Strategic planning is when an organisation sets its priorities, distributes resources, creates and implements strategies, and monitors their level of success. This process ideally occurs in conjunction with crisis planning. While strategic planning seeks to exploit opportunities available within a changing environment, crisis planning – also known as crisis management – tends to focus on the threats inherent in that same environment.
These plans may be created over a series of weeks or months. Yet, regardless of their composition and intent, as 2020 showed and to paraphrase Prussian field marshal Helmuth Von Moltke the Elder: “No plan survives first contact with the enemy”.
Amid the change instigated by the COVID-19 pandemic – such as the distributed work revolution – what the pandemic and its ensuring crises revealed was the limitations of AI-driven technologies in anticipating fast, disruptive change. Solutions built upon AI and machine learning provide excellence and efficiency when underpinned by stable and relatively consistent inputs. Yet, as tools that extract and ascribe patterns from data, if their inputs radically change, those supporting premises quickly become obsolete.
According to Cassie Kozyrkov, chief decision scientist at Google, machine learning and statistics has enjoyed increasing prominence within data science, while the “unloved stepchild” of data analytics and analysis were overlooked.
Statistics and machine learning “layer mathematical sophistication on top of a foundation of human intuition, creating an appealing allusion of objectivity and deft steering,” Kozyrkov says. Both can provide high quality answers so long as the correct question is being asked yet among the three, “analytics is the most essential competency for navigating crises”.
The pandemic threw up questions that governments and businesses of all sizes struggled to grapple with for months, as its effects became clear. To apply statistics without analytics, as Kozyrkov suggests, requires supreme confidence in the assumptions supporting statistical models and the queries they are directed to address.
Analytics and analysis offer robust strategic planning considerations
By comparison, analytics and supporting analysis is premised on extracting insights and alerting project or data owners when wider-than-expected change begins to occur. Analysts are familiar with ambiguity because their skillset is premised on exploration, a valuable commodity when foreseeing and responding to crises. Analytics seeks to identify the hairline cracks in a given context, and ask why these cracks are forming, how large they may become and provide a range of possibilities.
These possibilities can be presented to an organisation’s executive team, who may rank them from most to least likely to occur and take action accordingly. For example, when the COVID-19 pandemic first emerged in China, the following questions could have been posed to a leadership team:
- If the pandemic arrives in our country, how will our business be affected?
- What if the pandemic remains localised in Asia?
- How long may the pandemic last?
Unpacking an answer to the first question today with all that is known is complex. For an executive team dealing with the same question prior to the event, foreseeing all that was to come would have been impossible and with it easy to state that these possibilities are now being considered in hindsight.
However, as an example of the broad lens data analytics and analysis can provide, the pandemic is the exact type of threat and/or disruption they seek to account for. Through entertaining the question in the first place, an executive team may have been better prepared for the crisis and ready to consider worst case scenarios, and having a crisis plan for them, if they were to occur. At a crisis’ beginning, the degrees of separation in costs between being slightly prepared versus wholly unprepared are stark.
As the world adjusts to the pandemic’s effects, its change and its possible, gradual end, organisations for the short-to-medium term will likely be better prepared than ever to deal with major, unforeseen shocks.
However, as time passes and institutional memory fades, organisations should consider building or maintaining a data analytics function if possible, as it will allow for better preparation prior to a crisis striking. When the status quo is stable and profitable, that is an optimal time to be testing assumptions that underpin strategic planning and being aware of possible threats, no matter how remote.
Does your organisation wish to build a data analytics function to aid stategic planning but does not know how? Letsema’s Strategy and Enablement practice is highly experienced in data analytics and establishing analytics units within client organisations. To find out how they can help your organisation prepare for the next crisis, email strategy.team@letsema.co.za or info@letsema.co.za.
