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

My title is Chetan Sharma, and I’m presently based in Gurgaon, Haryana. Over the earlier 5 years, I’ve been deeply immersed inside the topic of Data Analytics, the place I’ve gained expertise in devices like MS-Excel, Vitality BI, and Vitality Automate, incomes certifications immediately from Microsoft.

Previous the realm of information analysis, I’ve a passion for sports activities actions. I’ve had the privilege of representing Haryana State in Nationwide Soccer video video games, showcasing my dedication and skills on the sector. Furthermore, as a non-public pursuit, I’ve been learning boxing for the earlier 12 months, discovering every bodily and psychological enrichment inside the sport.

In essence, my expert journey in data analytics is complemented by my dedication to sports activities actions, reflecting a well-rounded technique to every work and personal progress. 

Sooner than embarking on my journey with Great Learning, I was entrenched in a dynamic perform at FIS Worldwide Suppliers. As a Senior Analyst contained in the Enterprise Intelligence and Digital Transformation workforce, my predominant responsibility revolved spherical crafting impactful choices for our clientele. Notably, I spearheaded the occasion of bespoke Vitality BI dashboards tailored to the various desires of our buyers. This perform not solely honed my technical prowess however moreover sharpened my capability to translate data into actionable insights, driving enterprise outcomes.

My decision to enroll inside the Good Learning program stemmed from a deep-seated want to boost my occupation trajectory by way of upskilling. After three years of immersion inside the firm world, I felt compelled to broaden my horizons and delve into the realm of Info Science, recognizing it as a result of the pure improvement from my background in analytics.

What actually captivated me about Info Science was its transformative potential – the flexibleness to glean worthwhile insights and make educated selections from raw data. This innate curiosity led me to find quite a few avenues for learning, along with certificates applications offered by esteemed institutions like IITs. Nonetheless, it was Good Learning that stood out among the many many rest. The endorsement from a senior colleague, who served as a Lead Info Scientist at CitiBank, further solidified my decision. Their firsthand experience and recommendation underscored the credibility and top quality of coaching imparted by Good Learning.

Moreover, upon delving into Good Learning’s selections, I was impressed by the meticulously crafted curriculum, tailored timetables, and publicity to industry-level duties. These elements promised a holistic learning experience, bridging the outlet between concept and wise utility.

Perhaps most compelling was Good Learning’s sturdy occupation assist. The reassurance of entry to occupation options post-completion instilled confidence in my decision, making Good Learning the plain choice for my tutorial journey.

My experience with Good Learning has been distinctive. They’ve enabled me to faucet into my full potential by providing full insights into the intricacies of the data science {{industry}}. Notably, they supported me in resuming my analysis after a five-year hiatus post-graduation. All by this method, I gained worthwhile information in Python, the significance of statistics in driving enterprise selections, and moderately extra.

What stood out most likely probably the most was the unwavering assist from this method administration workforce, on a regular basis ready to assist, and the dedication of mentors in clarifying doubts and ensuring conceptual readability.

This technique has profoundly impacted every my expert trajectory and personal progress. Professionally, it facilitated a giant upskilling journey, transitioning me from MS-Excel and Vitality BI to proficiently developing built-in reporting strategies using Python, SQL, and Tableau. This transition has allowed me to streamline processes by fetching data from SQL Server, transforming it by way of Python, and creating dynamic dashboards tailored for enterprise and better administration desires.

This shift not solely minimizes human effort beforehand invested in handbook report preparation however moreover automates duties, optimizing helpful useful resource allocation contained in the agency. Personally, this transition signifies a milestone in my regular learning journey, empowering me to adapt to evolving {{industry}} requires whereas enhancing effectivity and effectiveness in my perform.

The difficulty house in our enterprise vertical at Airtel Worldwide LLP, considerably inside the Airtel Money part, revolved throughout the administration of suspicious transactions flagged by SAS strategies. With 150 million clients unfold all through 14 worldwide places, we encountered roughly 15 million alerts month-to-month. The absence of reporting on Service Diploma Settlement (SLA) adherence posed a giant drawback. It was important to guarantee that these alerts, immediately impacting revenue, had been promptly addressed contained in the agreed timelines to mitigate potential revenue loss.

To deal with the SLA reporting drawback inside the Earnings Assurance Compliance workforce at Airtel Worldwide LLP, I leveraged Info Science methods. Recognizing the inefficiency of month-to-month research produced in MS-Excel, which consumed 3-4 days because of large data volumes, I devised a solution. Firstly, I linked the SQL Database to a Jupyter Pocket e-book and scripted a course of to generate consolidated research for all 14 worldwide places every day, ending the responsibility inside quarter-hour. Subsequently, I built-in the outcomes of these SLA research proper right into a Tableau Dashboard, offering an entire view of weekly effectivity all through all worldwide places and the analysts coping with the alerts. This streamlined technique not solely accelerated reporting however moreover enhanced visibility and monitoring capabilities, enabling proactive administration of suspicious transactions and safeguarding revenue integrity.

The implementation of the model new reporting system yielded vital outcomes for our workforce:

1. Time Monetary financial savings: The transition from month-to-month MS-Excel research to every day automated reporting by the use of Jupyter Pocket e-book resulted in roughly 3-4 days of saved human labor. This optimization allowed the SAS/Airtel Money workforce to allocate sources additional successfully, enabling them to fulfill SLA targets efficiently.
2. Enhanced Effectivity Monitoring: The blending of SLA outcomes into the Tableau Dashboard supplied visibility into analyst-wise alert coping with, a beforehand ignored facet. This addition empowered the enterprise to ascertain brokers going by challenges in coping with alerts, facilitating centered assist and training initiatives to reinforce normal effectivity.
3. Operational Effectivity: By streamlining reporting processes and enhancing effectivity monitoring capabilities, the initiative contributed to normal operational effectivity contained in the Earnings Assurance Compliance workforce.

Shifting forward, efforts are underway to further improve reporting frequency, aiming to publish research on a near real-time basis. This regular enhancement shows our dedication to leveraging Info Science for ongoing course of optimization and enterprise success.