Enquiry Form

Blogs

The Importance of Data Quality Management and Data Cleansing for Banks

Updated On : February 2016  |  by Amol S. Khanvilkar

Dramatic transformations in banking have been driven both by new regulatory requirements and the technological advancements that aid banks in meeting and exceeding such requirements. One of the largest challenges faced by banks today is managing data – both for regulatory requirements as well as to gain meaningful insights.

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 introduced to help support the needs of such organizations. These generate almost unheard of volumes of data that modern banks must manage and ensure the quality of.

The data that banks are concerned with typically contains large numbers of business transactions and records and needs to be accessed for a various number of banking functions instantly from throughout their banking networks. Couple this with the strict nature of regulations banks must adhere to with their data in comparison to other business sectors, and the true size of the need for Data Quality Management and Cleansing for Banks becomes evident.

There are quite a few significant challenges banks must be aware of, focus on, and overcome to ensure proper Data Quality Management. Perhaps the most key are:

  • The sheer volume of data
  • Securing data at all times
  • Maintaining all statutory and regulatory requirements
  • Interfacing with legacy applications securely and efficiently

So what strategies and tools are available to banks to overcome these challenges as they work to ensure regulatory compliance and proper Data Quality Management? To begin with, a firm understanding of what Data Quality is and what managing it in the Banking sector looks like.

Data Quality Management for Banks

Certainly every business and IT department must be concerned with the quality of the data it maintains. However, the traditional needs of quality management are exacerbated by the unique circumstances of the banking sector described above (volume of data, regulatory requirements, legacy systems, etc.).

Here are few more significant reasons that Data Quality is of utmost importance for banks:

  • The every evolving need for Risk Management Applications creates an even more complex network of data and puts an even greater need for the accuracy of data.
  • The explosive growth of ecommerce has led to the creation of several new sources of revenue for account holders.
  • The regulatory guidelines banks must face all over the world are continually evolve and become even stricter. What works today may not be adequate in a few short years forcing banks to be on the forefront of data quality and security efforts.

Defining Data Quality

The quality of data, in this context, can be understood as it’s suitability for meeting the needs and requirements banking institutions require of it. To be clear, data doesn’t have to be perfect, but rather, it needs to meet the requirements of whatever system utilizes it or those systems return inaccurate results. To help ascertain whether or not data is high quality, a number of specific factors are taken into account:

  • Data Integrity
  • Data Completeness
  • The Data’s Accessibility
  • The Data’s Timeliness
  • Accuracy of the Data
  • Validity of the Data
  • Integrity of the Data

There are a number of causes that lead to a loss of data quality which will be covered more thoroughly in the next section. These include duplicate records, missing data, incorrect data, and even errors created during data entry.

Cleaning Data

So how do banks manage the quality of their data over and above simply trying to keep a better watch over these issues? There are a couple of employable strategies:

  • Locate and correct inaccurate and defective elements and values like misspellings and mistyped number values.
  • Standardize data by modifying it to uniformly confirm to standards that make using and understanding it easier and more effective. This can be accomplished by matching and merging records within a file.
  • Use filtering technique to catch duplicate, nonsensical and even missing data

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

  • During an ETL
  • In a Data Warehouse
  • In a Staging Area

Where Unclean Data Comes From

Misleading, missing, duplicate, or otherwise unclean data can come from quite a number of sources. These include but are 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 systems just as it does between speakers of different languages.

Any paper documents anywhere in the data chain can easily be the source of error as they require manual input into electronic systems.

Any changes to the account holder’s information that needs to be shared across different applications and systems within the banking network. For example, if an account holder gets married but the name change is not carried over to all accounts automatically.

Often information from different places such as call centers is incomplete as operators often have to enter it in a hurry which requires them to condense or leave out details.

Any data from third-party partners or systems that has errors in it could enter automatically and be incorrect. There are constant mergers and acquisitions in the banking industry. This constantly requires reintegration of data which can lead to duplicate entries, missing entries, and even corrupted data.

The Benefits of Cleaning

Cleaning Data and managing it to maintain quality provides banking institutes a number of advantages. Not only does it increase confidence in reports generated from the data, it ensures decision making is supported by accurate information.

Additionally, having systems in place to account for duplicate and unclean data automatically dramatically reduces the amount of time accounting staff must dedicate to such tasks. Additionally the amount of communication generated and transmitted internally and externally through banking networks about such incorrect data would be eliminated as well.

Clean data means effective business and increased profitability for the bank and its account holders by eliminating common mistakes like duplicate and missing mailings that can be directly related to unclean data.

To ensure the best implementation, data cleaning solutions should be done proactively and never after a failed or bad campaign. Here are three quick steps to help ensure a successful implementation of a data quality solution:

Step 1: Hire an external IT consultant to conduct a database audit to ascertain current data quality.

Step 2: A Data Quality Solution should be implemented before or simultaneously with any other planned data management solutions like data warehousing.

Step 3: Assemble a team of analysts, IT personnel, and experts from different domains or application areas that rely upon clean data should be formed to oversee the data quality management.

A partnership with a trained and experience technology consultant firmed is highly recommended for any bank or financial institute looking to proactive implement a data quality solution.

Nelito has a tries and tested Data Quality solution which helps banks in UCIC (Unique customer Identification) programs as well as in data enrichment and various other related programs.

Leave Comments :

CLIENT SPEAK

  • The Meticulousness of efforts to ensure customer delight by each of the team member during the course of the project is appreciable. - Data Archival Project

    R. Ganesh (Senior Manager)

    Indian Overseas Bank
  • Overall we are happy with the services provided by Nelito. The implementation team is sensitive to our support requirements and does a good job.

    Vinayak Khadye (Chief Digital Officer)

    Finanzmart Services Pvt Ltd
  • We are extremely thankful to the whole team who have devotedly committed and created this excellent database. Much of our time was wasted in data retrieval from i link where we were facing lots of issues.

    You have given us an awesome product which is superior to our Finacle itself in its display capabilities. The great idea of taking pdf/word/excel option is simply superb. Merging the party code and Finacle CIF and account numbers are another feather in the cap.

    N Sampath Kumar (Branch Manager)

    Corporation Bank
  • We have successfully made Nelito's Fincraft Software for NBFC live in early March for LOS,LMS and Sanjiv Khalkho, Arijit Chakraborty with his team have been very instrumental in making this software live . I would like to appreciate on the turnaround time during the go live phase of the team in getting some of our key requirements done and helping our users to use the software for recording and disbursing all the sales from the software.

    In technology side I would like to praise Pramod Navale and Ganesh Khetmalis for in depth knowledge of the product and their technical skills is outstanding. Looking forward for the continued relationship for our next release and whole of 2018.

    Prateek P Katyal (CTO)

    WheelsEMI Pvt. Ltd
  • "We are extremely pleased with the kind of support extended by you and your team for our year end work. Although whole bunch of people worked for making the support seamless from your end, my special thanks to following resources. Anurag, Arpit, Arvind, Pranit, Suraj, Udgran, Nilesh, Leena and Udgrand. Also, thanks to all others who have supported us directly or indirectly at your end."

    Ananda Padebettu (IT Manager)

    The Nav Jeevan Co-op Bank Ltd.
  • "For a Bank of our size, this system suffices and is very cost effective. Almost all of the bank’s activities run on the one system. Nelito constitutes a partner more than a supplier and the relationship includes provision of the bank’s underlying technology as well as its Fincraft range of applications."

    Chairman

    The Nainital Bank Ltd.
  • "The best mix of functionality and technological support and our vision of “Cash Less and Paper Less” work environment for Sonata has come true with our technological partner Nelito. I highly recommend FinCraft Core Microfinance platform and especially integrations with a number of third party services."

    IT Head

    Sonata Finance Pvt. Ltd.
  • CBS

    "We are happy to use FinCraft Data Archival & Retrieval solution from Nelito Systems; it has given huge ROI in terms of saving revenue by retiring legacy applications. We get all desired reports in a timely manner from the solution."

    Assistant General Manager (CBS)

    United Bank of India
  • Custom Application Development testimonial

    "Proactive & professionally supportive, technically skilled team."

    General Manager

    Federal Bank
  • DGM

    "My bank is working with Nelito from the last about 9 Years and the services provided by the company may prolong our association."

    Sikander Gupta | CEO

    The Jammu Central Co-op Bank Ltd.
  • DGM

    "Knowledge and service provided to the bank is beyond expectation. And we would definitely engage and recommend solution to others."

    Rachelle F. Rivas | IT Project Manager (Senior Manager)

    Bank of the Philippine Islands (BPI)
  • DGM

    "As Vendor strict adherence to process & procedures. Risk Mitigates in Place. Flexible to customer requirements."

    Dinesh Negi | VP (Clg)

    DCB Bank Ltd
  • DGM

    "Everything was successfully implemented and tested. The implementation was professionally done in a very efficient and cooperative manner. Migration was very smooth."

    Frithjof Ramb | Vice President

    SBI- Frankfurt, Germany
  • DGM

    Support services provided for Swift is really up to the mark and meets all clients requirement. Kindly keep this ongoing.

    Rudolph Banis | Head - IT CBS and Application Management

    IndusInd Bank
  • DGM

    Excellent

    Navin A Bijur | Manager - IBD

    The Shamrao Vittal Coop Bank Ltd