From Data Analytics to Real-Time Reporting: My Professional Journey

My name is Chetan Sharma, and I’m currently based in Gurgaon, Haryana. Over the past five years, I’ve been deeply immersed in the field of Data Analytics, where I’ve gained expertise in tools like MS-Excel, Power BI, and Power Automate, earning certifications directly from Microsoft.

Beyond the realm of data analysis, I have a passion for sports. I’ve had the privilege of representing Haryana State in National Football games, showcasing my dedication and skills on the field. Additionally, as a personal pursuit, I’ve been learning boxing for the past year, finding both physical and mental enrichment in the sport.

In essence, my professional journey in data analytics is complemented by my commitment to sports, reflecting a well-rounded approach to both work and personal development. 

Before embarking on my journey with Great Learning, I was entrenched in a dynamic role at FIS Global Services. As a Senior Analyst within the Business Intelligence and Digital Transformation team, my primary responsibility revolved around crafting impactful solutions for our clientele. Specifically, I spearheaded the development of bespoke Power BI dashboards tailored to the diverse needs of our clients. This role not only honed my technical prowess but also sharpened my ability to translate data into actionable insights, driving business outcomes.

My decision to enroll in the Great Learning program stemmed from a deep-seated desire to elevate my career trajectory through upskilling. After three years of immersion in the corporate world, I felt compelled to expand my horizons and delve into the realm of Data Science, recognizing it as the natural progression from my background in analytics.

What truly captivated me about Data Science was its transformative potential – the ability to glean valuable insights and make informed decisions from raw data. This innate curiosity led me to explore various avenues for learning, including certificate courses offered by esteemed institutions like IITs. However, it was Great Learning that stood out among the rest. The endorsement from a senior colleague, who served as a Lead Data Scientist at CitiBank, further solidified my decision. Their firsthand experience and recommendation underscored the credibility and quality of education imparted by Great Learning.

Moreover, upon delving into Great Learning’s offerings, I was impressed by the meticulously 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 compelling was Great Learning’s robust career assistance. The assurance of access to career opportunities post-completion instilled confidence in my decision, making Great Learning the obvious choice for my academic journey.

My experience with Great Learning has been exceptional. They’ve enabled me to tap into my full potential by providing comprehensive insights into the intricacies of the data science industry. Notably, they supported me in resuming my studies after a five-year hiatus post-graduation. Throughout the program, I gained valuable knowledge in Python, the significance of statistics in driving business decisions, and much more.

What stood out the most was the unwavering support from the program management team, always ready to assist, and the dedication of mentors in clarifying doubts and ensuring conceptual clarity.

The program has profoundly impacted both my professional trajectory and personal growth. Professionally, it facilitated a significant upskilling journey, transitioning me from MS-Excel and Power BI to proficiently building integrated reporting systems using Python, SQL, and Tableau. This transition has allowed me to streamline processes by fetching data from SQL Server, transforming it through Python, and creating dynamic dashboards tailored for business and upper management needs.

This shift not only minimizes human effort previously invested in manual report preparation but also automates tasks, optimizing resource allocation within the company. Personally, this transition signifies a milestone in my continuous learning journey, empowering me to adapt to evolving industry demands while enhancing efficiency and effectiveness in my role.

The problem area in our business vertical at Airtel International LLP, particularly in the Airtel Money segment, revolved around the management of suspicious transactions flagged by SAS systems. With 150 million users spread across 14 countries, we encountered approximately 15 million alerts monthly. The absence of reporting on Service Level Agreement (SLA) adherence posed a significant challenge. It was crucial to ensure that these alerts, directly impacting revenue, were promptly addressed within the agreed timelines to mitigate potential revenue loss.

To address the SLA reporting challenge in the Revenue Assurance Compliance team at Airtel International LLP, I leveraged Data Science techniques. Recognizing the inefficiency of monthly reports produced in MS-Excel, which consumed 3-4 days due to large data volumes, I devised a solution. Firstly, I connected the SQL Database to a Jupyter Notebook and scripted a process to generate consolidated reports for all 14 countries daily, completing the task within 15 minutes. Subsequently, I integrated the outcomes of these SLA reports into a Tableau Dashboard, offering a comprehensive view of weekly performance across all countries and the analysts handling the alerts. This streamlined approach not only accelerated reporting but also enhanced visibility and monitoring capabilities, enabling proactive management of suspicious transactions and safeguarding revenue integrity.

The implementation of the new reporting system yielded significant outcomes for our team:

1. Time Savings: The transition from monthly MS-Excel reports to daily automated reporting via Jupyter Notebook resulted in approximately 3-4 days of saved human labor. This optimization allowed the SAS/Airtel Money team to allocate resources more efficiently, enabling them to meet SLA targets effectively.
2. Enhanced Performance Monitoring: The integration of SLA outcomes into the Tableau Dashboard provided visibility into analyst-wise alert handling, a previously overlooked aspect. This addition empowered the business to identify agents facing challenges in handling alerts, facilitating targeted support and training initiatives to improve overall performance.
3. Operational Efficiency: By streamlining reporting processes and enhancing performance monitoring capabilities, the initiative contributed to overall operational efficiency within the Revenue Assurance Compliance team.

Moving forward, efforts are underway to further improve reporting frequency, aiming to publish reports on a near real-time basis. This continuous enhancement reflects our commitment to leveraging Data Science for ongoing process optimization and business success.