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 different purchasing affinity models you created a long time ago really need to be updated.
- Your manufacturing department keeps asking for regular demand forecasts to adjust the production planning.
- Colleagues in the finance team are often reaching out to you, looking for any interesting patterns your cash-flow forecast might have found.
- And the successful prototype you have just created still needs to be deployed into production. Orchestrating all required components into a stable system that can be maintained and supported, complying with internal governance requirements, is no easy feat.
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.
Automated Predictive Analytics—Your Answer to the Time Crunch
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
th 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.
Automated Predictive Analytics from SAP
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.
- Gatekeeper: Have a brief look initially with automated predictive to get a feeling for whether there appears to be any useful pattern in the data. If automated predictive analytics doesn’t find any pattern, you’ll have to try hard to find something useful. Failing often is okay. Failing early is better.
- Feature engineering: Not sure which features to engineer? Even out of a few existing columns often hundreds if not thousands of variables can be derived. Automated predictive analytics can help create a very broad view on the data. Either as a view or physically persisted. You’re still free to decide which features to work with later in Python or R.
- Feature selection: Hang on, maybe your preferred environment is not too comfortable with extremely large datasets? Automated predictive analytics can point out which features it considers most useful for your task.
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.
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