By Mary McGuire (@Badgerpolo)
Everywhere you turn these days, you are experiencing some flavor of data science. Whether it’s when you’re shopping on Amazon, driving your car, or at work (especially if you work for a tech company like me!) – Data science is all around us.
For those of you who aren’t familiar with data science, I wanted to take a few minutes to share a bit more about what it is and how it impacts Sales Enablement Technology.
I had the pleasure of sitting down with SAVO’s own in-house data scientist to learn a little about his career path and some insider info.
What is Data Science?
At a high level, data science involves using data and statistics to solve business problems.
Raj Bandyopadhyay does a great job of blowing out the definition further in his post on data science:
“The power of data science comes from a deep understanding of statistics and algorithms, programming and hacking, and communication skills. More importantly, data science is about applying these three skill sets in a disciplined and systematic manner.”
What is a Data Scientist?
Like the name suggests, a data scientist is a person who analyzes and interprets data.
Data Engineer, Josh Wills, defines the role of a data scientist nicely:
Data Scientist (n): Person who is better at statistics than any software engineer and better at software engineering than any statistician.
Data Science Encompasses AI (Artificial Intelligence), Machine Learning, Predictive Analytics.
Many of us have heard these buzz words but may not be sure what they mean.
We believe that everyone uses these terms a little differently, which is part of the problem. At a high level the best way to think of each of them is:
- AI: Intelligent machines. With AI, the ultimate goal is generalized intelligence. If you’ve ever seen the movie Her, you’ve seen AI in action!
- Machine Learning: A particular approach to AI. In it, a machine using an algorithm or algorithms, learns without having to be programmed explicitly. It’s when a system is built so that it trains itself to get better and better.
- Predictive Analytics: Using data from past events to best predict what might happen in the future.
What are the key ingredients in a successful Data Science program?
If you don’t have the data, you can’t even get started. Once you do, you need the right people to look at it the right way and determine what can be done with it. In general, the more and the better data you have, the more successful your efforts can be.
Much of the work is around helping people within the business figure out what the “problem” is, and then using data to solve it as best you can.
How does a person get into Data Science?
As with most careers, everyone’s path is unique and different.
Data scientists have very diverse backgrounds, in our case it started in healthcare working with researchers and physicians to quantify factors influencing health outcomes in different people (think studies of drugs, medical devices, and patient education programs). So when you hear stats about your health, that’s data science at work!
How does Data Science impact Sales Enablement technology?
Sales Enablement is all about serving up the most relevant content to sellers at the right stage of a deal. So it’s all about findability. Getting the right content, in front of the right reps, where content can be sales decks, training, coaching, subject matter experts – any form of information that sales need. It also involves getting managers the insight on who is using what, when and how.
Incorporating data science into sales enablement technology enables a new channel for getting that right information in front of the seller, at the right time. Predictive content recommendations shouldn’t replace prescriptive content recommendations put in place by your sales enablement teams, they should continue to build on them – further enhancing what your sellers are consuming, which ultimately impacts your buyers’ experience.
What’s the most interesting thing about being a data scientist at a Sales Enablement company?
A data scientist’s work can differ greatly depending on the type of technology they’re working on.
At SAVO, for example, the data comes in the form of images and text instead of numbers, because the content (pitch decks, sell sheets, videos, etc.) is considered unstructured data. This adds a layer of complexity that is fun to solve.
I learned more in my short sit-down with our data scientist than I expected to, and I hope you learned something new as well!
If you’re interested in learning more about Sales Enablement, check out our eBook: Unlock the Power of Sales Enablement.
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