Featured Employee: Trending in Tech – What Data Scientists Are Watching

We asked Andrew Voegtline, our Manager of Data Analytics and Insights, to give us his perspective on some trends in his field to watch.

In 2000, Marc Randolph and Reed Hastings, then co-founders of a little DVD-by-mail service known as Netflix, met with John Antioco, CEO of Blockbuster. Buy Netflix for $50 million — that was the offer. Antioco declined. Actually struggled not to laugh, as Randolph remembers it. 

While far from a booming business at the time, Netflix’s leaders were ahead of the trend that was pulling away from the physical movie rental experience and toward digital  — even if internet speeds were not yet fast enough for on-demand downloads. 

Today, only one Blockbuster video rental location exists. It’s in Bend, Oregon, and it attracts tourists seeking early-2000s nostalgia. Of course, it’s not enough to bring back the brick-and-mortar movie rental business. While there were many factors behind Blockbuster’s fall, it’s clear the company ignored the dot-com trend at its peril. Blockbuster failed to innovate and was left behind in the physical world. 

Netflix, on the other hand, embraced early tech trends to evolve into the streaming platform it is today, leveraging user data to offer personally curated titles ideal for endless binge-watching. 

Trends in technology and data science emerge quickly, and keeping abreast of these shifts and evolutions should be a priority for every tech professional. Those who are able to identify promising ideas quickly, help their teams innovate and lead the pack in pursuing an industry trend are better able to secure their company’s future. That’s why Built In Colorado sat down with three data scientists to learn about the tech trends they’re keeping a close eye on right now and the impacts these innovations might have for the future. 

What’s one data science trend you’re watching right now?

The adoption and general acceptance of easy-to-set-up machine learning services from AWS and other players is a trend I’m following. The expectation around high-quality data products is maturing across a wide range of industries, not just the historically data-hungry world of tech. Data is being viewed less as a way to reactively report on the past and more as a way to identify and prescribe opportunities in the future. 

These concepts aren’t new, but as more companies see the value of deeper dives into their data, these AI and ML services offer quicker results and greatly reduced barriers to entry into the predictive space.

What influence will this trend have on your industry? 

My initial gut reaction — and what I imagine is likely a natural first reaction for a lot of data professionals already building and tuning their own models — was to distrust these services. While I do think it’s healthy to distrust new tools until they’re proven useful, I also see a resource-saving balance being struck. 

We will need to have a pragmatic mindset to determine when these off-the-shelf solutions are well-suited for the task at hand and when a more bespoke approach is warranted. This requires folks who work in data to not only have a grasp of the “how” when working with predictions, but also become knowledge experts with a deep understanding of the “why.”


  • Become the data liaison for stakeholders who are eager to incorporate these new tools and communicate what can be accomplished given the state of the organization’s data.
  • Develop the ability to explain the model to a nontechnical audience.
  • Push for increased organization-wide data literacy.

Original article written by Kimberly Valentine, click here for the entire article.