Having clean and standardized data at the point of entry is crucial for maintaining a clean database and ensuring all important attributes are free of issues and mistakes. In addition, creating and enforcing a standard operating procedure for entering data can help ensure that only high-quality data is entered into the system.

Start With Proper Data Procedure

When introducing a new system into your technology stack, such as a new CRM, implement data hygiene practices from the start. 

You can start by Set document categories and establishing smart data policies, such as limiting how many people can edit data or perform a duplicate check. 

Next, make dropdown boxes available where possible to increase the chances of consistency in data from the start. 

Now to track the efficacy of the hygiene processes, create data quality KPIs to set expectations for your data and track its health. You need to come up with KPI tracking mechanisms that integrate with the data cleaning QA process. 

The idea of data hygiene can be summed up as follows:

• Identify where most data quality errors occur.
• Identify incorrect data.
• Understand the root cause of the data problem.
• Develop a plan for ensuring the health of your data.

Set Regular Data Cleaning Maintenance

Data cleaning should be baked into your normal operational processes and data management. It needs to be done regularly, not just a one-time thing. 

A good practice is to make data cleaning an integral part of the data acquisition process. This simple step minimizes the incidences of bad data going into analysis and decision making pipelines. 

After cleaning the data, communicate the importance of clean data with everyone across the organization, regardless of their function.

Create a Feedback Loop

Once you have pinpointed the source of dirty data and fixed it, create a feedback loop by re-indexing content and using the same interface to verify that the bad data is gone.

An important aspect of the feedback loop is to educate users on the sources of bad data and how they can avoid getting the data contaminated by following the process outlined by the organization. 

Conclusion

Data cleaning is a critical step in ensuring the accuracy and reliability of your data.

By following the steps outlined in this blog, including identifying business use cases, standardizing data entry, correcting data at the source, starting with proper data procedures, setting regular data cleaning maintenance, and creating a feedback loop, you can ensure that your data is free of issues and mistakes.

Data cleaning has many benefits, including improved decision-making, better customer experiences, reduced risk of errors and inaccuracies, and increased efficiency and productivity.

By investing time and resources into data cleaning, organizations can save themselves from costly mistakes and drive better outcomes for their business.

You can use several top tools to implement data cleaning processes within your organization. These tools can help automate the cleaning process and make it easier for teams to manage and maintain data hygiene.

Data cleaning is critical for any organization that wants to make the most of its data. By following best practices and using the right tools, organizations can ensure that their data is accurate, reliable, and ready for analysis and decision-making.