Big data forecasting is not new. Scientists have been assembling and managing huge data sets with the goal of understanding everything from the spread of consumers’ online shopping habits, weather trends and COVID-19 long before the latter was even a thing. This interesting topic came to us from Brookings in their article, “Forecasting and predictive analytics: A critical look at the basic building blocks of a predictive model.”
As models to forecast future events continue to proliferate, only a few groups of people understand the inner workings or assumptions of these models. Forecasting systems all have weaknesses and when they are used for policymaking and planning, they can have drastic implications on people’s lives.
Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data.
The science of predictive analytics can generate future insights with a significant degree of precision. With the help of sophisticated predictive analytics tools and models, any organization can now use past and current data to reliably forecast trends and behaviors milliseconds, days, or years into the future.
Melody K. Smith
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