Incorporating Decision Science into Data Science

Machine Learning and mathematical rigor are tools at data scientists' disposal. But we all know that having a better calculator won’t exactly result in better test scores, and having a more powerful computer won’t necessarily make you a better programmer. To develop a strategy that doesn’t underperform, a data scientist will need a qualitative understanding of decision-making. This is where decision science, the application of quantitative techniques to help facilitate decision-making, can play a key role for data scientists.

Incorporating Decision Science into Data Science

Author: Igor Pshenychny
July 12, 2022


Machine Learning and mathematical rigor are tools at data scientists' disposal. But we all know that having a better calculator won’t exactly result in better test scores, and having a more powerful computer won’t necessarily make you a better programmer. To develop a strategy that doesn’t underperform, a data scientist will need a qualitative understanding of decision-making. This is where decision science, the application of quantitative techniques to help facilitate decision-making, can play a key role for data scientists.

 

Data science is a necessary component of decision science that allows businesses to navigate big data and make well-informed decisions. The same applies in the other direction. Decision science techniques provide a data scientist with a strong foundation for efficient experimental design that saves time and money. Working in a specific domain for an extended period will eventually make a data scientist an expert, but even when starting out, there are general guidelines that one should follow to better aid the development of a project from conception to implementation.

 

Data science is a necessary component of decision science that allows businesses to navigate big data and make well-informed decisions. … Decision science techniques provide a data scientist with a strong foundation for efficient experimental design that saves time and money.

 

First, consider what questions are being answered and the appropriate tasks for answering those questions. If a decision isn’t being made, and you just need visibility into some aspect of operations, then the task will likely require descriptive analytics. If we are making a few decisions under uncertain conditions, then the task will require statistical inference. If we are making iterative decisions about the same topic, then we’ll consider building a machine learning model.

 

An essential component of decision science is defining your decision criteria before looking at the data. The requirements are pre-defined such that when specific criteria are met, then a decision is reached. In other words, true data-driven decisions aren’t made by a person but by pre-defined criteria.

 

Committing to decision criteria upfront might seem daunting, especially if you’re not well acquainted with the environment. But this serves to address any underlying cognitive biases that any of the decision-makers have. It also requires a broad understanding of the business or project, will force you to focus on the problem being solved, and can lead to asking better questions.

 

Some of these questions include:

  • What performance metrics are we using to guide our decisions?
  • What are appropriate ranges for those metrics?
  • At what level does it make sense to place the decision criteria threshold for each metric?

 

Gathering and using this information to define your decision criteria will help you make data-driven decisions. However, sometimes the decisions we must make will require flexibility or sensitive handling. In these cases, the decision-making approach should be data-inspired instead of data-driven. Data-inspired decision-making requires examining the data, interpreting the information, and then making an appropriate decision based on the findings or insights. In other words, it allows for interpretation of the data before the decision is made. While this introduces some bias into the decision-making process, it provides a less rigid and more natural approach to making informed decisions.

 

 

Whether you are using a data-driven or data-inspired approach, asking the appropriate questions and considering the pros and cons of your methodology will help improve your decision-making and save time and money long-term.

 

Integrating decision science principles into data science can help us make decisions that yield better results and benefit the individual task and the business. Whether you are using a data-driven or data-inspired approach, asking the appropriate questions and considering the pros and cons of your methodology will help improve your decision-making and save time and money long-term.

 

Here at Fulcrum Analytics, we incorporate decision science principles in our data science solutions as we help our clients enhance their analytical capabilities. Contact us for any questions or discussions.

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