The past years have proven to us that change is the only constant, with unexpected shifts in societal and business landscapes becoming more commonplace than any predictions would have allowed. From natural disasters to global pandemics and political disruptions, what was once described as an anomaly now seems like the new normal. Much of this increased volatility can be attributed to our highly interconnected world, fostered by technological advancements. Events that would have previously remained localised now reverberate around the globe, causing far-reaching disruptions and instabilities.
Technology as a Catalyst and a Solution
However, the irony lies in the fact that technology, while being a catalyst for the spread of such disturbances, is also our greatest ally in anticipating and dealing with them. History attests to the transformative role technology has played in enhancing human lives. From improving food quality to extending life spans, technology has been at the forefront. At the same time, organisations that failed to adapt to technological changes were left behind. Today, as we face a surge in unpredictable global events, we need to leverage technology not just to react but to anticipate and prepare.
Understanding Human-Driven Changes
It is important to note that not all significant changes affecting society and businesses are technology-led. Many are driven by human behaviour or natural events. The dot-com bubble of 2001, the global financial crisis of 2008, the European sovereign debt crisis, natural disasters like the 2004 Indian Ocean earthquake, the 2010 Icelandic volcano eruption disrupting thousands of flights, and the ongoing COVID-19 pandemic, all point to an increasing frequency of "long-tail" events causing severe disruption. These scenarios demand a reevaluation of organisational agility and the capability to anticipate and cope with sudden changes.
Adapting to the New Normal
The necessity for organisations to evolve their services and better position themselves for long-term resilience in the face of systemic shocks is becoming increasingly apparent. Consider the seismic shift from physical retail to online commerce driven by repeated COVID-19 lockdowns – a transition that caught even established retail chains off-guard. It's clear that organisations need to make themselves more tolerant of shocks that can significantly alter supply and demand dynamics.
Leveraging Data and AI for Preparedness
Preparation for the unpredictable requires analytics and data-driven decision-making. Consider the NHS as an example – it's a highly data-driven organisation where clinicians base decisions on well-sourced, accurate, and relevant data. The organisation is learning how to manage data at scale to provide the best outcomes, concentrate resources where they will be most effective, and continue to innovate.
However, to achieve full agility that allows predicting trends or events and determine the best course of action, data needs to be fed into AI and machine learning models. These models, learning from historical data and recognizing patterns, can use current data to indicate likely future developments and the best strategies for organisations to handle them. Predictive modelling capabilities can enable businesses to prepare for major changes and shocks, be it weather events, shifts in remote working, office downsizing, or the growth of smart cities.
The Importance of High-Quality Data
The foundation of predictive capabilities is access to high-quality data. Many enterprises already collect high volumes of data from sensors, hardware, and manual inputs. They are on the path towards predictive capabilities, although for many there is work to be done to effectively manage the data available to them. Emerging smart data fabrics can help organizations overcome these challenges and leverage their data to its full potential.
Smart data fabrics interweave data from multiple sources and formats, using a multi-tier approach that cleans data and uses an integration layer to make it usable. This approach leaves the data where it is while tracking its lineage for every item, allowing users to see where it has come from. By combining internal data with relevant external sources, organisations can prepare it for use in machine learning models, allowing for dynamic queries and data analytics. These provide the predictive insights that organisations need to anticipate what lies ahead and prepare for it more effectively than competitors struggling with poor-quality data or a lack of AI and machine learning capabilities.
As we move forward, access to AI and machine learning-based predictive capabilities should extend to small and medium-sized businesses. We need to encourage more organisations to start harvesting and effectively managing data. As this happens, models and use cases suited to their scale and requirements will develop, providing them with the agility needed for the future. Regardless of the scale of the business, if it has access to high-quality data, its leaders should be considering how to use it to predict the unpredictable and become a more adaptable and better-prepared organisation.