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The feeling of uncertainty felt during the pandemic was extensively triggered by two characteristics of the virus; it is invisible to the naked eye and it is unpredictable.
For many companies and CEOs, this pandemic has accelerated the adoption and innovation of new technologies by months or years. The benefits and implication of this technological acceleration and disruption are here to stay and will be permanent.
Among the driving forces of this disruption is the shift of user and consumer behavior from ecommerce to social interactions and financial transactions — as well as the need of companies and organizations to adapt to the new paradigm in order to survive.
According to a recent McKinsey survey, businesses have spent more on digital investments than on any other business continuity measures during the pandemic. Consequently, growth in digital tools and systems has jumped ahead by an average of seven years in just a few months of 2020.
The pandemic has been an eye-opener for industries that have been traditionally reluctant to embrace digital transformation, such as the property sector, and now find themselves in the middle of a digital revolution, scrambling their business models and operations to a more virtual environment. Retail (including Restaurants and Food retail) was also one of the industries reluctant to embrace digital transformation. Many small retailers closed during the crisis because they did not have online stores during lockdown periods. It’s no coincidence that Shopify stock has been one of the world’s most profitable in the last two years
Simultaneously, companies that have always had technology and digital innovation at the heart of their business are now in a strong position experiencing exponential growth, frequently leaving their not-so-digitalized competitors behind.
How businesses survive and become stronger
There are several organizational factors that drive positive change and growth:
- Agility: While businesses focus on their mission and profitability, they must be aware of their environment, accounting for future developments so they can adapt in time to avoid any potential shock. Once a risk or opportunity is identified, agility allows the organization to swiftly and smoothly adapt.
- Resilience: Resilience, on the other hand, is an attribute of an organization that enables it to withstand disruptive forces and unexpected changes. It is only supposed to come into play as a back-up procedure to survive when the organization has failed to anticipate a major event and the shock has already had an impact. The level of resilience then often measures the ability to return to as close as possible to the initial pre-shock state.
A resilient company is one that can sustain disruption and adapt quickly to the new reality, while still delivering on its core business functions and protecting the brand. This is no easy task, especially as disruptions continue to grow in number and in impact.
Business resilience in the next decade will require more than just security and compliance. Organizations will need to adapt quicker and smarter to changing conditions if they hope to remain competitive and relevant. New technologies can help businesses anticipate disruptions and respond with agility.
3. Anticipation: Anticipation is the cornerstone between agility and resilience. It is the ability to foresee and respond intelligently and appropriately to any eventuality. This transition is vital for businesses who want to stay ahead of their competition. Intelligence at a business level can only come from sound, quantitative modeling that leverages historical data as the basis for future predictions or forecasts, in other words, predictive analytics.
To thrive and grow, a business has to be agile. A lean organization, continuously improving processes while using agile and scalable technologies that can help them cope with almost any situation.
Being data-driven helps organizations be both agile and resilient. Needing to be able to look back and forward to identify potential future threats or opportunities.
This is where technologies like predictive analytics become relevant, enabling businesses to become more agile by being more proactive and consequently more resilient, basing predictability on learnings (big or small) from historical data and a systematic approach to provide insights into the unknown, thus reducing uncertainty.
However, the use of predictive analytics will come with some challenges. Right now most companies would probably struggle to implement predictive analytics in their organization overnight due to a recurring constraint — they do not always have a fully data-driven culture in place. As a result, strategy, organization, processes or technologies are not aligned to facilitate the collection, processing and transformation of raw data (in various quality and quantity) into actionable insights.
What is predictive analytics
Predictive analytics can be defined as a category of data analytics aimed at making predictions about future outcomes based on historical data and analytic techniques such as statistical modeling and machine learning. 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, days, weeks or years into the future. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future.
Predictive analytics is increasingly attracting the support of a wide range of organizations. According to Zion Market Research, the global predictive analytics market is projected to reach at least $10.95 billion by next year (2022).
Predictive analytics uses a number of approaches and technologies, such as big data, data processing, predictive modeling, machine learning, and various mathematical processes to sift through current and historical data to identify patterns, and predict events and conditions that may occur at a particular time based on specified parameters, and with a confidence scoring.
Organizations are leveraging this system to identify and exploit trends in data in order to spot threats and opportunities. Models can be created to discover correlations and causations between different variables, allowing for the assessment of risks and opportunities posed by a collection of circumstances, and consequently a more informed decision-making.
Related: How Various Industries Are Depending On Predictive Analytics
Benefits of predictive analytics and where it is being used
Adopters are using these techniques in a number of ways, which frequently result in reducing costs and increasing profits. For example, predictive models are often used by retailers to forecast inventory needs, monitor delivery schedules, and customize store layouts in order to optimize sales.
Airlines are using predictive analytics to adjust ticket prices based on previous travel patterns.
Manufacturers can track the condition and performance of equipment and anticipate failures before they occur by incorporating predictive analytics into their applications.
The Insurance sector can use predictive models to track and monitor potential scammers, reducing the time spent to analyze individual claims
The hospitality industry can forecast the number of guests on any given night in order to maximize occupancy and revenue. Real estate participants are turning their eyes towards predictive analytics, wanting to understand how different areas will evolve, what the ideal location will be and how assets may perform in the coming years across different locations, as well as using real estate trends to predict the housing bubbles.
In the healthcare sector, predictive analytics could save lives. According to some of the most recent reports, AI had detected this coronavirus at its very early stages. The company BlueDot, which uses machine learning to monitor the spread of contagious diseases around the world, had alerted about the rapid increase in pulmonary disease in Wuhan late last year.
More specifically, BlueDot gathered data on over 150 diseases and syndromes around the globe, scanning databases from official s like the Center for Disease Control or the WHO to less conventional s like worldwide travel patterns, environmental and animal data or social media sensing, categorizing this data and applying machine learning to identify relevant highlighted cases for further analysis.
While these are still early stages for AI, if enough trust was given to such a model, it could have helped authorities prepare, alert and take the necessary measures which could have perhaps prevented the outbreak in the first place. It is not unwarranted to think that going forward, more attention may be given to these signals.
Related: How AI Is Changing Corporate Decision Making
Shifting from reactive to proactive
While an absolute is not something that can be given when looking into the future, uncertainty is something that can be reduced through predictive analytics.
The key factor to consider here is the broadness, quality and granularity of the analytical models used, since these need to identify trends or make predictions with a holistic approach and at a global scale. Contrary to past years, the software advances in being able to capture a broader range of s and signals is undoubtedly higher and should keep increasing in the years to come.
Agility is the key to survival — a lesson that many companies are learning the hard way on a daily basis. If you think today’s market is volatile and unpredictable, then just wait for tomorrow.
To be forward-thinking requires a degree of understanding. To be adaptive requires a degree of control. To be agile requires both.
Predictive analytics is more than just about how to predict the unexpected. It’s about shifting from reactive to proactive. It’s about living in a world of changing conditions, one in which most companies have a tendency to be ill-prepared.
It’s about maintaining competitive advantage in the face of uncertainty; and embracing it with openness, readiness and agility. And most importantly, it’s about moving from a defensive to an offensive position. Not in a way that risks everything, but in a manner that facilitates opportunity and growth.
Shifting from reactive to proactive with predictive analytics is the equivalent of trading old-school basketball defense for the latest offensive strategies: less bracing against what you can’t control and more anticipation of what is coming next.
Related: How Predictive Analytics Can Help Your Business See the Future …
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