Tag: data analysis

I know quite a few people that are jumping on the data science bandwagon, brushing up on statistics, learning R, and hoping to land one of those lucrative data scientist positions. I wish you good luck, and some of you will succeed, but I wouldn’t count on there being lots and lots of data science positions in the future. The work is hard, and it will take a lot of learning and practice to become good. Plus, perhaps we won’t need as many data scientists as some are predicting.

There seems to be a lot of work being done with intelligent agents and “bots” that can perform some lower level analysis of data and interact with humans. However, there is also work being done with more capable software that might analyze data and find patterns by applying different algorithms and analysis to make it easier for business analysts to comb through the mountains of data. After all, the growth of IoT, of more metrics and measurements being made in all kinds of applications mean that we can’t just throw bodies at problems. We need better ways to work with large amounts of data.

This work isn’t really aimed at eliminating data scientists or data analysts. Rather, this is aimed at trying to free up their time to do the creative, thoughtful work of examining data. The grunt work of playing with algorithms and testing them according to some measure of success is handled by software. I think that this is a mantra that will be repeated over and over in the next couple decades in computing. Grunt work is automated, thoughtful work is done by (well-paid) humans. If you’re not doing the latter, you might not have a job with the former.

I do think there is still plenty of opportunity for data professionals, after all, we need to manage all that data, find ways to ensure it’s available and can be queried, data is somewhat clean, and certainly that we have some idea of what the data means to our business. I’m not sure software agents will easily be able to set those things up, though certainly they’ll do the work of applying our rules to large sets of data.

There will still be plenty of data science roles, and certainly humans need to be watching the software to be sure it’s working well. There will always be tuning efforts, and perhaps always reports that users need help in building. However, as software becomes more sophisticated, there will certainly be less grunt work in our industry, just like many other industries have seen lower, or at least lower mid-level positions reduced in number. We should be prepared to prove our value and ensure our skills are useful to our organizations.

Steve Jones