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Bringing This Century's Most Transformative Field To The Masses

Arya Eskamani, Manager, Data Science and Principal Data Scientist

You may have come across the term Data Scientist: the combined business, technology and mathematics profession dubbed by Harvard Business Review as the “sexist job of the 21st century”. Job openings for Data Scientists, with complementary positions like Machine Learning Engineers and Big Data Developers, have seen tremendous growth. Companies are seeking to expand their analytics arsenal with tools, technologies and skills that can transform data into competitive assets.

While demand for these roles has expanded exponentially, the necessary skills and experience required can be difficult to attain or be identified through recruitment. Of current data science professionals today, roughly 75 percent have a Master’s degree or higher in specialized fields of study including (but not limited to) Computer Science, Statistics and Mathematics, Economics, Physics and Engineering, far from being representative of the general workforce. From our own hiring efforts at Royal Caribbean Cruise Lines, I can tell you even fewer have the domain expertise and communication skills necessary to be successful.

Companies should nurture these capabilities internally to bridge this gap and keep their analytics arsenal strong. Making data science easy to learn, execute, and collaborate on intensifies the velocity of experimentation, which generates faster and better quality insight across an organization. Drawing from my own experiences, here are some ways you can grow data science disciplines within your organization.

  ​In an environment where data is the new oil, betting on and investing in your data nerds is a great way to internally develop analytic talent   


Gamble on your geeks

Recognizing potential and making calculated risks is a hallmark of good leadership. After graduate school, I worked for a healthcare technology start-up as a Project Manager, though did very little in data science. Sensing my passion for data, my bosses took a leap of faith and tasked me with developing the company’s analytics offerings…we later won our first data science contract with one of the largest biopharmaceutical manufacturers globally that same year.

In an environment where data is the new oil, betting on and investing in your data nerds is a great way to internally develop analytic talent. A first step is identifying employees who are heavily involved in data analysis, show interest in learning data science, and who the business feels would yield the most benefit with newly acquired skills. Enrolling them in online learning platforms like Udacity, Udemy or Coursera, ideally in groups for synergetic effects, is a great way for them to learn at little monetary cost.

Alternatively, if you already have data scientists in your organization you can have them create a curriculum or short tutorial and lead training sessions. You can also consider outsourcing your data science training efforts to a firm or consulting service that provides data science instruction on-site or remotely.

Empower the ‘citizen data scientists’

There are analytic professionals adding tremendous amounts of value without having to write a single line of code. While these folks are likely not suitable for exotic or advanced projects, there are tools and technologies which can enable your citizen data scientists to perform basic yet impactful tasks in machine learning and data science. These can be applied to their own projects or working together on larger projects with more advanced data scientists.

An excellent tool to explore this avenue with is KNIME: an open source data analytics platform featured as a leader in the 2018 Gartner Magic Quadrant. While a bit clunky to use at first, the ability to build an analytics pipeline within a drag-and-drop visual canvas at zero cost is an attractive proposition. Furthermore, this enables citizen data scientists to focus more on the application and understanding of the models they are building and less on development.

While open source tools like KNIME are low cost, lack of support can slow down productivity and create headaches as your analytics needs mature. Proprietary tools, some also featured on the Magic Quadrant, typically offer better support and usability in a visual interface. There are various price-points to balance their cost against their capabilities and the needs of your organization.

Don’t pass the torch but share the flame

I once read that a candle can share its light with another candle and be no-less bright, but the illumination benefits everyone… that pearl of wisdom is true of data science teams and their capabilities as well. Encouraging and proliferating data science teams beyond a core group benefits the organization as a whole.

Techniques spread across your workforce enable them to accomplish existing tasks more efficiently, unleashes their potential for more advanced analytic tasks, and frees capacity from the core data science team to focus on even more complex projects and research. It will also improve the quality of work out of the core team since they now must answer to the scrutiny of those who know their craft more often.

Finally, it’s a superb way to establish relationships between the core team and external members since they share a common, data-centric language. The most engaged stakeholders of Royal Caribbean’s Data Science team are those that have been through our sponsored training sessions or have had the opportunity to question our work as equals after a knowledge transfer. Some of our most exciting projects have been sourced externally from other departments because they have team members who understand machine learning. The dissemination of data science skills didn’t decrease demand for the core team but continues to increase it!

Creating an environment where ideas are freely shared is perhaps the most important component, but each action has its own unique and reinforcing effects and should be considered as part of a holistic analytics strategy.

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