“Risk is the price you pay for opportunity,” goes a famous saying. Businesses are built to create value from opportunities, and it’s near impossible to run a business without risks. Organizations today face a variety of risks, and these risks come in different shapes and sizes. Broadly, these can be categorized into operational risk and strategic risk, based on how they emanate and the impact they can have. For organizations to continue creating value without being derailed along the way, they need to manage these risks effectively.
Operational risk relates to the disruption of day-to-day business processes – situations pertaining to resources, systems, employees, and compliance. Examples include equipment breakdown, supply chain disruption, fraud, employee attrition, data loss, non-compliance, cyber threats, IT infra/network issues. Progressive organizations are already using data science heavily to mitigate operational risk. For example, today machine learning is helping build early warning systems that identify issues ahead of time and take corrective actions.
Strategic risk arises when an organization is not able to react to the market conditions and needs in time. These include changes in customer preferences, regulations, technological advances, competition, market shifts, etc. Such risks usually have a deeper impact and can affect an organization significantly. Traditionally, executives in board rooms proposed risk mitigation strategies based on their experience and gut. However, today, data science is becoming a valuable tool to manage certain types of strategic risks.
Here I share examples of three of our clients, all leaders in their industries, who used data science to manage certain types of strategic risk:
A global technology enterprise wanted to be prepared for the future in the face of rapid technological advances. In today’s world, several thousands of software technologies are being experimented with – most fade away, but some go on to become transformative technologies. Investing in the right technologies at the right time would mean the difference between being a market leader vs. a laggard. Rather than relying on business analysts, the company now uses an intelligent monitoring system that continuously scans through the new technology landscape, predicting which technologies stand the best chance of becoming game-changers two-three years later. This system is powered by machine learning which crunches through a dizzying variety of technology-related data, from open-source code to tech commentary, extracts relevant signals, and discovers patterns of how successful vs. not so successful technologies evolve. Today, this solution helps guide the company’s investments into nascent technologies.
A global F&B giant wanted to ensure their market share was not disrupted by upstarts who were coming up with innovative products with unique ingredients, positioning, and targeting. Moreover, their competitors had also been launching and acquiring various products with varying degrees of success. While they too had launched new products to not be left behind, they wanted to manage this exercise systematically. Analyzing a whole host of trends and network effects in the marketplace helped them quantify evolving customer preferences and shifting markets. It led to data-driven recommendations on the types of products to launch. In one initiative, the organization was able to not only address strategic risks but also uncover their next billion-dollar opportunity.
A transportation company wanted to understand the risks that economic fluctuations pose to their business. Networks of econometric models revealed how economies of different countries affected consumer demands, which in-turn affected imports and exports, which subsequently had a bearing on demand for various transportation services in different regions. Simulating various global economic scenarios, from positive growth to a depression, helped identify potential spikes and troughs in demand across the client’s services, the stress on their supply chain, and the financial implications. It helped them not only plan for the right redundancies across their supply chain but also plan for financial contingencies.
As you see in the above examples, strategic risks are also strategic opportunities. They are not structured problems, and there is a lot of uncertainty and ambiguity around them. To take advantage of these opportunities and minimize the downside, companies need a systematic process to identify and track these risks. Whatever be the process, data science can be a powerful means of quantifying and managing strategic risks and opportunities.