Modern policymakers realize that a country’s economy cannot wholly rely on tangible resources for rapid growth; data should be the real driver. In fact, it is data that helps businesses and statesmen envision future needs and aptly exploit the available resources to meet current and future demands. According to Peter Sondergaard, SVP and Global Head of Research at Gartner, “Information is the oil of the 21st century, and analytics is the combustion engine.”
It’s no more surprising that the big data market will surge to $229.4 billion by 2025. The industry is growing by leaps and bounds, and even the insiders are sceptical about the impact it will create on the world economy in the next 50 years.
Data science, a field rooted in statistics, computer science, and mathematics, emerged in the 1960s and 1970s when computer science and statistics began to intersect. Early data scientists used mainframe computers for statistical analysis, introducing data-driven decision-making. Databases allowed for systematic data storage and retrieval, enabling efficient work with structured data. Machine Learning (ML), a cornerstone of modern data science, originated in the 1950s and 1960s, with researchers like Arthur Samuel and Frank Rosenblatt laying the groundwork for Artificial Intelligence (AI) and machine learning algorithms. Then, in the 1980s, data warehousing became more prevalent, enabling organizations to consolidate and store structured data for analysis.
The convergence of these factors and the increasing need for extracting actionable insights from data across industries led to the formalization and rise of data science as a distinct field, encompassing data collection, preparation, analysis, visualization, and interpretation to make informed decisions and predictions.
In 1962, statistician John Tukey introduced the concept of data analysis, which he later called “the science of dealing with data.” The term “data science” was introduced by Danish computer science pioneer Peter Naur in the 1960s. Today, the world generates a massive amount of data due to the advent of computers and the Internet, and the COVID-19 crisis has increased the demand for data science. People are bound to big data and use it to create effective strategies in various fields, such as business, industry, sports, healthcare, elections, and national policymaking.
Today, data is ubiquitous and is invading all industrial fronts. Finance companies have a long history of making data useful, leading to a data-driven culture. Pharmaceutical and healthcare sectors are now extending their capabilities to new data types, such as unstructured data like text, where data can help reduce exploration costs. Media also heavily relies on data for understanding audiences, helping them find content they enjoy, engaging with it, and optimizing sharing across different platforms. The data is distributed across various industries, and the same mathematics and techniques enable data applications in various fields.
In the present job market, data science has become a distinct domain, offering a new approach to problem formulation, analysis, visualization, communication, and decision-making. A new professional has emerged: the data scientist, who combines the skills of software programmers, statisticians, and artists. In light of this growing demand, the rise of data science programmes at prestigious universities and institutes is an expanding phenomenon. Academicians and curricula experts worldwide are researching rigorously to develop academic and skill-building programmes to address both demands and challenges of the future. They emphasize developing courses where social and ethical concerns should be prioritized besides accomplishing economic benefits.
Data science, a discipline that emerged to solve specific problems, is set to evolve to cater to researchers and engineers with the rise of generative AI tools. As artificial intelligence and machine learning continue to advance, data science will rely on these technologies for deeper insights, automation, and accurate predictions. As data volumes grow, robust data governance frameworks and effective data management strategies will be crucial for organizations to derive value from their data assets.
The integration of natural language processing and advanced analytics techniques will enable users to interact with data more intuitively, facilitating broader access to insights. Ethical considerations and responsible data use will be crucial, with a heightened focus on fairness, transparency, and accountability. Data scientists must navigate stringent privacy regulations and implement robust security measures to protect sensitive information.
On the whole, in the future, there will be a greater emphasis on developing models that not only make accurate predictions but also provide explanations for their decisions, fostering trust and understanding of AI-driven outcomes.
(The author is Mr. Pravesh Dudani, Chancellor & Founder, Medhavi Skill University, and the views expressed in this article are his own)