The capacity to collect and analyze massive amounts of data in analyzing the response and prediction about future events has also transformed the businesses, policy making and all types of sciences, along with its implication at each sector of economy. In the presence of escalating data science, the twentieth century has been controversial in highlighting the global environmental issues with wide concerns about sudden observed change in climate relevant variables. The Big Data science has been imperative in solving these major concerns through volume, variety, velocity and veracity in gaining data from various sources. Various global initiatives that already sought to catalyze the interest of the climate change field have been documented that includes, United Nation’s Global Pulse Big Data Climate Challenge on climate resilience - A global call for data solutions to address increasingly pressing issues surrounding climate resilience in specific sectors, e.g. food Security, nutrition, forests and watersheds and the White House Climate Data Initiative.
The issue to deal with climate change is one of the largest examples of scientific modeling and simulation with wide variety of outcomes on frequent basis. Big data play a vital and essential role in climate prediction science, by providing corrective actions on the evolving predictive simulations through on-going data assimilation. The model output data that come out of these supercomputer simulations is enormous-terabytes of data from each of these daily simulations. These massive computer simulation output data require all of the typical big data tools for data storage, processing, analyzing, visualizing, and mining (for discovery) through computer-generated, thus complementing the massive observational data streams that come from the ubiquitous worldwide network of Earth sensors. Big data in climate first means that we have sensors everywhere -- in space looking down (via remote sensing satellites) and in situ ("on the ground") sensors, which are used to monitor and measure weather, land use, vegetation, oceans, cloud cover, ice cover, precipitation, drought, water quality, sea surface temperature, and many more geophysical parameters. These data sets are augmented with correlated data sets: biodiversity changes, invasive species, "at risk" species, etc.
Additionally, data science perspective on the climate adaptation is already embedded in research, business and policy arena. Various sectors of economy including industry, trade, manufacturing, transportation and agriculture are well documented to use the big data science in adapting to climate change. In most of the sectors, data science have been used as input to develop the production at wide ranging sectors, including water conservation technology at industrial sector, green vehicles at transportation and well recoded use of fertilizer as well as pesticides in agriculture clearly indicate that data science have been used as input to simulate and develop the production at wide ranging sectors.
Recently, adaptation finance has been growing concern of policy makers, mainly due to its essential role to get the productivity gains in financing adaptations across the sectors. In addition to that, big data science has been essential in various area of investment analysis including, risk investment, agriculture and transportation, trading, customer, interaction analysis and behavior modeling. These investments modeling scenario using Data science has also been effective to finance the most vulnerable household during natural calamities. But the use of more complex data system in analyzing the financial behavior is limited mainly due to the ill forecasting attached in predicting the future behavior of respondent using past information, data privacy as customer right, restricted accessible and transferable opportunities about data science etc. This restricted use of data science has made it less valuable among policy makers, scientists as well as business community, thus restrict its use in analyzing the financial behavior of customer.