The internet is a massive repository of unstructured data comprising millions of images, videos, and textual data. Raconteur, a content platform agency for businesses, estimate that an astounding 500 million tweets, 294 billion emails, 4 petabytes of Facebook content, 65 billion WhatsApp messages and 5 billion searches are performed every single day.
According to the firm’s figures, 463 exabytes of data would be generated globally every day in the next four years. Simply put, if one gigabyte is the size of the earth, an exabyte would resemble the sun. Uncountable and colossal as it seems, unstructured data can be analyzed to build predictive models that can offer a glimpse of future events or help arrive at strategic decision-making. As in the words of American statistician W. Edwards Deming, “In God we trust, all others must bring data.”
Data scientists use machine learning, statistics, and programming to extract information by capitalizing on databases. Apart from processing and analysing the history of raw data, data scientists look for hidden patterns using complex algorithms to extract traces of predictive elements. This is what makes data science a cost-effective solution for businesses to spot trends, forecast new market movements and discover transforming consumer behaviour.
With voluminous amount of commercialized data extracted from users all over the world, data scientists are using composite data structures, such as those involving a multitude of languages, to innovate business models while also solving issues in logistics and supply chain management. It draws specific data using Internet of Things (IoT) such as smart cars and wearables to gather insights in huge scale so that companies can cater to consumer preferences in real time.
A 2020 report by ResearchAndMarkets.com shows the global data science platform market to reach $165.5 billion by 2026. Some of the tools used for managing data sets include Apache Hadoop, Hive, SAP HANA, MongoDB, Neo4j etc. Most of these tools are available On Premise and the Cloud making online analysis an easier option, equipped with the power of ubiquity.
Software programming used by data scientists is designed to accommodate different kinds of data architecture apart from being available in open source and commercial versions. R (and RStudio), Python are the most popular choices allowing for data manipulation through deep, analytical methods.
R’s statistical language makes it a preferred financial and analytical tool, typical for large enterprises like Google, LinkedIn, and Facebook. With its simple programming, Python topped software code quality tracker TIOBE’s ‘Top 3 most popular languages’ in 2020, making it the most preferred tool among 55% of early-career data scientists according to executive recruiting firm Burtch Works.
Ascending influence of data science has made it one of the major gamechangers in the healthcare sector where data from medical devices have made far-reaching impact. Machine learning algorithms that predicting anomalies and alert doctors of stroke and rising stress levels have helped patients get timely assistance before emergencies.
IBM’s Digital Health Pass developed by its Watson Works enables users to transfer health credentials data for verification by organisations and at public events. Qualcomm’s low-sensor devices also offer greater accuracy by tracking fitness and kids’ safety through their futuristic wearables. Apple’s apps built through its CareKit and ResearchKit open-source platforms enable patients to track recovery progress and conduct research among select demographics respectively.
Data Science applications have wide prevalence in the banking and financial sectors where it is used to for risk analysis, fraud detection and personalization of investment products. AI-powered financial assistants have proven to generate high user satisfaction levels, which plays a huge role in customer retention and purchase behaviour.
In retail, data science has made huge strides in predicting customer preferences by optimizing price, sensing purchasing behaviour, and as in the case of IKEA using augmented reality to allow buyers to scan products from their catalogue on their app and virtually place the furniture in their living rooms. Applications in data science pervade the entertainment, media, logistics, governance, manufacturing sectors too. In social media, data science drives trend prediction and response waves.
Analytics Insight, a global AI, Big Data and Analytics publication, reports that the number of analytics jobs in India multiplied between April 2016 and 2017. The report also pinpoints that the demand for data scientists in 2020 would grow by 28% as indicated by IBM. In another study on Indian analytics, AIM Research and AnalytixLabs reveal that India earned consolidated revenues of $35.9 billion as of March 2020, indicating a 19.5% growth in revenue over the previous year.
Data Science for India (DSI), a global organisation founded by a team of inter-disciplinary students at UC Berkeley, works to empower grassroots data science learning in several schools in India by connecting students to resources and mentors in the discipline. The Interdisciplinary Cyber Physical Systems (ICPS) Division under the Department of Science & Technology has also started a Data Science Research Initiative to further the country’s research scope in analytics.
