Financial Data Infrastructure: Optimisation of ERP Integration

Solution

 

• Develop a solution to retrieve accurate master data from legacy system
• Design a robust data transfer and validation process with an extracttransform-load (ETL) layer

 

Banking area

 

•  Group Financial Control
• Technology Enablement SKILL SETS
• Data Processing and ETL
• Business and Process Analysis
• Agile Methodology
• SQL
• SSIS Processes

 

Insights

 

• Due to the complexity and high number of downstream processes, a clear understanding of all interdependencies in the various process flows is critical to ensure all requirements are addressed
• Ensure and validate your understanding of the data requirements in order for any transformations to be applied correctly

Key skills required

 

• Technical ETL skills - manipulating and transferring data with consideration of data quality, accuracy and completeness

• Business knowledge of the Group Finance infrastructure and data architecture

Overview

 

A large retail and corporate bank approached Monocle to assist with a project aimed to optimise their financial data infrastructure. The client introduced a multi-dimensional ledger through a single platform. In order to continue to meet the data requirements for downstream processes including internal and external financial reporting, an operations data store was developed to include the new ledger data that would be enhanced by optimised master data. In order to achieve this, a standardised level of master data granularity needed to be agreed across different business entities and key stakeholders.

 

Monocle was responsible for developing an automated process to retrieve all the agreed upon master data from across the bank and ensure its data quality through an ETL layer. A validation process was then developed as part of the new data mart to ensure the completeness of the data. Finally, a reconciliation between the current and the new financial data solution was performed.

 

Monocle was able to deliver a robust automated ETL process for their standardised master data that was supported by strong data quality controls. Additionally, Monocle performed various gap assessments on downstream processes to ensure their data requirements would be met by the new solution.

 

Results 

 

• Automated the ETL process of retrieving standardised master data
• ETL process to load the data to the new data mart with validation controls
• Standardised financial data provided to the various downstream processes