I saw a job posting on LinkedIn this week for a data product manager at a big tech company. My first reaction was “cool!” My second reaction, after reading more deeply, was “what is this person supposed to actually do?” The job description was so generic to the point that I had no idea what product they were actually responsible for. Then it hit me - just like product management frameworks tend to abstract away details to fit “general concepts”, talking about “data product” in a generic way can produce the same result. Data product is a useful idea, but to make it really create value, we have to get into the specifics - and that means talking about types of data products.
Thanks Eric, I always enjoy reading your pieces, really great stuff!
While I agree there are many different areas when it comes to Data Products, they still all require people that are passionate about data and also come with at least some basic understanding of what Data is about. Being a Data Product manager is not yet as widely spread as what you'd normally call a product person. Because of that, specifically asking for a Data PM with a passion/past experience in the ML world, for example, can also lead to very little talent "pool", so that's a tradeoff to take when pinpointing to specifics in a job description.
Thanks Eric, I always enjoy reading your pieces, really great stuff!
While I agree there are many different areas when it comes to Data Products, they still all require people that are passionate about data and also come with at least some basic understanding of what Data is about. Being a Data Product manager is not yet as widely spread as what you'd normally call a product person. Because of that, specifically asking for a Data PM with a passion/past experience in the ML world, for example, can also lead to very little talent "pool", so that's a tradeoff to take when pinpointing to specifics in a job description.