By Prof. Smitha Rao Program Chair, B.Tech (Hons) – Computer Science an Engineering (Data Science), B.Sc. (Hons) – Decision Sciences Professor of Data Science, School of Computational and Data Sciences, Vidyashilp University
From its inception in mid-2008, data science has emerged as a significant domain of interest for top IT recruiting companies. The importance of data has increased significantly in the digital era, making proficiency in data science one of the most in-demand skills in the industry. The field of data science involves the use of statistical, computational, and analytical methods to extract valuable insights from large amounts of data. In recent years, there has been a surge in the demand for data scientists, as companies seek to gain a competitive edge by leveraging data-driven decision-making. In this article, we will discuss the top five reasons why skilled data scientists are gaining the attention of top IT recruiting companies.
The Indispensability of Data-Driven Decision-Making
Organisations are looking for ways to use data to understand customer behavior, market trends and other business metrics. As per a survey report published by Sigma, 63% of companies cannot gather insights from organisational data. Data-driven decision-making is the approach that uses insights from data to make smarter and more informed decisions that drive business success. Data science holds the key to unlocking those insights, through sophisticated algorithms that turn raw data into actionable insights. It can help businesses optimize their operations, improve their bottom line and create a competitive advantage.
The application of data science in business has revolutionised the way companies approach decision-making, as it allows them to make data-driven decisions that lead to a positive return on investment. Data scientists are needed to develop and employ models to help companies make better data-driven decisions.
The Surge in Big Data
The amount of data generated globally has been increasing exponentially over the years due to the deluge of data from various sources such as social media, IoT devices, and other digital platforms. Data science stats in 2022 estimated that 149 zettabytes of data would be copied, captured and curated by 2024. This is huge compared to the 2 zettabytes created back in 2010.
The data generated have become so massive that traditional data processing methods cannot handle it. This has led to the development of new technologies and tools to handle and analyze massive amounts of data while creating a demand for professionals who can manage and handle these large datasets. Data scientists possess the necessary skills to collect, clean, and analyze data, as well as to develop predictive models and algorithms that can help organisations make informed choices based on their data.
The Rise of Artificial Intelligence and Machine Learning
Artificial Intelligence (AI) and Machine Learning (ML) are two of the fastest-growing areas of technology. AI refers to the development of computer systems that can perform tasks, which would typically require human intelligence, such as recognizing speech or making decisions based on data. Machine learning, on the other hand, is a subset of AI that involves the development of algorithms that can learn from data and improve their performance over time. These technologies enable companies to build intelligent systems that can learn from data and make predictions. The demand for machine learning and AI skills is hence on the rise and companies are looking for professionals who can build and deploy machine learning models to automate tasks, improve customer experiences and optimize operations.
Leveraging Predictive Analytics
Predictive analytics is the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. This can help organisations anticipate future trends and identify potential risks to gain insights into customer behavior, market trends, and business performance. According to a survey on data monetisation, shared by McKinsey, about 47% of survey respondents stated that data science has helped them achieve a competitive advantage as data analytics has reshaped the competition in their sector. Predictive analytics can help companies optimize their operations, improve their decision-making processes and identify new opportunities for growth. Professionals with skills in predictive analytics are valuable assets to companies in a wide range of industries, including finance, healthcare, and e-commerce.
The Need for Real-Time Data Analysis
In today’s fast-paced business environment, the ability to make decisions quickly is critical. Real-time data analysis is the process of analyzing data as it is generated in real-time, allowing organisations to make informed decisions quickly. Data science plays a crucial role in real-time data analysis. Machine learning techniques, such as Natural Language Processing, enable organisations to extract valuable insights from real-time data streams. Hence skills in this domain help in developing real-time data analysis systems, which can enable companies to quickly identify trends and patterns in their data and respond to changes in their business environment.
In addition to the reasons mentioned above, skills in data science also cater to various other
requirements for recruiting companies such as proficiency in developing effective and impactful Data Visualisations to communicate complex data insights in a clear and concise manner, Identifying Cost-Effective Solutions by developing algorithms that optimize processes, Improving Customer Experience by analyzing customer data to understand customer behavior, and preferences, etc. Organisations that invest in developing their data science capabilities are likely to gain a competitive advantage by harnessing more informed data-driven decisions.
Though big data and data science are the buzzwords at present, according to data science
statistics, 60% of companies still find it hard to recruit skilled data scientists due to a severe
talent shortage. This goes to show that the demand for skills in data science has been growing steadily over the years and is expected to continue in the years to come.