Over the past five years, the question of unlocking the value of data science and big data programs for various applications in finance and supply chains has come up repeatedly. This question has only grown in importance as economic growth has slowed, with the unpredictable disruption of COVID further complicating the landscape.
For companies aiming to maximize their data science programs, two critical aspects must be considered: the functional component, which is divided between operational and strategic activities, and the technical component, which is divided between infrastructure and science. Functional areas often fall into operational activities, which are necessary for daily business functions, or strategic activities, which focus on identifying new opportunities and mitigating risks. These categories are not mutually exclusive, but they frequently compete for funding and prioritization. Operational data science efforts include building automated decision systems, such as forecasting models that use machine learning or inventory management systems based on operations research techniques. Strategic data science efforts focus on long-term business opportunities and risks, supporting areas like pricing strategy, scenario planning, and product lifecycle management.
When deciding how to fund data science programs, it is crucial to consider the economic cycle. Economic conditions are not solely defined by GDP growth but also by factors such as consumer spending, the labor market, inflation, and access to credit. The ROI profile of infrastructure and science investments will evolve based on the organization’s level of sophistication and maturity. During economic downturns, leadership often prioritizes operational efficiency over strategic initiatives in an effort to maximize short-term shareholder value. While this approach may seem necessary, it can hinder long-term growth prospects. Many business decisions tend to be reactive, optimizing for short-term benefits based on current economic conditions rather than proactively preparing for future opportunities. Ideally, companies should anticipate new risks and opportunities in a counter-cyclical manner, taking action before economic conditions shift rather than reacting once they do. Understanding customer behavior and predicting reactions at different stages of the economic cycle is key to capitalizing on future growth.
The ROI of infrastructure and science investments also varies over time. Infrastructure investments focus on improving the sophistication of data pipelines, including data warehousing solutions, ETL tools, and software engineering capabilities that enable scalable deployment of data science initiatives. This includes MLOps capabilities, simulation frameworks, and real-time optimization systems. The benefits of these investments are not always immediately visible, as infrastructure evolves from basic data management to advanced model orchestration and performance monitoring. Over time, strong infrastructure can lead to significant savings through automation and efficiency gains while minimizing technical debt and enabling expansion into other business applications.
Science investments focus on advancing analytics capabilities, ranging from basic data visualization and statistical analysis to custom algorithms tailored for specific business needs. A diverse team of data scientists, including machine learning specialists, predictive modeling experts, operations researchers, and quantitative economists, plays a key role in both operational and strategic initiatives. Quantitative economists, in particular, offer expertise in causal inference, experimental design, and econometric modeling, making them valuable for decision systems and risk identification. The impact of science investments is often immediately quantifiable when an organization has low data science maturity, but as sophistication increases, the ROI may plateau. However, customized, business-specific science applications can unlock new value, particularly in understanding customer behavior and market dynamics.
Chasing growth in uncertain economic conditions requires a deep understanding of the economic cycle and customer behavior. Organizations should allocate funding to both operational and strategic data science efforts to maintain innovation and take full advantage of economic upswings. The ROI profile of infrastructure and science investments will evolve over time, and while incremental gains become more challenging as an organization matures, sustained focus on customer insights and adaptability will ultimately drive long-term success.
