IIM B UG (B.Sc-Hons.)- Sample Paper 2026 · English Comprehension · Question 7

RC passage 2: Data-Driven Analyses in Crises — this question belongs to a set. Read the full set with all 4 questions →

Read the passage below and answer the questions that follow. Choose the best answer to each question.

As crises continue to erupt worldwide, the ability to address their adverse effects remains elusive. While the jury is still out on best practices for anticipating and responding to crises, the growing salience of what has been dubbed as ‘evidence-based policymaking’ has led to an increasing demand for data-driven analyses (DDAs). This is arguably because statistics are usually deemed apt at capturing, and processing, the high-frequency nature of crises. DDAs can provide a digestible way of measuring the temporal evolution and spatial distribution of crisis drivers like social unrest, macroeconomic imbalances, armed violence, food insecurity and population displacement. Through graphical representations, numerical data can convey otherwise complex information that usually requires pages of text to summarize. A visual can sometimes be worth a thousand words. But while the quantitative approach has its notable advantages, data is often taken at face value, usually creating the illusion that statistics are unquestionably reliable. Because of the hazardous and even politicized method of collecting and disseminating information across crisis-affected locations, data must always be consumed with caution. Indeed, data is not impervious to errors, bias, misrepresentation, or manipulation. When dealing with issues that require contextual knowledge, data can only tell us so much. For this reason, DDAs should be envisaged as complementary tools to qualitative insights. This is particularly true in crises, where widespread disruption can cause much confusion due to the multiplicity of events taking place simultaneously, some of which cannot necessarily be quantified. When analyzing different datasets, careful consideration must however be paid to the caveats that data providers tend to identify, notably regarding the process of collecting and coding quantifiable information on crisis events. This is especially important when the sourcing of the information is incomplete, contested or even manipulated. In turn, poor data limits the potential to develop more ‘sophisticated’ forms of DDAs, notably the ones that are growing in popularity and that aim to make use of Artificial Intelligence. But so long as crisis data remains largely small in sample size, DDAs in this realm will remain limited as large samples are required for statistical validity.