The rapid evolution of business intelligence and analytics has transformed the way enterprises derive value from data. This heavy reliance on information has made managing data quality and ensuring data integrity a top priority for businesses. However, if data quality issues are not identiﬁed and validated at an early stage, bad data can lead to operational inefficiencies, financial losses, and missed opportunities.
That’s where data cleansing comes into play.
What is Data Cleansing and Why is it Important?
Data cleansing, also known as data scrubbing or data cleaning, is the first step in the data preparation process. It involves identifying errors in a dataset and correcting them to ensure only high-quality data is transferred to the target systems. When data is coming from multiple sources, such as in a data warehouse, the need for cleansing data increases as the sources might have redundant data or incompatible data formats.
Moreover, given the increasing reliance on information systems and technology for deriving strategic business insights, poor data quality increases an organization’s exposure to risk. Hence, to remain competitive in today’s dynamic business environment, it is essential to get rid of data inconsistencies. Therefore, enterprises must employ a rigorous data cleansing process to ensure that their data assets are accurate, timely, and complete.
Difference between Data Cleansing and Data Transformation
Businesses generate and receive large volumes of data from every business function. This data is often stored in separate information systems in a variety of formats. To create a central data repository and aid data retrieval and analysis, organizations use various information systems including data warehouses or databases, for storing data. Data transformation and data cleansing are two techniques that help prepare this enterprise data for integration, reporting, and analyses.
Data cleansing helps ensure data accuracy so that only high-quality data is made available for analysis and decision-making. For instance, a business receives data from various sources, such as a CRM or an ERP system, which might contain duplicate data or incorrect information. An effective data cleansing or data scrubbing tool would help identify these inconsistencies and rectify them. The cleansed data will then be converted into a suitable format and loaded into a data warehouse or target database.
On the other hand, data transformation involves converting raw data according to the format and structural requirements of the target database. The data transformation process can be simple or complex depending on the data integration scenario – merge, aggregate, lookups, parse, and join are some of the tasks performed for transforming data into a compatible format.
Data Cleansing and ETL
Although data transformation and data cleansing are two separate terms, many ETL tools offer advanced data cleansing capabilities along with data transformation functionality to cater to complex data management scenarios.
Astera Centerprise is an enterprise-grade data management solution that enables users to evaluate the integrity of critical business data with its flexible data quality and validation features, which help cleanse data during the ETL process, and provides accurate data for business intelligence.
The Role of Data Cleansing in a Data Warehouse
Data warehouses are critical for utilizing historical data for business reporting purposes. However, the question is whether the data stored in a data warehouse is fit for use or not? To ensure that only high-quality data is sent to a data warehouse, a data cleaning tool is used.
Performing data cleansing processes in a data warehouse can be challenging due to the following reasons:
- Semantic Complexity: Semantic complexity refers to the user’s representation of what data represents in a particular database. It generally occurs when different users have different assumptions about what does a particular dataset represent. For instance, a business wants to consolidate data stored in two databases containing ‘Customer Information’ and ‘Order Information’. One user might select Order ID as the primary key while another might mark Customer ID as the primary key which may cause data conflict, leading to missing values in some of the tables.
- Multi-Source Problems: Naturally, data quality issues grow with the increase in the source systems from which you’re integrating data into your data warehouse. Individual data sources contain certain attributes that may be represented differently. Such data sources, when combined, can lead to a higher number of data quality issues. For instance, naming conflicts occur when attributes in different database sources are assigned the same name.
- Single-Source Problems: Generally, data warehouses are built using more than one sources; hence, single-source problems are not common but may still arise. Some of the single-source errors include spelling mistakes, duplicate data, and missing information.
Finding the Right Data Cleansing Software – Features to Consider
Many industries, including banking, insurance, retail, and telecommunications, generate heaps of data every day and need accurate insights for strategic decision making. This makes data scrubbing or cleansing a crucial step as the incoming data might contain errors. However, going through millions of records manually can be a daunting task. Hence, businesses require an intelligent data cleansing solution which can identify any discrepancies based on defined rules.
The key to selecting the right data cleansing tool is research. Browsing through review websites like Capterra, G2 Crowd, etc. will give you a fair idea of what options are available in the industry. However, the most important step is to know about the basic features that will help you streamline the data cleaning process.
Here are the essential features that your data cleansing tool should possess:
- Extensive Data Profiling Capabilities
An end-to-end data cleansing tool should include data profiling features, which can automate the identification of metadata and provide clear visibility into the source data to pinpoint any discrepancies.
- Advanced Data Quality Checks
Data quality checks are objects or rules that you can integrate into the information flow for monitoring and reporting any errors that may occur while processing data. They perform robust validation of the data based on defined business rules to ensure data integrity.
- Easy Data Mapping
To ensure that your data is being cleansed with accuracy, it is essential to correctly map data from source(s) to transformation(s) and then to the destination(s). Tools featuring a code-free, drag-and-drop, graphical user interface can support such functionality. Easy data mapping also enhances the usability of a data scrubbing tool.
- Enhanced Connectivity
A data cleansing tool should provide support for the commonly-used source data formats and destination data structures, including XML, JSON, EDI, etc. Connectivity to popular destination formats allows you to export the cleansed data to versatile destinations, such as SQL Server, Oracle, PostgreSQL, and BI tools, like Tableau and PowerBI. This enables your business to access high-quality information faster for timely decision-making.
- Workflow Automation
This will help automate the entire data cleansing process right from the profiling of incoming data to its conversion, validation, and loading to the preferred destination.
Astera Centerprise – The Smarter Way to Cleanse Data
Astera Centerprise is a complete data management solution that offers data integration and data quality features in a unified platform, facilitating data transformation while ensuring its reliability and accuracy. The advanced data profiling and data quality capabilities allow users to ensure the integrity of critical business data, speeding up the data scrubbing process in an agile, code-free environment.
Astera Centerprise can help businesses cleanse data in multiple ways, including:
- Identification of errors
The first step of every data cleansing process is to identify data inconsistencies. The Data Profile transformation allows the user to examine source data and get detailed statistics about the content, structure, quality, and integrity of data.
Figure 1: Data Profiling
The screenshot below shows the data profiling results of sample customer data. Users can study the source data and determine the error count, blank count, data type, duplicate count, etc.
Figure 2: Data Profiling Results
- Correcting Duplicates in Data
Data duplication is one of the major causes of poor-quality data and is often caused due to manual data entry or data consolidation from multiple sources. To get a unified view of data, you must merge the complementing datasets and remove the duplicates. For deduplication, you can use the Distinct transformation, which is designed to identify and remove duplicates from data.
Figure 3: Distinct Transformation
- Correcting Incorrect Information
Businesses receive data from multiple sources that might contain incorrect information, thus affecting data accuracy. Moreover, using this data for reporting and analytics can lead to misleading results.
Using the wide selection of advanced transformations available in Astera Centerprise, users can tackle any data cleansing scenario.
Figure 4: Data Cleansing
For example, in this scenario, the source data contains incorrect email addresses. Now, the company wants to ensure that all email addresses contain ‘@.’
The Expression transformation can help fix these errors. With the help of ‘ReplaceAll’ expression, ‘@’ can be added to the records as shown in the screenshot below.
Figure 5: Expression Builder
Want to know more about how Astera Centerprise can help improve the quality of your enterprise data? Get in touch with our solution architects to work out a plan to scrub, clean, and validate data using advanced automation techniques and ensure your business users get accurate analytics.