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Owing to the sensitive nature of their industry, banks have to maintain data that has been cleaned, categorized, and classified. Regulations in the financial industry are frequently changing and increasing in number and scope. This requires banks to spend a large part of their budget to ensure compliance with these requirements. In addition to regulatory pressure, banks must remain competitive and efficiently serve the rising demands of their customers. After all, it’s becoming harder to acquire new customers and keep them satisfied for long periods of time.

What is data cleansing? Data cleansing, also called data scrubbing, involves the fraud detection and removal of errors and inconsistencies from data in order to improve data quality. Quality issues are present in data collections, such as files and databases, due to misspellings, incorrect data entry, missing information or other invalid entries. When multiple data sources need to be integrated, such as in data warehouses, federated database systems or global information systems, the need for data cleaning increases significantly. In order to provide access to accurate and consistent data, consolidation of different data representations and elimination of duplicate information become necessary.

Banks typically deal with data that contains business transactions and records that need to be accessed almost instantly from throughout their banking networks. Large businesses driven by modern technology operate through a large number of channels, which in turn generate large volumes of data that modern banks must support. New technologies such as ERP, SCM, and CRM systems have been supporting the needs of such large organizations, and generate even more volumes of data that modern banks must manage and ensure the quality of.

Source of Unclean Data

Misleading, missing, duplicate, or otherwise invalid data can come from quite a number of sources, including, but not limited to:

  • Interfacing and integrating with other systems and databases across the globe. Systems are set up differently in different parts of the world, miscommunication happens between these Internet-based systems

  • Any paper documents anywhere in the data chain can easily be the source of error as they fed in manually

  • Any changes to the account holder’s information that needs to be shared across different applications and systems within the banking network

  • Information from different places such as call centers can be erroneous or incomplete as operators enter details manually

  • Data from third-party partners or systems that has errors in it could enter automatically and be incorrect

  • Business level changes in banks, such as mergers and acquisitions, require reintegration of data that can lead to duplicate entries, missing entries, and even corrupt data.

To gauge whether or not data is of good quality, banks can check the following factors:

  • Data Integrity

  • Data Completeness

  • Accessibility

  • Data’s Timeliness

  • Data Accuracy and Validity

So how do banks manage the quality of their data over simply trying to keep a better check over these issues? 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.

  • Use the filtering technique to identify duplicate and missing data.

  • Identify, locate and correct errors, misspelled, mistyped number values and defective data.

  • Locate and correct inaccurate and defective elements and values like misspellings and mistyped number values.

The Benefits of Cleansing

Cleaning data and managing it to maintain quality has several advantages. Not only does it increase confidence in reports generated from the data, but it also ensures decision-making is supported by accurate information. Having systems in place to account for duplicate and unclean data automatically dramatically reduces the amount of time accounting staff needs to spend on such tasks. The amount of communication generated and transmitted internally and externally through banking networks about such incorrect data also reduces. Clean data means effective business and increased profitability for the bank and its account holders by eliminating common mistakes in data that can result in huge errors.

Data cleansing helps banks improving customer acquisition, thus increasing revenue, productivity, and enhancing account management. For banks, there are three strategies that can help boost performance:

  • Identify 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 data analysis and cleansing of data

  • Have a well-crafted guide for data governance that helps reduce cost and risk while maintaining top quality data.

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About the author

Tamil Bharathi is the Client Advisor at Zuci. He specializes in advising and offering critical customer-centric solutions to new and existing customers for the best end-user experience. He is a fun person with a great sense of humor. Check him out at Tamil Bharathi.