Data Scrubbing – A Way to Enhance Data Reliability

By |2021-11-01T07:11:34+00:00July 9th, 2020|

A One of the most vital assets of a business is its data, which makes good data management the key to running a successful enterprise empire. As organizations grow, their data volume increases over time, which makes it difficult to manually identify inaccuracies or errors they may contain.

Erroneous data can cost large sums of money. Therefore, businesses need to ensure that their enterprise data is clean, good-quality, error-free, and readily available for reporting and analysis so that the business operations can be cost-and-time effective. This is where data scrubbing comes into play.

Let’s start by understanding what is data scrubbing and why it is important.

What is Data Scrubbing?

Data scrubbing by definition is cleaning raw data and translating it into an accurate, clean, and error-free form. Your data may be erroneous due to various reasons such as improper formatting, human errors at the time of data entry, and/or missing data.

Data scrubbing improves data quality as it removes duplicate, incorrect, incomplete, or poorly formatted data.

Importance of Data Scrubbing

Effective data cleansing or data scrubbing is important as it can help businesses direct their resources towards value-adding activities while highlighting opportunities for cost-cutting. Most organizations work with large amounts of data. With proper management, these inputs enable smooth functioning of daily operations and more accurate decision-making over the long term.

Consider the example of a Logistics function at an eCommerce company. Clean, accessible customer data provides this department with key insights, such as which regions create the most orders, what products are currently popular, and the average order size of customers. Armed with this information, the department can arrange their warehouse and delivery processes to ensure quicker and more cost-effective order fulfillment, customer information management, and more accurate market and sales trend analysis.
This information must be analyzed so that the business can make useful decisions to set up successful strategies.

By comparison, erroneous or bad data would make the analysis incorrect, which can lead to:

  • Time-intensive processes
  • Additional costs
  • Additional labor required to correct the errors
  • Lower efficiency
  • Poor productivity
  • Poor decision-making

In the long run, persistent data quality issues can lead to your business losing customers due to mounting inefficiency and ongoing miscommunications. Therefore, it is important to have a data quality strategy in place. Having bad data can make a dent in any organization’s bottom line. The solution is working with clean, accurate data.

The data gathered by an organization comes from various external and internal sources. In order to get maximum and valid use of it, the raw data must be cleaned and compiled before it can go through other data processes.

Data Scrubbing for Effective Data Management Processes

A vital role is played by data scrubbing in a wide range of data management processes, such as:

Data Integration

Data Integration is the process of combining data from different sources so that it can be consolidated in a single platform. Ensuring data quality in raw data coming from disparate sources with different structures and formats can be time-consuming and difficult. A data scrubbing tool, cleans the incoming data so that the integrated data set is standardized and formatted before being fed into the destination system.

Data Migration

Data Migration involves the transfer of files from one system to another. It is important to maintain data quality and consistency during this transfer so that the correct formatting and structure are present and there is no duplication at the destination. A large volume of data is usually involved in this process. Data scrubbing tools help clean your data efficiently, ensuring better data quality throughout the enterprise.

Data Transformation

All the Data must be transformed before it is loaded onto the destination of your choice to meet the system’s criteria of the format, structure, etc. Data Transformation involves applying certain rules, filters, and data cleaning before it can be analyzed further. A data scrubbing tool help cleanse the data using built-in transformations, enabling you to meet the desired operational or technical requirements ahead.

Data Scrubbing in ETL Processes

Data scrubbing helps prepare data during the ETL (extraction, transformation, and loading) process for reporting and analyses. It ensures that only high-quality data is being used for decision-making and analysis. For example, a retail company receives data from multiple sources, such as a CRM or an ERP system, containing erroneous information or duplicate data. A good data scrubbing or data cleansing tool would find out the inconsistencies in data and rectify them. The scrubbed data will then be converted into the standard format and loaded into a target database or data warehouse.

Benefits of Data Scrubbing Tools

Data scrubbing tools can help you skip through the tedious process of going through all the data manually by cleansing it through built-in transformations. Cleansing data manually involves going through the entries individually, row-by-row, and inspecting them for any invalidities, missing values, etc.

For example, consider the lead list delivered from your marketing team. Now, imagine going through each contact’s name to verify the complete addresses, phone numbers, and email IDs provided. Think of how much time this process takes and the operational issues that could be created if just a few erroneous entries are left uncorrected. On the other hand, data scrubbing tools can help you eliminate errors via automated processes to systematically inspect the data, using different rules and algorithms to identify any flaws and correct them. Hence, making the process of analysis and business intelligence simpler and more effective.

Data scrubbing tools make it easier to clean data without any concerns about mistakes or inaccuracy. They improve the quality of your enterprise data, making it easily available for accurate and useful data analysis. Thus, making data scrubbing tools a worthy investment for businesses.

How To Simplify the Data Scrubbing Process

Astera Centerprise offers business users an easy solution for data integration, featuring built-in connectors that can retrieve information from disparate data sources. Various transformations and automated data validation processes help users perform a range of data-related tasks, including data scrubbing, data cleansing, maintaining data quality, and delivering standardized datasets to their chosen destination.

Centerprise contains features, such as Data Cleanse Transformation, that can be used for data scrubbing and attaining a clean data set for further use.

Let’s take a look at how to scrub data using the data cleansing transformation in Centerprise.

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Figure 1- Data set containing white spaces and formatting issues

The dataset shown in figure 1 contains information regarding different customers, and as you can see, there are some white spaces between the postal codes, and it is not formatted properly. Thus, we will be using the Data Cleanse transformation on this data set.

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Figure 2 – Features of Data Cleanse Transformation

Figure 2 shows the various cleansing options present in this transformation. You can simply remove white spaces, letters, digits, punctuation, or specify any other characters you want to remove. Secondly, you can also replace null characters or find and replace any other characters by applying numerous options given in the fields with one click. You can also apply custom expressions to clean your data.

Figure 3 shows the data preview after applying the Data Cleanse transformation.

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Figure 3- Cleansed dataset

As you can see, all the white spaces have been removed, and the data is now properly formatted. Furthermore, it can be transferred to any destination of your choice.

Other transformations like Data Profiling and Data Quality Rules enable users to profile data sets to get a statistical breakdown and set quality standards to identify records that contain errors or warnings.

Conclusion

The easy-to-use interface and drag-and-drop transformations in Astera Centerprise simplifies information scrubbing. It allows business users to clean high-volume datasets in just a few minutes without writing any code. Automated processes can be set up using workflow automation and job scheduling features that can execute data scrubbing jobs without any manual intervention. This can help you save substantial time and resources and transform data, preventing your business from falling into the negative traps of bad data and poor data management.

Download a free trial and get to know more about how Astera Centerprise can help you get clean, high-quality data.