Understanding Data Scrubbing: 7 Comprehensive Aspects

• Data Scrubbing cannot be overlooked especially when managing Databases because keeping clean data with consistent and accurate input is essential to having and running a smooth business.
• Digital data input is subject to errors as this is done by humans and it would be difficult to avoid making mistakes such as misspellings, redundant entries, incomplete or missing values, and inconsistencies, therefore, there is always the need for cleanup.
• Institutions like banks, insurance, retail, telecommunications, and transportation are driven by heavy data and cannot survive without Data Scrubbing as they constantly need to weed out data flaws by systematically and continually examining generated data.

What is Data Scrubbing?

• Data Scrubbing, also referred to as Data Cleansing, is the act of correcting your data in a Database that has errors, is incomplete, not properly formatted, or has duplicate entries to make it usable before exporting it to another system.
• Data Scrubbing is an integral part of Data Science as it would be difficult working with impure data because this will lead to many challenges.
• A Database Scrubbing Tool generally includes programs that will help in amending specific types of mistakes. Data Scrubbing is done using algorithms, rules, using look-up tables, and other methods.

Why Data Scrubbing is Important?

Data Scrubbing is important as there are numerous benefits.

• Having clean data, free from errors helps increase your efficiency and allows you to have an optimal analysis which will improve your decision-making process. 
• Having incorrect data would mean not having an accurate outcome. Even though your algorithm may be wonderful, it will be processing a wrong Dataset thereby causing you to waste time, effort, and resources as you will be required to carry out the Analysis all over again.
• With Data Scrubbing, you can monitor errors as you will be able to see where the errors are coming from making it easy to fix wrong or corrupt data.
• Data Scrubbing removes errors like duplicates that are inevitable when multiple sources of data are brought together in a Dataset hence streamlining your data to match what is required for usage.
• When you clean up data before trying to get more information from them, your final deductions will be near accurate because they will be fewer errors and this will lead to happy clients, colleagues, employees/employers, management, etc.
• With your data scrubbed, It gives you the ability to map its different functions and get clear insights into what the data is intended to do.

Who Should Employ Data Scrubbing?

Data Scrubbing is an essential part of managing data in a well-mannered format. Different industries and sectors require data to be clean to run their daily activities efficiently.

Let’s go through some of the common sources of Database errors listed below:

• Human errors during manual data entry.
• A lack of company-specific or industry data standards.
• Older Systems with obsolete data.
• Merging Databases.

Some facts about data quality are listed below:

• Due to bad-quality data Ingestion, businesses lose up to 20% of their revenue.
•  Managing data quality is time-consuming, and employees waste about half of their working time handling bad-quality data.
• In an hour, almost 5 dozen of companies change addresses, and names and 50 new businesses opened, which creates data inconsistency.

Data Scrubbing vs. Data Cleaning vs. Data Cleansing

Many times the question arises what is the difference between Data Scrubbing vs. Data Cleaning vs. Data Cleansing? These terms are used interchangeably when it comes to practically applying them in the data preparation process. 

Data Scrubbing is more related to the number of specialized processes involved in the data preparation such as merging, translating, decoding, and filtering data. Data Cleaning involves the process of cleaning the raw data that involves, filling NULL values, identifying outliers, etc. 

We can use Data Scrubbing, Data Cleaning, and Data Cleansing internally as refer to the same process of data preparation because they have the same end goal. 

How does Customer Data Quality Impacts Business Processes?

Customer data quality is directly proportional to the impact on business decisions, and it ultimately touches every facet of your business.

For example, take the Sales data. The sales team-high relies on quality customer data to deliver the context of the conversations that they have with clients. The low-quality data makes the process cumbersome and harms their ability to speak directly to the customer and address their issue.

Similarly, the Marketing data helps manage the Marketing Campaigns and low-quality data makes it difficult for Marketers and companies to create personalized Campaigns and avoid injecting them into your messaging.

The low-quality data highly impact business operations and lowers the benefits. This unscrubbed customer data grows rapidly and takes up more storage than the cleaned data. It will increase the costs of storage and computation and also slows the process making it harder to search through the data. Businesses miss out on $9.7 million on average due to bad data.

What are the 5 Best Data Scrubbing Tools?

In this section, you will read about the best Data Scrubbing Tools that you can use to clean data. The top 5 Data Scrubbing tools are listed below:

• Hevo Data
• Winpure
• Cloudingo
• Trifacta Wrangler
• Data Ladder

1) Hevo Data

Hevo Data, a No-code Data Pipeline helps to integrate data from 150+ sources to a data warehouse/destination of your choice to visualize it in your desired BI tool.

Check out what makes Hevo amazing:

• Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
• Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
• Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
• Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
• Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
• Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
• Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.

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2) Winpure

Winpure is a popular Data Scrubbing tool that helps companies eliminate duplicate data, clean large datasets, and seamlessly correct and standardize the information. It can easily integrate with Access, Dbase, and SQL Server, spreadsheets, CRMs, and more.

3) Cloudingo

Cloudingo is the best Data Scrubbing tool if your company you Salesforce. It can perform Data Migration, delete duplicates, etc. Cloudingo can handle businesses of all sizes and eliminates all human errors. There’s even additional support available for application programming interfaces (API) with REST and SOAP frameworks.

4) Trifacta Wrangler

Trifacta Wrangler is a Data Scrubbing tool that focuses on less formating time and analyzing data. It helps Data Analysts clean data quickly with accuracy so that they can analyze and generate insights from it. Trifacta Wrangler uses Machine Learning algorithms for Data Scrubbing by suggesting common transformations and aggregations.

5) Data Ladder

Data Ladder is a Data Scrubbing tool that is known for its fast speed and accuracy. It features an easy-to-use interface that gives users the power to seamlessly clean, match and deduplicate data. It also taps into an impressive collection of algorithms to identify fuzzy, phonetic, and abbreviated data issues.

What are the Benefits of Data Scrubbing Tools?

• Data Scrubbing manually is a tedious and time-consuming process as it involves manual checking of data entries row by row, which makes it very time-consuming, and there are high chances of human errors. 
• Data Scrubbing tools make all the processes hassle-free as they automate all the Data Scrubbing or data cleaning process by systematically inspecting data based on different rules and algorithms. It makes the data clean and ready for analysis. 
• Many Data Scrubbing tools are available in the market but choosing a good one that suits the company’s requirements is still a confusing part. Enterprises use Data Scrubbing tools to automate their data cleansing process and save time.

Data Scrubbing for Effective Data Management Processes

Data Scrubbing plays a vital role in Data Management Processes. Some of the effective processes are listed below:

Data Integration

• Data Integration is the process of combining data from multiple data sources into a single unified platform that can store huge volumes of data.
• The raw data from the data sources is low-quality data that needs to be structured and transformed into a common format. Data Scrubbing cleans the raw data and transforms it into a standard format so that it can be integrated with other data.

Data Migration

• Data Migration is the process of transferring data from one system to another.
• It is essential to maintain Data Integrity and consistency while migrating data from one system to another.
• It ensures that correct data with the right format and no duplication is replicated to the target system.
• Data scrubbing tools help clean your data efficiently, ensuring better data quality throughout the enterprise.

Data Scrubbing in ETL Processes

• Data Scrubbing plays an important in Data Analytics and decision-making.
• While the ETL (Extract Load Transform) process takes place Data Scrubbing ensures that only high-quality data passes through and loads into Data Warehouse.
• High-quality data can be seamlessly used by BI tools, Data Analysts, and Data Scientists for making smarter and better data-driven decisions.
• Data Scrubbing tools detect anomalies and inconsistencies in data and rectify them automatically. Now cleaned data can be loaded into Data Warehouse or other destinations using an ETL process.

What are the Steps to Perform Data Scrubbing?

You can perform Data Scrubbing, by observing the following:

1. Removal of Duplicate or Irrelevant Values 

• Removing unwanted entries like duplicates and irrelevant data to a given Dataset for further review is a form of Data Scrubbing.
• Duplicate data easily occurs when you have a collection of Datasets from various sources which will increase the volume of your load while irrelevant data can be described as data that does not fit a specific solution, therefore, it is not needed for that Analysis.

2. Avoid Structural Errors

• Structural errors include typos, wrong naming conventions, incorrect capitalization, string size, etc.
• It is good to fix these errors as they can cause categories and classes to be mislabeled.

3. Convert Data Types 

• Another way of Scrubbing Data is to ensure that all data types are uniform across the Dataset. Where a String is applicable, only String values should be inputted as a String cannot be Numeric, neither can a Numeric value be a Boolean and vice versa.
• In situations where you cannot convert a specific data value, the ‘Not Available (NA) value’ should be used.

4. Handle Missing Values

A lot of algorithms do not accept missing data and there will be missing data in a Dataset and this has to be handled before Analysis can be carried out.

• Dropping fields that have missing values especially when it is enormous. Doing this might mean losing information so you have to think through and be careful before embarking on this.
• You can input the missing values based on observations from the other values like using an average or a range. Having said that, inputting missing values may slightly alter the integrity of the data because you may be operating based on assumptions and not facts.
• Thirdly, you can use null values where there are missing values. For example, in cases where Numeric values are needed, 0 can be used to fill up those missing values but you should make sure to ignore these values during Statistical Analysis.

5. Inform Your Team and Co-Workers

After Scrubbing your data, it is important to inform your fellow team, co-workers, etc. of changes done to the data as this would help promote the adoption of the new protocol and create a culture of having quality data within the organization to avoid making similar errors as in the past.

Conclusion

• Since most of the work now revolves around data, it is ultimately important more than ever that Databases are as close to perfection as possible.
• Wrong Data Analysis and submissions can adversely affect a lot of society and this may arise as a result of faulty data.
• Clean data will require protocols and algorithms which will require long lines of code and the right sets of skills.
• These however can be done easily without having to write any code through Hevo Data, a No-Code Data Pipeline.
• Hevo Data does all the work of cleaning up your data and guarantees that your data will be clean, consistent, and ready for Analysis.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite firsthand.