Today every business relies on large volumes of data including facts or figures to carry out their day-to-day activities. This data needs to be accurate and reliable. Over the years, advancements in information technology have optimized everyday operations of business. There are advanced data analysis tools that help in analyzing and verifying data and thereby making it easy to make the right organizational decisions. Outsourcing to data cleansing services helps ensure good quality data that can be utilized effectively.
Significance of Data Cleansing
Data cleansing is the process of detecting and correcting corrupt or inaccurate records from your database. It requires specialized software, subject matter expertise and a strategic project plan to manage an ever changing dataset.
Every organization gathers and processes voluminous data and with data cleansing, and with this organizations can ensure that obsolete data is removed from the system. This also reduces the cost of storage and allows you to be compliant with policies regarding data protection.
The Example of Banks
Consider the example of banks. Banks deal with large volume business transactions and records and this involves a large amount of data. This data needs to be instantly accessed via banking networks. Another important concern is the strict nature of regulations banks need to be compliant to. This makes data cleansing very important for banks. Here is a look at the main concerns banks have.
- Huge data volume
- Need to secure data each and every minute
- Need to observe compliance with all statutory and regulatory requirements
- Secure and efficient interfacing with all legacy applications.
When it comes to data quality, the important considerations are:
- Completeness of data
- Data integrity
- Data accessibility
- Legality of data
- Data accuracy
- Data relevance
To ensure the quality of the data, banks can incorporate strategies such as the following:
- Standardize data by modifying it to uniformly conform to standards that make using and understanding it easier and more effective. This can be accomplished by matching and merging records with a file.
- Use filtering technique to identify duplicate and missing data.
- Identify, locate and correct errors, misspelled, mistyped number values and defective data.
Clean data helps in improving customer acquisition activity results, increasing revenue, productivity, and enhancing account management and analysis. A study “The Cost Of Poor Data Quality” published in Industrial Engineering and Management points out that 75 percent of organizations have identified costs stemming from poor quality data while less than 50 percent of companies claim to be highly confident in the quality of their information.
Strategies to Boost Performance with Good Data
For banks and other organizations, there are three strategies that can help boost performance:
- Identifying data quality issues for data assessment that can give executives a better understanding of the data condition.
- To ensure a smooth data migration process from legacy to new system or ERP, it is important to have a powerful software platform for analysis and cleansing of data; and experienced consultants.
- The third strategy is to have a well crafted guide for data governance that helps reduce cost and risk while maintaining top quality data.
Data cleansing helps in minimizing business disruptions and in instantaneous control and response in complex autonomous systems. The above mentioned strategies and techniques can improve the efficiency of banks and prevent them from incurring huge losses. Hence cleansing business data with the help of data cleansing services is essential for banks as well as other businesses in the financial and non-financial sectors in today’s competitive world.