Value of SME’s (subject matter experts) to Data Scientists

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SMEs (Subject Matter Experts) are critical to the success of Data Science projects, yet you rarely see them getting credit for passing their knowledge along to a Data Scientist. This post is to give them the credit they deserve and give an example of expected time they could put into a successful Data Science project.

Unless the Data Scientist is an expert in the domain (which is rare), they need a SME to work hand-in-hand with, especially at the beginning of a project. They can bring the following assets:

  • where to get data & tools they use today
  • nuances in the data
  • domain (business knowledge), which is how & why things are done a certain way
  • who are the other key players in the process

An example of a SMEs could be the Marketing Analyst, DBA, Director of Sales, Engineer, and the list goes on. Data Scientists benefit greatly from their help, and whether or not the project is successful, they should be recognized as partner and rewarded for doing so.

If looking to build a ML Model, SMEs are much more involved in initial stages of ML model development, and levels off to 90/10 workload after initial POC. They can help Data Scientists think through scenarios critical for model training and target variables.

However, SMEs are most likely very busy, and carving out time to help Data Scientists are limited, so it’s up the Data Scientist to be prepared, do research, take notes, and clearly communicate. How much time is needed by SMEs? Every project is different, so it really depends. In my experience, if there were such a thing as an ideal project (I wish there was), the time commitment could look something like this:

Again, this is an example of ideal project, but at least a starting point. Thank you SMEs!

This is a personal blog. Any views or opinions represented in this blog are personal and belong solely to the blog owner and do not represent those of people, institutions or organizations that the owner may or may not be associated with in professional or personal capacity, unless explicitly stated. Read entire disclaimer here.

ML vs AutoML

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I’ve been working on building AutoML Apps and explaining to others the difference between traditional ML and AutoML. I created visualization below to help tell the story:

This is a personal blog. Any views or opinions represented in this blog are personal and belong solely to the blog owner and do not represent those of people, institutions or organizations that the owner may or may not be associated with in professional or personal capacity, unless explicitly stated. Read entire disclaimer here.