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, making it challenging to identify inaccuracies or errors they may contain manually.
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 to be cost-and-time effective. This is where data scrubbing comes into play.
Let’s start by understanding data scrubbing, why it is essential, and how data scrubbing tools simplify this process.
What is Data Scrubbing?
Scrubbing data is the process of cleaning raw data and translating it into an accurate, clean, and error-free format. Data can be erroneous for various reasons, such as improper formatting, human errors during data entry, missing data, etc.
Data scrubbing improves data quality by removing duplicate, incorrect, incomplete, or poorly formatted data.
Is it Different from Data Cleaning?
Data cleaning and data scrubbing are often used as synonyms. On a surface level, the two terms can be used inter-changeably. However, data cleaning and scrubbing differ on a technical level. Data cleaning is the broader term for preparing analytics-ready data. Data scrubbing comes under the umbrella of data cleansing, and it deals with removing inconsistencies in data and ensuring proper formatting.
Importance of Data Scrubbing
Effective data cleansing or data scrubbing is essential as it helps businesses direct their resources towards value-adding activities instead of manual data cleaning and scrubbing. Simultaneously, it highlights opportunities for cost-cutting. With clean, trusted data at hand, companies can execute 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.
If the company has clean, consistent customer data at hand, they can get access to crucial insights readily, such as which regions create the most orders, what products are currently popular, and the average order size. 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 reliable and trustworthy so that the business can make sound decisions to set up successful strategies.
By comparison, erroneous or flawed data would make the analysis incorrect, which can lead to:
- Time-intensive processes
- Additional costs
- Additional labor is required to correct the errors
- Lower efficiency
- Less productivity
- Poor decision-making
In the long run, persistent data quality issues can lead to your business losing customers due to mounting inefficiency and constant miscommunications. Therefore, it is essential to have a data quality strategy in place.
An organization gathers data from various external and internal sources. To get maximum and valid use of data, it must be cleaned and compiled before going through other processes.
How Data Scrubbing Simplifies Data Management
Data Scrubbing plays a vital role in a wide range of data management processes, such as:
One of the core data management processes is Data Integration. It is the process of combining data from different sources to consolidate it in a single platform. 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 involves the transfer of files from one system to another. It is essential to maintain data quality and consistency during this transfer so that the destination data is in correct formatting and structure and there is zero duplication. The transfer process also involves a large volume of data. Data scrubbing tools help clean your information efficiently, ensuring better data quality throughout the enterprise.
All 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 specific rules, filters, and expressions to the data before loading it into a system. A data scrubbing tool helps cleanse the data using built-in transformations, enabling you to meet the desired operational or technical requirements ahead.
Data scrubbing helps prepare data for reporting and analysis during the ETL (extraction, transformation, and loading) process. Data preparation ensures that only high-quality data is 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 tool would identify the inconsistencies in data and correct 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 the tedious and manual process of data cleaning, saving you the trouble to go through the entries individually, row-by-row, and inspecting them for any invalidities, missing values, etc. Instead, the tool cleanses the data through built-in transformations.
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. Erroneous lead entries can create operational issues and lead to time wastage.
Data scrubbing tools can help you remove errors via automated processes to systematically inspect the data, using different rules and algorithms to identify any flaws and correct them. Hence, making the analysis and business intelligence more straightforward and effective.
Scrubbed data improves your enterprise data quality, making it readily available for accurate and valuable data analysis. Thus, making them a worthy business investment.
How To Simplify the Data Scrubbing Process
Astera Centerprise offers business users an easy solution for data scrubbing, cleaning, and integration. The solution features 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, 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 look at how to scrub data using the data cleansing transformation in Centerprise.
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 correctly. Thus, we will be using the Data Cleanse transformation on this data set.
Figure 2 shows the various cleansing options present in this transformation. You can 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 use custom expressions to clean your data.
Figure 3 shows the data preview after applying the Data Cleanse transformation.
As you can see, all the white spaces have been removed, and the data is now correctly 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.
The easy-to-use interface and drag-and-drop transformations in Astera Centerprise simplify information scrubbing. It allows business users and data analysts to clean high-volume datasets in just a few minutes without writing code. Users can also set up automated data pipelines. These pipelines use automation and job scheduling features to execute data scrubbing jobs without any manual intervention. Scrubbed and cleansed data can help you save substantial time and resources when transforming data.