My name is Chetan Sharma and I currently reside in Gurgaon, Haryana. Over the past five years, I have been deeply immersed in the field of data analytics, building expertise in tools such as MS-Excel, Power BI, Power Automate, and receiving certification directly from Microsoft.
Beyond the realm of data analytics, I have a passion for sports. I have had the privilege of representing the state of Haryana in national football matches, demonstrating my dedication and skills on the field. Additionally, as a personal activity, I have learned boxing over the past year and have found physical and mental enrichment through the sport.
In essence, my professional journey in data analytics is complemented by my dedication to sport, which reflects a balanced approach to both work and personal development.
Before starting my journey with Great Learning, I held a dynamic role in FIS Global Services. As a Senior Analyst on the Business Intelligence and Digital Transformation team, my primary responsibility was to create impactful solutions for our customers. In particular, I led the development of custom Power BI dashboards tailored to the diverse needs of our customers. This role not only honed my technical skills, but also improved my ability to transform data into actionable insights to drive business performance.
My decision to enroll in the Great Learning program was motivated by a deep desire to improve my career trajectory by improving my skills. After three years immersed in the corporate world, I felt the need to broaden my horizons and explore the realm of data science, recognizing that it was a natural progression from my analytics background.
What really captivated me about data science was its transformative potential: the ability to gather valuable insights from raw data and make informed decisions. This innate curiosity led me to explore different ways of learning, including certificate courses offered by esteemed institutes like IIT. But what stood out from the rest was the great learning. My decision was further strengthened by the support of a senior colleague who worked as a senior data scientist at CitiBank. Their first-hand experiences and recommendations highlighted the reliability and quality of the training provided by Great Learning.
Additionally, as I took a closer look at Great Learning's offerings, I was impressed by their carefully crafted curriculum, tailored timetables, and exposure to industry-level projects. These elements promised a holistic learning experience, bridging the gap between theory and practical application.
Perhaps most notable was Great Learning's strong employment support. Guaranteed access to employment opportunities after completion gave me confidence in my decision and made Great Learning a solid choice for my academic journey.
My experience with Great Learning was very special. This has helped me reach my full potential by providing comprehensive insight into the complexities of the data science industry. In particular, they supported me in resuming my studies five years after graduation. Throughout the program, I gained valuable knowledge about Python, the importance of statistics in making business decisions, and more.
What stood out the most was the unwavering support of the program management team, who were always ready to help, and the dedication of the mentors to clarify doubts and ensure clarity of concepts.
This program has had a profound impact on both my professional trajectory and personal growth. Professionally, this facilitated a significant upskilling journey, moving from MS-Excel and Power BI to proficiently building integrated reporting systems using Python, SQL, and Tableau. This transition allowed us to streamline the process by taking data from SQL Server, transforming it through Python, and creating dynamic dashboards tailored to business and senior management needs.
This change not only minimizes the human effort previously invested in manual report preparation, but also optimizes resource allocation within the company by automating the task. For me personally, this transition marks a milestone in my ongoing learning journey, empowering me to adapt to evolving industry needs while improving the effectiveness and efficiency of my role.
A problem area for Airtel International LLP's business segment, particularly Airtel Money, was related to the management of suspicious transactions flagged by the SAS system. It has 150 million users across 14 countries and receives approximately 15 million notifications every month. Lack of reporting on service level agreement (SLA) compliance caused serious problems. To mitigate potential revenue loss, it was important that these alerts, which had a direct impact on revenue, were resolved quickly and within an agreed upon timeline.
I leveraged my data science skills to solve SLA reporting issues for the Revenue Assurance Compliance team at Airtel International LLP. Recognizing the inefficiency of monthly reports generated in MS-Excel taking 3 to 4 days due to large amounts of data, we designed a solution. We first connected a SQL database to a Jupyter Notebook and scripted a process to generate consolidated reports for 14 countries each day, completing the task in less than 15 minutes. We then consolidated the results of these SLA reports into Tableau dashboards to provide a comprehensive view of weekly performance across all countries and analysts handling alerts. This streamlined approach not only accelerates reporting, but also improves visibility and monitoring capabilities, allowing you to proactively manage suspicious transactions and protect revenue integrity.
Implementing a new reporting system has had important results for our team:
- Time savings: Switching from monthly MS-Excel reports to daily automated reporting through Jupyter Notebook saved approximately 3-4 days of manpower. These optimizations allowed the SAS/Airtel Money team to allocate resources more efficiently to effectively meet SLA goals.
- Enhanced Performance Monitoring: Integrating SLA results into Tableau dashboards provided visibility into alert processing by analyst, a previously overlooked aspect. This allowed the business to identify agents who were struggling to handle alerts and drive targeted support and training initiatives to improve overall performance.
- Operational Efficiency: By streamlining reporting processes and enhancing performance monitoring capabilities, this initiative contributed to overall operational efficiency within the Revenue Assurance Compliance team.
Efforts are underway to further improve reporting frequency, with the goal of publishing reports in near real time in the future. These continuous improvements reflect our commitment to leveraging data science for continuous process optimization and business success.