CASE STUDY

How we enabled a leading bank’s data transformation with data consolidation and standardization? 

Sep 28, 2022

Highlights of the Case Study: 

Particulars Description 
Client A leading bank based in Mexico partnered with Quantzig to improve data quality and implement data consolidation and standardization. 
Business Challenge The client wanted to transform its data collection process to make it suitable for data analytics. It wanted to leverage its vast repository of customer data for business insights.   
Impact Quantzig helped the client execute a data strategy, which involved investing in the right platforms to lay a future-proof data foundation while maintaining agility and flexibility.  

Game-Changing Solutions for the Banking Industry   

First, let’s understand the reason for the increasing role of data consolidation services in the banking industry. Many players in the financial services sector are now in charge of vast quantities of extremely sensitive consumer data due to the industry’s rapid digital transformation. As the threat of data theft expands in size and sophistication and customers become more conscious of data privacy, managing this data can be difficult. The typical consumer is growing more informed about how businesses gather, store, and share their data. As a result, transparency is swiftly gaining popularity in the financial services industry as companies strive to build a rapport with and inspire confidence in their clients. Maintaining openness in data management can also assist organizations in showing that precautions are being taken to protect sensitive data and assets. 

Organizations can maintain total visibility throughout their whole IT ecosystem with the help of Quantzig’s analytic software, making it easier to gather, recognize, and take action in response to any potential cyber threats that may aim to steal financial services data. This analytical tool is frequently employed in data warehousing, where users must obtain historical and current data to build the data warehouse. Data warehouses combine transactional and historical data, often from various heterogeneous systems. Consequently, the tool ensures that this data is appropriately extracted, converted, and put into the data warehouse. 

The Challenges of the Client 

Our client is a leading bank based in Mexico which has made significant investments in gathering and storing customer data. However, the client was facing several issues concerning data accuracy and maintenance. This included a lack of front-office controls that resulted in unsatisfactory data input and limited validation, an inefficient data architecture with numerous legacy IT systems, a lack of business support appreciating the value of data transformation, and an absence of executive-level attention to IT issues. 

The client approached Quantzig to leverage its expertise in offering a comprehensive data-driven analytics framework for data consolidation and standardization. Our client wanted to extract, transform, and load all data into the target system. Therefore, Quantzig’s analytics intended to focus on data transformation that involves deduplication, cleaning, joining, and encrypting the data to ensure suitability for data analytics. Quantzig’s data analytics solution helped the client customize the transformation processes based on their reporting and analytics requirements.  

Through Quantzig’s data consolidation services, the banking client wanted to develop a framework for data standardization.

Quantzig’s Data Consolidation Services for Banks 

Quantzig’s experts deployed a cutting-edge big data consolidation services solution. This solution helped the client execute a data strategy, which involved investing in the right platforms to lay a future-proof data foundation while maintaining agility and flexibility in its operations. Our analysts assisted the client in achieving data transformation in the following manner. 

  • Defining a clear data strategy 
  • Translating the data strategy into tangible use cases 
  • Designing innovative data architecture to support the use cases 
  • Setting up robust data governance to ensure data quality 
Customer Analytics Solutions Churn
  • Defining a clear data strategy 100% 100%
  • Translating the data strategy into tangible use cases 100% 100%
  • Designing innovative data architecture to support the use cases 100% 100%
  • Setting up robust data governance to ensure data quality 100% 100%

Impact Analysis of Quantzig’s Data Consolidation Service in Banking  

The solution offered by Quantzig helped the client reap more significant benefits from a well-segmented and thoroughly analyzed customer database. In addition, our data analytics solution enabled the client to collect large amounts of data, cleanse it, and standardize using automated programs. Quantzig’s analytical solutions helped correct standard errors and helped the client harmonize data. Accurate, cleansed, standardized, and consistent data provided numerous advantages to the client. Our intervention helped the client implement robust systems for better data management which, in turn, helped the client improve productivity and increase customer acquisition. This led to better decision-making based on data-backed insights, leading to business growth and increased profits.  

Key Outcomes 

Quantzig analysts used an analytical solution for the extraction process involving the transferring or exporting of raw data from various databases or data lakes to a staging area. Through multiple filtrations and validation, data quality is ensured. Quantzig also assisted the client in accelerating data consolidation through local and cloud-based analytical tools. Implementing these tools helped automate the process and began replicating data within minutes.  

The standardized and consistent data provided significant advantages to the client regarding cost reduction, improved productivity, improved customer acquisition, and better decision-making.

Broad Perspective on Data Analytics in the Banking Sector 

Over the years, the rapid digitization of the banking sector has created a vast repository of customer data that needs to be secured and maintained. This data consists of sensitive customer information that is susceptible to misuse and needs to be managed appropriately. This data also holds the key to the future growth of the banking sector. Over two-thirds of decision-makers anticipate AI will have a beneficial influence on the banking sector. By utilizing these cutting-edge capabilities, businesses can automate processes more efficiently, expedite data management tasks, and obtain helpful information to enhance workflow and close security holes. 

Quantzig’s data analysis solutions enable clients to develop strategies to enhance and retain customers, thereby significantly lowering costs associated with onboarding new customers. Quantzig’s data analytics solutions based on technologies like artificial intelligence (AI) and machine learning (ML) are gaining traction with many businesses.  

Key Takeaways 

  • Data standardization  
  • Cost reduction 
  • Improved productivity 
  • Improved customer acquisition 
  • Better decision making 

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