If you’re a data scientist, what do you enjoy working on most? Going by my own experience and what I’m hearing around me, chances are you enjoy being creative! Sinking your teeth into a business challenge, trying to find a way for data science to help solve a problem. Obtaining data that might be helpful, getting a feel for the data, and trying many different ways of squeezing the most value out the data. And you may even want to obtain more data, and try different approaches to get the best possible insight for the business challenge. That’s fun—and you may well be using many different tools in the process.
Once you’ve cracked the nut, what do you want to focus on next? Let me guess…you probably want to work on the next topic that poses a similar challenge.
But how does that match with reality? If you can really pick and choose to work only on such interesting projects, congratulations to you! Often the day-to-day reality gets in the way.
The list probably goes on. There is never enough time in the day. There is never going to be sufficient manpower to look after all requirements that come to you or that you would like to propose as a new project yourself.
You’ve guessed it, this is where automated predictive analytics comes to the rescue. It won’t replace you as a data scientist. It’s another tool in your box that is supporting your productivity! Automate where possible and spend your time on the cases you believe will benefit most from your personal time and effort.
Let the recurring requirements be dealt with automatically. Don’t waste your time by retraining the churn model for the 20 time. There is no need to manually forecast every single’s product quantity every month by hand. It is not fun to keep doing the same things over and over. And for a data scientist, it is typically not much fun to deploy and scale a prototype into production either.
The framework of our automated predictive analytics is designed from the ground up to automate the training, re-training, and application of predictive models all the way to operational deployment into business processes. With the recurring requirements looked after automatically, you’ll be able to add even more value to your business. You can focus on new challenges and spend less time on manually re-adjusting existing models or trying to bring them into production.
You want to work on a high-value case by hand in Python or R? Sure. Go ahead. And even here, automated predictive analytics can support you.
Hopefully that sounds appealing, but you aren’t convinced yet? Then I’m guessing that you’re thinking of this as a “black box,” which might give you an odd feeling. Please have a look at this blog, in which we peek into what is happening inside that automated predictive process. It’s not just an algorithm. It’s a very comprehensive framework that adjusts itself to the data.
So paradoxically, automated predictive analytics can increase your productivity and the value that you can add to the organization, whilst allowing you to spend more time on the cases and tools you might be most passionate about.
Yes. More fun for data scientists—with automated predictive analytics!
It’s time for a closer look.