In this article: COVID-19 isn’t the first major disruptive event our world has seen, nor will it be our last. In times like these, historical intelligence and traditional and organizational structures are of little use. To thrive, companies must adapt their cultures and strategies to not just survive regular disruption, but to profit from it.
While COVID-19 is having a dramatic effect on society, it isn’t the first major disruptive event our world has seen, nor will it be our last. Society’s response to prior disruptions has resulted in better managed supply chains, critical infrastructure redundancies, and faster, more responsive and robust care facilities.
There are many different scenarios that might have significant impacts on consumer and business sentiment, behavior, and demand. Depending on severity of a second wave of virus affecting the population in the next 6-12 months, there might either be sustained reductions in major investments and capital expenditures or a huge pent-up demand “spending spree” causing a spike in consumer products, business infrastructure, and investment spending.
There will likely be a hybrid of these two scenarios depending on industry. Whatever the direction, there will be challenges several orders of magnitude bigger than what we are used to dealing with.
When this passes what position will your organization be in to respond to (and anticipate) the global demands of your customers amongst such uncertainty? Moreover, how will you differentiate from the competition?
While predictive data analytics can help anticipate shifting customer behavior, is this enough to address these disruptive events and to maximize return while minimizing the pain of the economic fallout? This requires organizations to have an operating model that accommodates an extreme level of uncertainty while delivering a fast-response competitive strategy.
SETTING THE PLAYING FIELD
All of our historical intelligence generated to date is almost completely useless. In a relatively “steady state” setting, historical insights can provide some value on how organizations behave, industries evolve, and reflect some general rules governing resource utilization, competitiveness and growth.
In current conditions, virtually all business and competitive historical analyses are not applicable.
On September 10th, 2011, organizations thought they had a handle on exactly how things could potentially play out, how to position themselves to best address market pressures and industry fluctuations and how to maximize investment return.
Then September 11th happened and everything that was generated up to that time was completely useless.We are essentially in the same scenario today. Much of the competitive and market intelligence, customer insights, market dynamics, global economic scenarios, and all related intelligence & insights from only a few weeks ago are no longer applicable to a vast majority of anticipated scenarios.
Additionally, the speed of disruption is accelerated by business uncertainty, consumer fear, and difficulty discerning fact from fiction. From this, a common truth rises to the top.
MANAGING THROUGH CONTINUOUS DISRUPTION
Both 9/11 and the COVID-19 pandemic are far-reaching “upheavals” that have a huge effect on global dynamics. But aren’t the business and market disruption dynamics that are experienced on nearly a daily basis really “mini” upheavals of their own?
The 4th Industrial Revolution has created disruption sufficient to render historical analytics obsolete in relatively short order. Unfortunately, businesses underinvest in trying to understand and predict fallout from normal business disruptions.
And that is exactly my point – in order to have a competitive advantage, strategy executives need to treat almost every day as a potentially highly-disruptive scenario to map and plan in order to be in front of a business disruption.
Otherwise, businesses lag behind, waiting to see how things will evolve before taking action.While many companies have disaster plans readily available, there are extreme cost pressures that inhibit adequately preparing for lower probability disruptions, or ones with a frequency that is hard to determine.
Moreover, companies often focus on the familiar threats because they have systems in place to monitor known risks. This adds very little value to long-term planning when faced with unfamiliar threats, and, worse, it can lead to organizations having to make quick and sometimes, inappropriate decisions under duress.
There are several approaches to managing this. Some companies stress crisis management as a separate but related process that gets organizations quickly out of a crisis state while working to sustain normal business operations.
Alternatively (and sometimes concurrently), companies can take steps to establish a temporary stop-gap to the new normal. These activities move into play usually after some aspect of the crisis has been resolved. This methodology outlines a sequential series of activities and behavioral impacts to do “damage control” during the disruptive event, while putting in a robust plan to embrace the “New Normal” after the event. Building this bridge to the “New Normal” is key to disruption preparedness, and requires a seamless integration between intelligence and strategy, regularly informed by data analytics and modeling performed on a continuous basis.
ALL UNCERTAINTY ISN’T CREATED EQUALHowever, there is a level of what McKinsey1 calls “residual uncertainty”: information that is still unknown, and that is dependent on the outcome of circumstances outside of the organization’s control, i.e., government policy, performance of new technology, or anticipation of an immunization and protective measures to address a viral pandemic. To address this, this uncertainty falls into 4 categories:
A Level 1 scenario suggests that the uncertainty is small enough that a general forecast based on analytics and known parameters can be reasonably assessed with minimal risk. This is usually done through the traditional tools in a Data Scientist’s toolbox, market research or competitive analysis.
In a Level 2 scenario, the environment is a bit more complex and uncertainty is higher. This is where probability factors are incorporated to help assist the likelihood of which scenario might play out given various outcomes.
In a Level 3 environment, little is known about the outcome; possibly a few key endpoints or milestones. But now, discrete trends or patterns emerge from the traditional analyses. Strategists use Scenario Planning and Discrete Predictive Analytics to help segment potential roadmaps and develop a suitable narrative around supporting a resultant strategy.
Level 4 is a worse-case situation, where it is impossible to identify potential outcomes, let alone scenarios. Additionally, there could be subsequent variables still yet to be encountered which could amplify the degree of uncertainty even more. Here, the number of tools is few. However, there are some effective techniques that can help reduce uncertainty. By studying how analogous markets developed in other level 4 situations, by determining key attributes of winners and losers and identifying the strategies they employed, strategists can leverage actions, develop patterns, and draw key conclusions on near-duplicate experiences.
BUILDING AN ORGANIZATION THAT CAN WEATHER UNCERTAINTY
So how does one continuously prepare for these highly disruptive environments with varying levels of uncertainty and moreover employ an Intelligence System that can better anticipate global shocks? McKinsey suggests that; “At the heart of the traditional approach to strategy lies the assumption that executives, by applying a set of powerful analytic tools, can predict the future of any business accurately enough to choose a clear strategic direction for it.”
These different levels of uncertainty and the corresponding course of actions are only one piece of the puzzle. Many of these tools and actions require an appropriate organizational structure, culture and mindset instilled through all levels of the company that aligns to this proactive strategy.
Otherwise, the result is a siloed approach, where only certain functions within the company understand the level of urgency and are aligned to the direction. In these cases, we see infighting, fiefdom building, and multiple “edicts” on organizational direction. Ultimately this leads to the demise of many companies that cannot (or will not) align to a single course of action.Effective organizations must look at this issue through two lenses:
- Anticipating and understanding ramifications of disruption
- Developing and sustaining an infrastructure to respond to this new landscape
While there are many complexities to this, what follows are a few fundamentals that must be internalized, disseminated, and acted upon to ensure an effective overall company strategy:
DISRUPTIONS ARE THE NEW NORM
Since the 4th Industrial Revolution has taken a foothold in business economics, disruption take hold very quickly, are highly impactful, and are essentially continuous. Adapting to and embracing these disruptions goes against human nature – many resist change and uncertainty, and resort to fear, uncertainty and doubt.
This can be amplified by sensationalism, exaggeration, and a lack of clarity between fact and fiction.Companies should employ several mechanisms – such as “trend spotting” to quickly assess rapid shifts in business direction, technology usage, or supply chain sourcing – in order to identify early fluctuations that may impact not only a business model, but the industry sector as a whole.
SCENARIO PLANNING & PREDICTIVE ANALYSES NEED TO HAPPEN CONTINUOUSLY
Scenario Planning2 can provide a variety of perspectives for executive leadership that were not previously considered.
Additionally, it can highlight areas ripe for change and disruption. This focus can aid in a preemptive push for change by the firm that could redefine the firm’s industry position.Scenario Planning needs to be generated and played out on multiple time horizons, segmented by business function.
Another study by McKinsey3 suggests employing “plan-ahead” teams that can develop strategic crisis-action plans that guide organizations though stages of the disruption.
Plan-ahead teams should be charged with collecting forward-looking intelligence, developing scenarios, and identifying the options and actions needed to act tactically and strategically. Unlike a typical strategy team, they will plan across multiple time horizons on a continuous basis to ensure operational readiness and maximum flexibility.
Finally, scenario planning reminds executives of the relationships between all industries. Industries are interconnected, much like a spider web.
As James Bezjian writes, “When there is movement in one area of the web, the entire web shakes. If a portion of the web collapses or breaks, the entire web is compromised and must be discarded.”However, performing such planning exercises in a manual, batch format is time consuming and inefficient given the urgency surrounding disruption.
The lack of scale, speed and application have slowed traditional business intelligence from driving real business value.
Thankfully we have achieved important technology milestones where applying predictive analytics (mostly likely, through Augmented or Artificial Intelligence applications) can be a game-changer. The ability to run millions of regression models with different combinations to identify key influencers and develop hypothetical scenarios to predict their impacts is instrumental in driving strategy models best suited for continuous disruption.
DATA ACQUISITION, ANALYTICS, & STRATEGY DEVELOPMENT NEED TO BE FLUID
Data generation and consumption needs to be performed as frequently as possible. In the current pandemic, there is new content every day around infection rates, vaccination studies, and the impact of quarantining on “the curve”. All of this dynamic information has to be synthesized continuously to effectively understand predictive outcomes and subsequent potential roadmaps.Business scenarios can change just as quickly. Major disruptions can come from the unseen forces of earthquakes, floods and viral pandemics, but also by the kid in the basement of their grandmother’s house who has found a different and more enticing way to digitize another form of social interaction on the internet. These changes can literally occur overnight and have an immediate, global impact on entire industries.
Ascertaining actionable insights from the exabytes of data that are generated every day is a daunting proposition. In order to garner any value, it’s imperative to utilize AI tools and knowledge management systems to manage this vast source of data. These tools can not only perform complex analytics on the data itself, but can regularly monitor the data over time, thus benefiting the Data Scientist with great insight into potential trends and roadmaps that can be predictive, and ultimately, prescriptive in nature.
Source: Paul Santilli is a 24 year veteran of Hewlett Packard Enterprise (HPE) and currently leads the HPE Worldwide (WW) Industry Intelligence & Strategy Organization for the Original Equipment Manufacturer (OEM) Solutions Business. He is a recognized thought leader in Data Analytics Modeling around Industry Intelligence, Insights and Strategy.
Paul also serves as Chairman of the Strategic and Competitive Intelligence Professionals (SCIP) Board of Directors Executive Committee and is active in several advisory roles to industry conferences and forums. Paul presents worldwide on Intelligence, Innovation, and Strategy in keynote and executive audiences, and has published numerous papers in industry and academic journals related to Intelligence Modeling, Innovation, Disruption, and Strategy.
Prior to HPE, Paul contributed 10 years at Apple Computer in various leadership roles around Quality, Operations and Product Development. Paul has a Bachelor’s degree in Engineering from the University of Michigan, and a Master’s degree in Engineering and Business from Stanford University.