The measurable turnaround of public confidence in data science applied for social good since the outbreak of COVID-19 represents an opportunity for data-driven analytics to embed far more deeply in society than was envisaged in the preceding years, with the triumphs and challenges of the COVID-facing public sector data science setting a new benchmark and level of expectation across the commercial sector as well.
However, although there’s reason for optimism that AI-focused data science can thrive in both sectors in the coming years, data science consultants need to carefully evaluate the evidence for this in light of the current global upheaval.
Nearly all the contributing variables for the coming economic and market environment are in flux; not only have the massively skewed spikes and troughs of the COVID era literally forced a re-write of many analytics and AI-driven prediction systems, but they have also cast doubt upon the last few years of trends and predictions related to the industry3.
Let’s take a look at the few broad indicators we currently have and examine how the wider data science sector might need to reconcile the volatile conditions of 2020 and beyond.
Making Sense of the Blown Gradients after COVID
According to McKinsey, the rout of ‘face-to-face’ society has broken historical analytics models so badly that pre-COVID data may itself have to be considered in a separate historical context, and compensated for in any attempt to maintain a continuum of measurement for year-on-year progression in business cycles.
At the time of writing, post-outbreak data has had less than a year of existence. With no way to know when the post-COVID era will begin (or whether the threat will diminish with the speed of the Millennium Bug or the languorous, decades-long struggle against HIV), it’s difficult to understand at what point a long-term analytical context will emerge that isn’t specifically related to efforts against the virus.
Even if we were inclined to consider the period since the outbreak as a ‘hard reset’ in analytics and forecasting, not only is post-outbreak data insufficient yet to form a base for prediction, but it’s also defined by a number of pivotal factors that are in no way constant or reliable enough for forecasting further years of combatting the epidemic. These factors include:
- State-led fiscal interventions that give a statistically skewed representation of national and global consumption, productivity and stability.
- Supply chain interruptions, which have had a significant effect on downstream economic logistics management.
- Changes in customer behavior that relate directly to the increased short-term and long-term economic insecurity, and which are not in themselves constant or predictable.
- The ambient effect of the diminution, destruction and/or bankruptcy of multi-billion dollar industries that underpinned the pre-COVID model, such as airlines and hospitality.
There is general agreement, however, on one upcoming scenario in which the data science sector will not only play an important role, but need to negotiate in itself.
The Implications of Recession for Data Science
The World Bank estimates a 5.2% reduction in the global economy in 2020, characterizing the growing downturn as a portent of the deepest recession since WWII, affecting the largest number of countries since 1870. Others, including the IMF and Time, assess the coming decline in terms of a depression that’s likely to last longer than any comparable economic crises of the last 100 years.
Though the effect of furloughs, lay-offs and terminations has not left the data science sector untouched, with a reported 40% of surveyed companies freezing new data science hires, the downward trend is slower relative to the average in comparable fields.
It should also be considered that sector demand was declining directly prior to the outbreak, and that it’s now impossible to tell how that short downward trend might have developed.
In any case, outside of public-sector data science projects either directly or indirectly related to COVID, data science seems destined for a period of reductionism and a focus on essential, proven analytics implementations.
5 Areas of Expected Data Science Uptake
There is a strong possibility that the customary recovery environment of a late-stage recession will be different from those of the past; and that the inability to quickly reboot into a pre-COVID economic template will elevate data analytics to a more central and enduring position in civic and commercial culture, no matter what the outcome of the crisis might be.
If we do not currently need the new roads, train lines, and other travel-based infrastructure boosts that kick-started the failing economies of the more mobile societies of the past, and if we are unable to re-capitalize (as after 2008) by continuing to inflate the cost of real estate in major cities in the face of an urban exodus and an emerging long-term telecommuting culture, it seems inevitable that money will follow consumers into virtual space — which signifies a massive acceleration of digital conversion and the analytics culture that accompanies it.
For business, this external pressure to migrate from analogue transactions to digital paradigms and platforms is merely an acceleration of what was already occurring in the years leading to 2020.
The following are the key areas where data science is poised to aid in sector recovery and endurance.
Global and National Supply Chain Logistics
The use of AI to model historical and ‘live’ logistics data allows distribution companies to simulate thousands of scenarios, incorporating ad-hoc factors such as state and national restrictions that may come into force or be cancelled according to infection levels and local and national policies.
As with many sectors that depend on predictive machine learning systems, the Supply Chain Risk Management (SCRM) industry has had to recover from unexpected variations in demand since the late winter of 2020, and is in some cases merging prediction models for the lifetime of the COVID-19 crisis into traditional logistics forecasting systems.
Remote Productivity Management
In the United States alone, the coronavirus crisis is set to force a 733% increase in the number of Americans working full-time from home, with many influential companies now committed to partial or permanent long-term telecommuting, irrespective of societal progress against COVID-19.
Though there are diverse AI-powered solutions to aid remote workers in scheduling, search, and even to improve the quality of teleconferencing, it seems likely that innovations in AI-based remote monitoring will also bring new data streams that can be utilized for performance monitoring at an individual and team level.
The use of data science and machine learning to evaluate employee performance pre-dates the pandemic. Such systems are centralized and can be applied to the remote working sphere via authorized VPNs, with only minor latency considerations compared to on-premises workstations.
One company has experimented with AI-enabled fitness monitors to assess the level of employee stress during remote working under COVID-19. However, depending on the geographic region and its applicable laws, the proliferation of such systems may eventually bring privacy and diverse legal implications into the public arena, notwithstanding an employee’s contractual consent.
Machine learning has been used in a number of evaluative systems for students forced to follow academic courses in ad-hoc online environments — a necessary innovation that may eventually feed into more stable and generalized online management systems.
Besides providing curated information on the COVID-19 crisis for overburdened health services, chatbots are proving an essential stopgap for companies whose former investment in intelligent virtual assistants (IVAs) has become urgent under coronavirus-related restrictions, and for whom negative public perception of AI-based assistants has been relegated by the scale of the current public health crisis.
Thus it appears that increased public tolerance for chatbot-based interaction in core services such as health may be improving the general reputation and uptake of chatbot development on the whole.
The pre-COVID estimate that chatbot sales would grow to US$112 billion by 2023 is now thought by some to have multiplied significantly. However, the current trend towards economic decline also indicates that lower general economic demand could cancel out this trend, leaving the market with similar prospects to those forecast in the pre-COVID era.
The necessity for increased engagement with chatbots represents an unforeseen opportunity to improve both their performance and reputation: as the volume of IVA transactions increases, analysis of the interactions allows their underlying systems to improve in a manner that could not have been foreseen prior to the pandemic — a further boon to chatbot research and deployment.
After-Market Service Through AI-Driven Analytics and Logistics Systems
According to Deloitte, the possibility of ongoing disruption to supply chains, together with diminished spending patterns, constitutes a threat to the hardware technology release cycle, including computing components, smartphones, consumer electronics devices, and a range of business technologies.
This situation could trigger a short- or long-term emphasis on after-market support, where the role of machine learning analytics could transform from a minor industry advantage to a critical edge in a market often beset by low margins.
The much-criticized tendency of manufacturers to abandon software support for older models in order to perpetuate the buying cycle could even transform from time-limited after-market care to the subscription-based SaaS models that have come to dominate the software market since 2015.