Need of reinventing planning and analysis processes in view of colossal data and novel technology.

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Projections, theories, conclusions and research methodologies being put together, shaped over years, formulated over months, are susceptible of getting outdated even before getting published and usually starts losing significance by the time actually put into practice. Even moment after arriving at a conclusion, outcomes are getting overshadowed and gets diluted considering unavoidable quantum of intellectual insights, discussions, counterarguments and constructive criticism on subject which follows on multiple public forums, not to speak of colossal new raw data surfaced post publication which was never part of base data and assumptions in the first place and poised to lead outcome to a different conclusion. Even scientific theories, concepts and methods change over time or sometimes radically on a new discovery.

We need to acknowledge that data being an amorphous, colossal and dynamic entity and a foundational attribute as a common denominator across knowledge streams has unprecedented and overwhelming potential to disrupt edifice of accumulated knowledge. To give a perspective and to substantiate thoughts its noteworthy that as per Forbes 2018 report there are 2.5 quintillion bytes of data created each day and 90 percent of data was generated over just two years. Its nearly impossible for any individual or expert to process such flux of data in a limited time with exiting widespread tech-tools, where any random piece of newly generated data may be vital and not to be overlooked. We also need to acknowledge that most of existing workforce around the world is neither equipped to handle such amount of data nor skilled enough today to use exascale computing system which may arrive anytime soon. It’s difficult to come to terms to the fact that a substantial quantum of unfiltered near real-time data can supersede a legitimate factual outdated piece of information. There is also an urgent need to recognise the upcoming role of machine in data processing and experts should increasingly focus more on human aspects of planning and policy.

Learning, for individuals as well as community, is an enlightening journey from one conclusion followed by disruption leading to another conclusion and so on, that’s how we evolve, hence policies, governance and planning processes have to take cognizance of the ever emerging information and data set, quickly and constantly adapting to the new data inferences and insights and should mimic the dynamic attributes based on which they are drafted and implemented.

It also leads to the realisation that traditional predefined data sets and tools to analyse and interpret data, including popular methodology of research and planning are somewhat overrated and calls for a new renaissance.

Author- Anoop Jha