Workflow Automation of MIS Generation in Financial Industry


Master of Science in Data Science


Department of Computer Science

Faculty/ School

School of Mathematics and Computer Science (SMCS)

Date of Submission

Fall 2023


Dr. Muhammad Saeed, Visiting Faculty, Department of Computer Science, Institute of Business Administration (IBA), Karachi


ETL, SSIS, MIS, Dashboard


This Master's project presents an innovative approach to enhance the efficiency of Management Information Systems (MIS) and Analytics departments by automating the process of MIS generation within the risk team at HBL. The project leverages advanced technologies and tools to streamline data extraction, transformation, and visualization, thereby significantly reducing manual efforts and minimizing errors.

The automation process is initiated through a robust task scheduler, ensuring timely and scheduled retrieval of data from IT. Subsequently, a precisely designed SQL Server Integration Services (SSIS) package is employed to execute a comprehensive Extract, Transform, Load (ETL) process. This process is tailored to handle complex data transformations, cleansing, and integration, ensuring the delivery of accurate and standardized datasets.

The core of this project lies in the implementation of a newly developed dashboard on Power BI, providing an intuitive and visually appealing interface for data representation. The Power BI dashboard serves as a dynamic and interactive platform for stakeholders to gain insights, track key performance indicators, and make informed decisions based on data.

Furthermore, the project optimizes the use of Microsoft Excel reports, ensuring that they are not only seamlessly integrated into the MIS generation process but also enhanced for better analysis. The optimization includes the incorporation of advanced Excel functionalities, resulting in improved clarity and accessibility of information.

The successful implementation of this automated MIS generation process demonstrates a substantial reduction in manual labor, increased accuracy in reporting, and enhanced accessibility to critical information for decision-makers. This project contributes to the advancement of data-driven decision-making processes within the MIS and Analytics domain, showcasing the potential for increased operational efficiency and strategic insights through automation.

Document Type

Restricted Access

Submission Type

Research Project


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