All Blogs
Data Analysis

Essential Techniques for Effective Data Scrubbing & Cleaning

February 5, 2024
min read

We know that almost every organization today is data-driven, meaning it uses data for its business strategy and growth. However, despite many businesses analyzing and tracking their data trends, the data being used for decision-making is not always ideal. It can include duplicate values, typos, incorrect or modified entries, and even missing fields.

As per Gartner, improving data quality (DQ) will be a key factor for most organizations, and in 2024, 50% of organizations will adopt modern DQ solutions to support their business initiatives.

While this “bad” data can be an issue for data analytics, it can create more harm if used for ML model training. To ensure that ML models are trained using sanitized data, organizations must adopt data cleaning and scrubbing into their processes.

Here is a look at what these terms mean and how they help you ensure optimal data quality for your AI projects.

Understanding Data Scrubbing and Cleaning

Data scrubbing and data cleaning are often used interchangeably, but there are subtle differences that make this usage inaccurate.

Data cleaning, also known as data cleaning, is a process that involves tidying up data, focusing on processes like data formatting, identifying data inconsistencies, and correcting or deleting these errors. Data professionals work on data cleaning to ensure that raw data is transformed into the required format, creating consistency.

Representation of Data cleaning cycle

Data scrubbing, on the other hand, is the process of modifying or removing incomplete, incorrect, inaccurate, and repeated data from the database. This usually happens after the cleaning process, as datasets need to be in the same format and type to identify gaps.

Pre-Cleaning Assessment

Before we start data cleaning and scrubbing, we must ensure the incoming data is properly analyzed. This is where the pre-cleaning assessment comes into play and encompasses processes for specific activities such as:

  1. Understand the Context: This involves data source exploration, which delves into the data's origin, collection methods, and potential biases and helps predict the types of errors and inconsistencies that might be present.
  2. Quantify the Mess: Analyzing means, medians, variances, and other statistical measures unveil the overall data distribution and potential outliers.
  3. Prioritize the Cleansing Tasks: Understanding areas that need cleansing and analyzing the potential impact on data quality helps prioritize resources effectively.

This initial step sets the stage for the subsequent cleaning techniques, guiding the data cleaning strategy based on the specific challenges identified during the assessment.

Manual Data Cleaning Techniques

Data cleansing starts with converting raw data into actionable data format. However, most organizations still do this manually, which can often lead to inaccuracies and inconsistencies, especially if the data set is large.

Manual data cleaning involves the use of tools like:

  • Spreadsheets: The use of Microsoft Excel, Google Sheets, or other tools that can help with basic data cleaning features. These allow you to sort, filter, and freeze columns to find and edit inconsistencies like typos, formatting errors, and missing values.
  • Database Languages: Some organizations will use database languages like Qlik, RDBMS, SQL, or NoSQL for data standardization and cleaning.
  • Scripting Languages: For larger datasets, many organizations will use Python or R to help automate repetitive tasks like identifying outliers, formatting dates, or imputing missing values.

These tools can be a great starting point for data cleaning and scrubbing. However, as datasets grow in size and complexity, there is an increasing need for automated solutions.

Automated Data Cleaning Tools

Automated data cleaning tools have emerged as indispensable assets in the data scrubbing process to meet the demands of large and intricate datasets. These tools leverage advanced algorithms and machine learning techniques to identify, rectify, and enhance data quality at scale.

Some of the most used data-cleaning tools include:

1. OpenRefine

OpenRefine data cleaning tool website

This open-source tool tackles everything from basic deduplication and string manipulation to sophisticated data transformation and visual profiling. OpenRefine's user-friendly interface and powerful scripting capabilities make it a versatile choice for both beginners and experts.

2. Trifacta Wrangler

Trifacta Wrangler, now Alteryx, is a commercial tool that boasts lightning-fast performance and intuitive visual data flows. Drag-and-drop modules let you cleanse, transform, and enrich your data with ease, even if you're not a coding whiz.

3. DataWrangler by Stanford

DataWrangler data cleaning tool website

This open-source Python library empowers data scientists with powerful functionalities for data exploration, cleaning, and transformation. Its integration with popular Python libraries like NumPy and Pandas makes it a favorite amongst coders and data scientists.

4. Pandas Profiling

Pandas Profiling data cleaning tool website

Built for Python enthusiasts, the Pandas Profiling library generates a comprehensive statistical and visual report of your data, highlighting potential issues like missing values, outliers, and data type inconsistencies. It's like having a data detective at your fingertips, uncovering hidden problems within your datasets.

5. Open Data Profiler (ODP)

ODP data cleaning tool website

An open-source Java tool, Open Data Profiler caters to large-scale data environments. It scans your data across various file formats and databases, uncovering anomalies and generating detailed reports to guide your cleansing efforts.

Challenges in Data Scrubbing and Cleaning

Data cleaning and automation tools are your allies in improving data quality and consistency. However, it is not a replacement for your own data cleaning and scrubbing processes. This is because the data cleaning journey can have several pitfalls, which include:

  1. Incomplete or Inaccurate Data Documentation: Inadequate documentation hampers the understanding of data, making it challenging to identify errors. The lack of information about data sources, collection methods, and variable definitions can lead to misinterpretations and flawed analyses.
  2. Handling Missing Data: Every data set has its gaps, but the question is how to handle them. Strategies for handling missing data include imputation, where missing values are estimated based on existing data, or exclusion if the missing data is too significant.
  3. Identification and Removal of Duplicate Data: Duplicates can distort analyses and mislead decision-making. Advanced algorithms and matching techniques can be employed to identify and eliminate duplicate entries systematically.
  4. Selecting Appropriate Data Cleaning Techniques: Choosing the right tools and techniques is crucial to ensure the overall quality of our data. This should be done with a strong understanding of the data sets, the platforms used, and other factors that play a part in ensuring data quality and governance. You should also assess available resources, time, and budget to choose a cleaning approach and tool that aligns with your requirements. 
  5. Balancing Accuracy and Computational Cost: While thorough cleaning is ideal, it can be computationally expensive, especially with large datasets. Striking a balance between accuracy and efficiency is crucial.
  6. Handling Time-consuming Processes: Some cleaning processes, particularly manual ones, can be time-consuming. Efficient strategies and tools are needed to streamline these processes without sacrificing accuracy.
  7. Ensuring Consistency Across Datasets: When dealing with multiple datasets, maintaining consistency in cleaning approaches is challenging. Establishing standardized procedures helps ensure uniform data quality.

Best Practices for Effective Data Scrubbing

Now that we have covered the challenges and the tools that you can implement for your data cleaning and scrubbing process, here is a look at some of the best practices. These guidelines can help elevate your data quality outcome, enabling you to gain data consistency and clarity for your ML models.

1. Plan Before You Clean

Data cleaning doesn't just start with the actual cleaning process. You need to first define the goal and understand the context of the data. So before you begin:

  • Define your Goals: What are you trying to achieve with your analysis? Tailor your cleaning efforts to address these specific needs.
  • Understand your Data: Explore its context, origin, and potential biases. This helps anticipate challenges and guide your cleaning strategy.
  • Document your Process: Keep a detailed record of your cleaning steps, decisions, and tools used. This ensures transparency and repeatability for future analyses.

2. Clean Iteratively

Once your data cleaning and scrubbing goals are defined, start by focusing on aspects that need attention. This includes:

  • Focusing on tackling the most critical errors and inconsistencies first. This ensures quick wins and keeps you motivated.
  • Regularly check your cleaned data for unexpected changes or residual errors. Don't wait until the end to discover problems.
  • Learn from each iteration. Adjust your techniques and tools based on what works best for your specific data.

3. Prioritize Data Quality Metrics

Define and prioritize metrics that matter most to your analysis. Focusing on critical aspects ensures that the cleaning process aligns with the objectives of the data analysis.

4. Collaboration is Key

Foster collaboration among data scientists, domain experts, and stakeholders. A multidisciplinary approach brings diverse perspectives and insights, enabling more robust data cleaning.

5. Test and Validate

Aim for clean, reliable data, but don't get bogged down in endless scrubbing. Sometimes, "good enough" is good enough. You may even test and validate your data to ensure that it is good enough for your ML models and define a baseline to ensure that there is refinement as you progress.

Future Trends in Data Scrubbing and Cleaning

While manual and automated tools have served us well, the data-cleaning landscape constantly evolves. Just like any other industry, data cleaning processes are continuously being automated using the power of AI and other new-age technologies. Some of these include:

1. Advanced Machine Learning Integration

Once the data cleaning process is defined, it includes several repetitive tasks and workflows. Machine learning algorithms can learn from past cleaning efforts, automate repetitive tasks, and even suggest new cleaning strategies. This means less time spent scrubbing and more time exploring the insights revealed by your sparkling data.

2. AI To Detect Outliers

Data scrubbing involves going over the cleaned data for any outliers or anomalies. AI can help find these inconsistencies faster and improve the accuracy of the data. This means humans no longer have to painstakingly hunt for anomalies or use multiple data formatting rules to find these. AI can be your Sherlock Holmes and inform you about any data points that do not meet your specific standards.

3. Collaborative Data Cleaning Platforms

Data cleaning often happens in silos, with each team cleansing and formatting datasets to meet their specific requirements. Platforms facilitating collaborative data cleaning efforts are slowly making an impact, allowing teams to work seamlessly on refining and validating datasets.

In the future, this will be even more collaborative, allowing teams to share cleaning rules, best practices, and even entire datasets for a more structured cleaning process.


Data cleaning and scrubbing may seem like an additional step for most ML projects, but this is crucial for the accuracy of your project. Unclean data may lead to issues that may not be noticed at first glance but will lead to problems if the ML model is trained using this wrong data.

To help you automate and simplify your data-cleaning workflows, you can use innovative platforms like MarkovML. Be it for students, data scientists, or ML engineers, the collaborative data intelligence and reporting platform helps you clean and manage your data meticulously, ensuring your data is consistently clean and ready for analysis.

Explore our Data Intelligence & Management Solution for more details.

From Data To GenAI Faster.

Easily Integrate GenAI into Your Enterprise.
Book a Demo

Create, Discover, and Collaborate on ML

Expand your network, attend insightful events

Join Our Community