Making Better Data-Informed Decisions to Navigate Disruptions
Unprecedented levels of disruption have significantly complicated business leaders’ efforts to make critical data-driven decisions to guide their organizations successfully into the future. To keep their analytic capabilities, and associated technology, on par with the rapidly changing environment, businesses must reimagine their approach to data and algorithms with a focus on trust and reliability.
As business leaders navigate an environment of multiple simultaneous disruptions, and where unimagined risks have become more commonplace, they should acknowledge that the decision-making process demands humility and collaboration. While looking for data to provide clarity, leaders must identify concepts that can be measured and monitored using reliable data sources.
Along these lines, they need to be aware of data and measurement biases to inform more impartial outcomes that are likelier to inspire confidence and trust. Finally, business leaders must remain open to reevaluating and course correcting as updated or new information could prompt a revised assessment of the underlying situation.
A New Class of Disruption
Traditionally, companies have managed risks across domains that, while often volatile, were nevertheless limited in scope. Market dynamics, disruptive technology, and regulatory risks can change dramatically quarter to quarter, for example, but business leaders often rely on several key assumptions about broader global trends. However, the events of recent years have made manifest that business and political leaders can no longer rely on these assumptions.
A lingering pandemic and its impacts have drawn into question traditional supply chain and risk management approaches. Social and political concerns have introduced new regulatory risks to businesses across industries. Global economic uncertainty lingers. Climatic risks require business to reconsider both their current supply chain strategies and long-term geographic footprints. Finally, geopolitical risks—including war and sanctions —and the uncertainty of some international agreements have upended traditional assumptions about the security of long-term investments.
Furthermore, the advancement of artificial intelligence (AI) and its broad use in business processes and decision science have augmented business leaders’ strategies. While data and automation have supported business decision making for years, recent advances in AI have called into question many traditional assumptions about what aspects of business analysis can and should be automated.
Just as important, concerns about the trust and reliability of AI-enabled decision-making tools and the data sources, the measures, and the methods they employ require risk management officers to consider new risk vectors along with the cost-saving opportunities of automation. Regardless of whether the ultimate decision maker is human, autonomous, or a hybrid team, data remain paramount. Globally consistent unique identifiers that are trusted by producers and consumers can help businesses assimilate multiple data sources that collectively provide both flexibility and depth.
A Necessary Humility
In this daunting environment, now is not the time for business leaders to presume that they will have the answers to effectively navigate this turbulence. Now is the time to seek a more holistic view by assimilating new data streams from multiple perspectives and domains previously left unexplored. This can mean understanding a short time horizon in conjunction with longer-term planning. It can mean assessing operational, financial, geographic, and any other number of risks independently and collectively. And it can mean a location-based strategy that incorporates climate risk, economic trajectories, policy constraints, compliance history, and geopolitical concerns.
In sum, it’s a time for business leaders to acknowledge what they don’t know. They need a necessary humility to face this new paradigm, allowing them to onboard new perspectives and reconsider longstanding assumptions.
We know that disruption can have compounding effects that challenge business resilience. Specifically, disruption inhibits a business’ ability to recover from shocks.
Therefore, business leaders need to develop proactive business continuity plans that they can adjust in uncertain times. Appropriate data and analytics can support such planning. To be specific, business leaders and the organizations they run will have a greater likelihood of weathering uncertainty by studying the response to past disruptive events, modeling scenarios in future states, and optimizing for desired outcomes.
In this environment, today’s business leaders also need to understand that individual intelligence, quality data, and management of this data, along with an advanced technology stack likely won’t be enough for companies to solve everything by themselves. The solution lies in expanding the circle to collaborate with others who can provide different perspectives that will help leaders better address what they’re trying to accomplish. While navigating the pandemic required collaboration and knowledge sharing internationally, locally, and across industries. Responses came from government, industry, public-private partnerships, and non-profits. Solving inventory disruptions in specific verticals, like manufacturing, required logisticians and local domain experts often within, across, and outside an company.
Next, business leaders must understand the question they’re trying to answer and acknowledge the underlying biases embedded in the inquiry process. Take steps to make sure to ask the right question; a leading question can divert someone from finding an accurate answer.
Regardless of where the data appear to point, it’s also important to understand the limitations the answers provide. Business leaders need to be able to properly gauge how wrong they can be and still make the same decision—also called decision elasticity.
And business leaders should be careful with how they analyze and use their data. Embracing beautifully visualized poor or incomplete data can steer leaders toward inaccurate answers and the wrong conclusions.
The Risks of the Status Quo
In a data-led economy, increasing transparency and reducing information asymmetry is also imperative; organizations can share insights across their various business units to ensure relevant business metrics are current, compliant, and actionable. It’s advisable that the data reflect any potential impact on financial, ownership, and operating structures.
Ultimately, companies need to re-think how they make data-based decision to fit today’s environment probably more than they expect. Those organizations that take no action against these simultaneous disruptive events will fall behind competitors at increasing speeds as their analytic capabilities fail to keep pace with the rate of change.
This shift will require more than just hiring smart people to help find solutions. Business leaders will need to carefully reconsider their approach to using technology and data to meet these challenges, survive, and grow.
About the author: Dr. Amber Jaycocks is Senior Vice President of Public Sector Data Science at Dun & Bradstreet. She leads analytics covering applied econometric and machine learning research to develop insights and solutions that help organizations grow and thrive. Jaycocks’ team of data scientists, economists, and analysts work with Dun & Bradstreet’s proprietary data along with macroeconomic, third-party, custom, or publicly available sources. The global data assets are integrated with multi-disciplinary approaches for applications that cross policy domains. Her research primarily focuses on decision-making for complex systems. Dr. Jaycock’s diverse experience in quantitative research spans both public and private enterprises. They include the RAND Corporation, a think tank, a supranational organization, and the World Bank. She previously served as the Head of Data Science for Morningstar, a financial research company. Other professional endeavors include work with fintech and deep tech startups, quantitative financial research, and the federal government. Dr. Jaycocks earned a bachelor’s degree in environmental engineering from Massachusetts Institute of Technology (MIT) and a master’s and doctorate degree in policy analysis from the Pardee RAND Graduate School.