Artificial intelligence is no longer just evolving nomenclature in IT. With the technology’s recent progress, organizations of all shapes and sizes are taking interest. With the mainstream press and industry analysts from every corner weighing in, it is worth taking stock of the technology and learning how to differentiate between three arguably over-hyped terms: machine learning, artificial intelligence (AI), and deep learning.

It’s best to consider the concentric model depicted in the figure below. AI is shown as the superset since it was the idea that came first, and it has been evolving and expanding since then. A subset of AI is machine learning, which came out of the quest of AI at an early stage. The innermost subset is deep learning, which is just one class of machine learning algorithms. Deep learning is a hot area right now, and the one most typically associated with the rise in AI today.

A guide from the head of HR at a leading analytics company

Data scientists are in higher demand than ever before. According to the latest CrowdFlower survey, 79% of respondents reported a data scientist shortage in 2015. In 2016, that number grew to 83%. The race is on to find skilled people who can organize, structure, and make business sense out of Big Data sets. People with heavy STEM, analytics, and conceptual skills, and the attendant work-friendly personality traits (insatiable curiosity, ability to prioritize, and a healthy dose of skepticism, to name a few), can virtually write their own tickets.

Everywhere you turn, both business and IT talk about data science. But there’s also trepidation about how to get started, especially in the context of attaining an organization’s business goals and objectives beyond the realm of lab or departmental experimentation.

“Didn’t you just go to a similar Big Data conference recently?” my wife asked me. “How much could have changed in a few months?” I was hesitating about attending another conference in a short time span. My wife is right about most things, but in this case, I am glad I didn’t listen and went anyway. I learned about many new advances in both commercial and open source tools and across the whole technology stack: new hardware, in-memory databases, and new-and-improved tools.

Everybody seems to be talking about machine learning these days, and a quick check of Google returns 21.2 million search results for the term. Clearly, this is a popular topic. Yet the term “machine learning” can have many different meanings, depending on the context in which it is being discussed. It is also associated with an equally lengthy list of data science techniques and technologies. Business leaders often feel overwhelmed by this rather bewildering array of terms, analytical approaches, and technology solutions. There are hundreds of algorithms, with new variants seemingly appearing every day. Researching them online does not seem to clarify the choices or point to an obviously superior decision, as most articles target deep experts and revolve around nuances of a particular model or open source package. As a result, business leaders can be reluctant to adopt something they don’t fully understand, resulting in missed opportunities.

The struggle is real — and it’s becoming increasingly apparent to companies that have dipped their toes into popular data science tools. As enterprises test the limits of their new tools, old technology, and data scientists’ time, their infrastructure is starting to show its cracks. Read on to see how these issues are revealing themselves — and more importantly — gather some ideas on what to do about it.

Over the past year, I have been averaging 2–3 customer meetings per week, resulting in over 100 customer and partner conversations around Big Data, analytics, and data science for the enterprise. From these conversations, I have found one key recurring theme: scale. Large enterprises no longer want to build one model quickly or implement just one use case in production. They all struggle with a large backlog of ideas. They need a way to rapidly turn these many ideas into real use cases that deliver tangible business value.

However, many companies simply can’t find a pathway to make this happen. Across my numerous conversations, I noticed very similar patterns and identified 5 common obstacles that can prevent companies from achieving scale for data science.

Most companies realize they are sitting on a treasure trove of customer data that has the potential to deliver tremendous business benefits; however, most also have no idea how to realize those benefits. How can companies use their data to bring in more customers, increase the amount they spend, and make them more loyal? How can companies use data to turn unhappy customers into loyal champions of the brand? And perhaps most important, how can companies use that data to drive a significant increase in revenue?