Coverage of Apeejay School, Mahavir Marg alumnus in Vocal Media

From driving strategic decisions to creating personalised customer experiences, data’s influence is omnipresent. To delve deeper into this transformative power of data, Apeejay Newsroom spoke with Agastya Anand, an alumnus of Apeejay School, Mahavir Marg, Jalandhar, from the 2018 batch. Currently working as a Software Engineer and Data Analyst at Merkle DGS, Agastya shared his valuable insights on the role and future of data in our society.

Understanding data: The basics

Agastya’s straightforward definition of data is illuminating. He explains, “For a five-year-old, I would say data is anything, any kind of information that you can get regarding any topic. It might be useful or it might not be useful. That is not the job of collecting data; that is the job of processing data.” This fundamental understanding sets the stage for comprehending how raw data, often unremarkable on its own, becomes powerful through analysis and interpretation.

Data can be categorised into various types:

Structured Data: Organised in a predefined manner, often in rows and columns. Examples include

databases and spreadsheets.

Unstructured Data: Lacks a specific format or structure. Examples include emails, social media posts, and multimedia files.

Big Data: Extremely large datasets that require advanced tools and techniques to process. Used extensively in industries like finance, healthcare, and e-commerce.

Examples of Data Utilisation in Different Industries

Healthcare

Patient records and medical imaging data are analysed to improve diagnostics and personalised treatments. For instance, predictive analytics can identify patients at risk for certain conditions, enabling early intervention.

Finance

Transaction data is used to detect fraud and assess credit risk. Algorithms analyse spending patterns to flag suspicious activity, protecting both banks and customers.

Retail

Customer purchase histories help tailor marketing strategies and manage inventory. Retailers use data to predict trends, ensuring they stock the right products at the right times.

The Role of Data in Business

Using a relatable example, Agastya illustrates how businesses in Jalandhar collect and leverage customer data. “When you buy anything from local stores, they sometimes ask for your phone number. Later, you receive SMS notifications about discounts. That is how companies use your data to boost their sales.” This example underscores the practical application of data in enhancing customer engagement and driving business growth.

Detailed Case Study: Amazon’s Recommendation System

Amazon uses data collected from user interactions to power its recommendation engine. This system analyses purchasing history, browsing behaviour, and even product reviews to suggest items customers are likely to buy. By leveraging machine learning algorithms, Amazon creates a personalised shopping experience that significantly boosts sales. For instance, if a customer frequently buys gardening tools, the recommendation system might suggest related items like seeds or plant pots.

The Importance of Data Processing

Agastya emphasises that data’s utility goes beyond mere collection. The real value lies in its processing, where data is transformed into actionable insights. For businesses, this means understanding customer behaviour, optimising operations, and making informed strategic decisions. Tools like data visualisation and machine learning algorithms are crucial in this transformation process.

The Growing Demand for Data Professionals

As the world becomes increasingly data-driven, the demand for data professionals is skyrocketing. Agastya notes, “Data-related jobs are becoming pretty common. There is a lot of demand in the market for it because data is involved in everything you do, even when using platforms like Netflix or Amazon Prime. There is no algorithm without data making your life easier.”

Statistical Backing and Market Trends

According to a report by the U.S. Bureau of Labor Statistics, the employment of data scientists is projected to grow 36% from 2022 to 2032, much faster than the average for all occupations. This surge is driven by the need to process and analyse the vast amounts of data generated daily.

High-Demand Skills and Emerging Roles

Skills in high demand include proficiency in programming languages (Python, R), data visualisation tools (Tableau, Power BI), and machine learning algorithms. Emerging roles within the field include data engineers, machine learning engineers, and AI specialists.

Pathways into Data Careers

For those aspiring to enter the data field, Agastya offers practical advice. “You don’t necessarily need a degree in engineering. There should be some inclination towards the science stream, but it could be B.Sc or anything related. Many roles, like business analysts, are non-tech or semi-tech and often recruit from non-tech backgrounds.”

For aspiring data professionals, the key lies in developing strong analytical skills, staying abreast of technological advancements, and continuously honing one’s expertise. As Agastya aptly puts it, “Data is everything you do.” Embracing this reality can open doors to exciting and rewarding career paths in the ever-evolving landscape of data science and analytics.