At its core, developing a data science solution for a business challenge involves breaking down the functional task into well-established data science task primitives. While there are only a few such primitives (see (Provost & Fawcett, 2013) for details), this process is rarely straightforward. A naive approach often exposes hidden challenges, such as unverified modeling assumptions or suboptimal models that are expensive to build and maintain. (Pyle, 1999), though dated, offers timeless guidance and a comprehensive overview of the tasks involved in a typical data science project.
Creating a successful data science model often requires significant exploration, including collaboration with business stakeholders and iterative modeling experiments. Success hinges on the ability of the data science team to ask the right questions and frame the problem correctly. This demands skill, experience, and sound engineering judgment. (Provost & Fawcett, 2013) encapsulates this idea succinctly:
A critical skill in data science is the ability to decompose a data- analytics problem into pieces such that each piece matches a known task for which tools are available. Recognizing familiar problems and their solutions avoids wasting time and resources reinventing the wheel. It also allows people to focus attention on more interesting parts of the process that require human involvement—parts that have not been automated, so human creativity and intelligence must come in‐ to play.
If you’re ready to bring your data science-driven business idea to life, schedule a conversation with me today. For an overview of my approach, visit the process section. To explore the range of services I offer, check out the services section. You can also explore topics of interest on my blog.
Bibliography
- Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. " O’Reilly Media, Inc.".
- Pyle, D. (1999). Data preparation for data mining. morgan kaufmann.