I've been involved in a lot of Data Science projects, and the most successful ones are the ones where the Data Scientist either did or was part of the infrastructure and Data Engineering.
Most of the failed projects basically were "Hire a Data Scientist to make a magic model" and nothing else was supported. Basically, "Here's some data, do some magic." I read somewhere that 90% of data is low/zero value, and I'd agree with that.
There's absolutely a place for people doing pure statistical or ML/Deep Learning modeling. But the rest of the organization has to support them so they can have that narrow focus. A lot of places want to take a shortcut and not do the data work.
5 years ago the FOMO was from Hadoop. Hadoop Here, Hadoop there...people just forgot you have to actually collect a lot of high quality data to make Hadoop useful. It's AI now.
Most of the failed projects basically were "Hire a Data Scientist to make a magic model" and nothing else was supported. Basically, "Here's some data, do some magic." I read somewhere that 90% of data is low/zero value, and I'd agree with that.
There's absolutely a place for people doing pure statistical or ML/Deep Learning modeling. But the rest of the organization has to support them so they can have that narrow focus. A lot of places want to take a shortcut and not do the data work.
5 years ago the FOMO was from Hadoop. Hadoop Here, Hadoop there...people just forgot you have to actually collect a lot of high quality data to make Hadoop useful. It's AI now.