Role of data science in government sector -

The massive proliferation of digital technology has resulted in generation of huge amount of digital data and growing immensely – from web data to e-commerce to fintech to stock markets to scientific research to governance to mobile devices to social media to IoT devices; the list is almost endless. All these have suddenly led to data explosion. International Data Corporation (IDC) predicts that the amount of digital data generated will be 175 Zeta Bytes (equivalent to 1021) by 2025. Due to the tremendous volume of digital data being generated and at such a rapid pace, such information cannot be easily interpreted by an individual but instead has to be relied on machines to interpret and process it. Therefore, it is pertinent to be able to extract relevant insights from the data which can be leveraged as a competitive advantage. The term data science was originally coined by a Danish astronomer and computer scientist Peter Naur. He was an academic and called it the “science of data” to refer to computer science.

Data Science Lifecycle

Data science is an interdisciplinary field and combines a spectrum of techniques, processes, and algorithms. A team working on data science would generally comprise mathematicians, statisticians, scientists, developers, systems engineers, and domain experts. Typically, data scientists adopt the following process:

(i) Capture : Gather raw structured and unstructured data from relevant sources
(ii) Prepare and Maintain : Cleaning, deduplicating, organizing, transforming, integrating the data
(iii)Process : Examine patterns & determine the data’s suitability for analytics
(iv)Analyse : Perform statistical analysis, predictive analytics, regression, learning algorithms.

Data Science in Government

Data Science can find ample application in government. It can help in extracting useful information and knowledge from large volumes of data in order to improve government decision-making or providing the insights to make data-driven decisions, making use of predictive causal analytics, prescriptive analytics, and machine learning. The government can harness the data to address implementation gaps, detect overlaps and target the right beneficiaries and feed into smart policy making, for instance, through improved predictive analytics.

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