This manuscript was prepared between the latter part of 2019 and the beginning of 2021. At the start of the work, the general consensus among even moderates in the business community was that at some point in the future people would work remotely and collaborate online and that companies would become more agile. The eight-hour day, bookended by a frustrating commute, spent mostly in maintaining archaic processes and completing reports, would die out in a new brighter, more lean future.

In March 2020, the COVID-19 pandemic caused mass lockdowns, made old heuristics instantly obsolete, and made historic data all but useless. Metrics such as year over year growth quickly became meaningless. Physical processes that had always been a bottleneck were now incompatible with lockdown laws and were promptly and enthusiastically discarded. Court filings could be done online. Engineering documents could be submitted by PDF. Every industry was forced to rapidly mature and to immediately cut costs to preserve cash.

Old budgets and forecasts were no longer relevant, and neither was the normal process to generate them. Without intuition and growth projections to depend on, many organizations turned to their analytics function to create a more systematic way to navigate the new environment.

While none of this has changed the future of data science and analytics, it has certainly accelerated the pace of change. Despite significant capital constraints, several organizations continue to invest in the function, and newly laid-off people are seeking to upskill and enter the field.

There will certainly be failures because of this rush to change approaches, but without an option to change they will remain into the future. Black swan events, whether new diseases, the political landscape, or the environment, will require organizations to be able to adapt quickly and to have robust data-driven decisions to stay alive. Prospects remain bright for analytics to support and enable this less certain future.