No doubt you have heard data science success stories of some of the world’s most innovative companies. Whether it’s Google’s self-driving cars, Amazon’s employee-less stores, or the classic story of Target who predicted a woman’s pregnancy before she even told her own family, machine learning and data science are changing the way we do business.
But, with all of the hub-bub around the power of big data and predictive models, it might feel like these tools and techniques are out-of-reach for the average small or mid-size company. It might be challenging for you to believe that your company, regardless of where you’re at in on the continuum of analytical maturity, is ready to start using data science right now.
Well, CHALLENGE ACCEPTED! Today, I am going to give you 3 reasons why your company is ready to implement and begin benefitting from data science RIGHT NOW.
1 – It’s doesn’t have to be that complicated
Yes, data science is an incredibly powerful field. Sure, techniques like random forests, neural networks, and deep learning are pretty difficult to understand. However, you do NOT have to turn to complicated modeling techniques to yield massively powerful results! Rather, some of the most well-respected data scientists who have written some of the best-performing models agree that, many times, more straight-forward techniques can yield incredible results.
Now, I’ll admit, we in the data science community have a tendency to prattle on and on about the nuances of our field, but you should not let that scare you away from joining the party.
At Sprocket, our goal is to help you implement the right model for your needs and help you understand how it’s working under the hood. Want to conceptually understand how multiple regression models work? Give us a pen, a napkin, and 2 minutes. We can get you there. It doesn’t have to be that complicated.
2 – It’s not that expensive
Let’s cut right to the chase on this one. Gone are the days when your only options were to rely on traditional hardware for data storage and expensive analytical tools to analyze it. Cloud-based storage solutions like Amazon Web Services and open-source (i.e., FREE!) software like R and Python have virtually eliminated cost as a barrier to entry.
We Sprocketeers like to describe ourselves as “tool-agnostic.” This means, if you already have an existing database with an enterprise SAS license, we’re happy to work in that eco-system. However, if that doesn’t describe your situation, you shouldn’t let the fear of the cost of implementing a set of tools keep you from taking advantage of data science! It’s just not that expensive.
3 – It’s not that difficult to implement
The words “machine learning” conjure up visions (nightmares) of large-scale IT projects in order to implement a model. Similar to my case about the varying degrees of complexity of data science techniques, your implementation process can vary widely in difficulty. And, dare I say, it might even be down-right easy.
I’ll give you one concrete example. My best-performing predictive model accurately predicted an outcome-of-interest about 95% of the time. The implementation of this model amounted to my client dropping a glorified algebra equation into their existing data scripting process in order to implement it. Yup, that’s it. It doesn’t have to be that difficult to implement.
So, how did I do?
Are you convinced yet? Are you ready to join the data science party? Want to chat a bit about your specific situation? We would love to talk to you more about how you can take advantage of the power of data science to create your own success story for your business. Click here to contact us; we’d love to see what we can create together!