Data Quality Management for Banks in 2021

The Importance of Data Quality Management and Data Cleansing for Banks:

Data Quality Management in the banking sector is being driven by new regulatory requirements and technological advances that are helping banks to meet those requirements. One of the biggest challenges facing banks today is managing data, both for regulatory requirements and for obtaining meaningful information.

 Businesses powered by modern technology work through different channels that generate large amounts of data to support modern banks. New technologies such as ERP, SCM, and CRM systems have been introduced to support the needs of these organizations. Generates almost unprecedented amounts of data that modern banks need to manage and ensure quality.

 The data that banks process often contains a large number of transactions and business data and must be accessed directly from your banking network via various banking functions. This goes hand in hand with the strict nature of the regulations that banks must comply with in respect of other business sectors, and the true extent of the need to clean and manage the quality of data for banks becomes clear.

There are a number of important challenges that banks need to know, focus on and overcome to ensure good quality of the data management. Perhaps the most important are:

• A large amount of data

• Data is always secure

• Application of all legal and regulatory requirements

• Work securely and efficiently with legacy applications

So what strategies and tools exist for banks to address this compliance with regulations and good challenges to quality management? To begin with, a good understanding of what quality is and what it looks like in the banking sector.

Data quality management for banks

Of course, all business and IT departments need to be concerned about the quality of the data they store. However, traditional quality management needs are exacerbated by the unique circumstances of the banking sector described above (amount of data, regulatory requirements, legacy systems, etc.).

Here are some of the key reasons why quality is more important for banks:

• The ever-changing need for risk management applications drives an even more complex data network and a greater need for data accuracy.

• The explosive growth of e-commerce has created a number of new revenue streams for account holders.

• The regulatory guidelines facing banks around the world are constantly evolving and becoming stricter. What works today may not be enough in a few years’ time, forcing banks to stay at the forefront of data quality and security efforts.

Define the quality of the data

Data quality in this context can be understood as sufficient to meet the needs and requirements of banking institutions. To be clear, the data does not have to be perfect, but it must meet the requirements of the system used, otherwise, these systems may produce inaccurate results.

To determine if the data is of high quality, a number of specific factors are taken into account:

• Data integrity

• Completeness of data

• Accessibility of data

• Data opportunities

• Accuracy of data

• Validity of the data

• Data integrity

There are several causes that lead to the loss of data quality, which will be discussed in more detail in the next section. These include duplicate records, missing data, incorrect data, and even data entry errors.

Clean data

So how do banks manage the quality of their data and try to keep these issues closer? There are several useful strategies:

• Find and correct inaccurate and defective items and values, such as misspellings and numeric values.

• Standardize data by adapting it to meet standards that make it easier and more effective to use and understand. This can be achieved by fitting and merging records in a file.

• Use filtering techniques to capture duplicate, meaningless, and even missing data

The best place to clean data is always the source system or application. If it is not available, there are other options:

• During an ETL

• In a data warehouse

• In a scene

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