By ‘statistically speaking’, I mean that most companies today have no absolutely no need for machine learning (ML). The majority of problems that companies want to throw at machine learning are fairly straightforward problems be ‘solved’ with a form of regression. They may not be the simple linear regression of your Algebra 1 class, but they are probably nonetheless regression problems.
That’s huge and something that many companies forget (or ignore) when working with their data. Without proper data quality, data governance and data management processes / systems, you’ll most likely fall into the Garbage in / Garbage out trap that has befallen many data projects.
Now, I’m not a data management / data quality guru. Far from it. For that, you want people like Jim Harris and Dan Power, but I know enough about the topic(s) to know what bad (or non-existent) data management looks like – and I see it often in organizations. In my experiences working with organizations wanting to kick off new data projects (and most today are talking about machine learning and deep learning), the first question I always ask is “tell me about your data management processes.” If they can’t adequately describe these processes, they aren’t ready for machine learning. Over the last five years, I’d guess that 75% of the time the response to my data management query is “well, we have some of our data stored in a database and other data stored on file shares with proper permissions.” This isn’t data management…it’s data storage.
A small minority of the organizations I’ve worked with do have proper master data management processes in place. They really understand how important quality, governance and management is to good data and good analysis. If your company understand this importance, congratulations…you’re a few steps ahead of many others.
Go right ahead. There’s nothing stopping you from diving into the deep end of ML / DL. There is a time and a place for machine learning…just don’t go running full-speed toward machine learning before you have a good grasp of your data and what ‘legacy’ approaches can do for the problems you are trying to solve.
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