| | DECEMBER 20239CIOReviewthe sense that it will hurt your long-term growth prospects; let me explain. Most important decisions are often made in a procyclical manner, which means they are inherently made in reaction to the current state of the economy to optimize for short-term benefits. Optimally you want to identify new opportunities and risks ahead of time in a counter-cyclical manner (preferably at the peak of the economic cycle or slightly on the downside at the latest) in order to give you a chance to implement relevant action plans on time. You also want to understand who your customers are and be able to anticipate their reactions at different stages of the economic cycle. This is so you can take full advantage of the upside of the economic cycle and not miss out on the upside momentum. Figure 1 below explains the concept: Science vs. InfrastructureWhere is my ROI? I will start by showing you my view of the ROI profile as a function of time/sophistication (I assume that as time passes, they become more sophisticated) from the time you make an investment. When I speak about infrastructure, I am speaking of the degree of sophistication of your data science pipeline, which includes your data warehousing solution, your ETL tools, and your software engineering capabilities to facilitate the development and deployment of your science at scale (whether it is MLOps capabilities, simulation capabilities or real-time optimization). I will leave the debate on cloud vs. on-premise aside for a future article. When you invest in data science infrastructure, it is sometimes hard to see the benefits immediately. You start with your data infrastructure; then you move to improve your modeling capabilities, and finally, your model orchestration along with the performance monitoring process. The value of your infrastructure is more obvious towards the end when you can translate it into savings on your SG&A due to end-to-end automation of complex modeling tasks. If done right from the beginning, you can minimize technical debt and create actionable value for the future as your infrastructure can scale to other business applications.When I speak about science, it is about the degree of sophistication of your analytics capabilities. It starts with simple data visualization or statistics and goes all the way to custom algorithms developed specifically for your use cases. Science is also about your people. There are multiple flavors of data scientists, including (but not limited to): machine learning scientists, experts in predictive modeling, operation research scientists, an expert in optimization, and quantitative economists who usually sit at the junction of these fields. Quantitative economists can be used to tackle both operational and strategic problems because they have a combination of skills in causal inference, experimental, and statistics. They can estimate causal parameters used as inputs in your decision systems or support the identification of risks and opportunities using custom econometric modeling adapted to your specific problems and data. Results from science are almost immediately quantifiable, particularly if your organization starts at a low level of data science maturity. However, as the complexity of your science increases, the ROI on your science might not be linear as a function of time/sophistication. It might plateau as you keep exploring alternative science methods but will definitely bounce back as you invest in custom capabilities specific to your business and geared towards understanding your customers.Conclusion Chasing growth in uncertain economic conditions requires an understanding of the economic cycle and who your customers are so you can anticipate how they will react at each stage of the economic cycle. My recommendation is to always keep some funding for your strategic data science capabilities to keep the innovation going so you can make the most of the upward phase of an economic cycle. Keep in mind that your ROI profile on infrastructure and science capabilities will vary as a function of time/sophistication and how mature your organization is. The more mature your organization, the more challenging it will be to generate the incremental gain you are looking for, but it is completely worth it in the end as long as you focus on understanding your customers' behavior and adapt.
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