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Big Data and AI: Toronto conference highlights the need to focus on searching for the elusive unicorn of data science talent

By 19 June 2018 No Comments

By Amar Vivek, ICTC’s Data Analyst. He helps the organization strengthen Canada’s digital advantage, by providing actionable insights using data analysis skills. Amar has approximately 10 years of experience in the IT sector, with expertise data warehousing, ETL and Big Data technologies. He has keen interest in emerging technologies such as Machine Learning, Artificial Intelligence and Deep Learning.

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From June 12th-14th, Toronto played host to Canada’s biggest Data and Analytics Conference. With world-renowned companies like IBM, AWS, Deloitte, Microsoft and SAS in attendance and over 4,000 participants, the conference highlighted the notion that developments like Big Data and artificial intelligence (AI) will continue to be driving forces across the global economy. The 2-day conference showcased both technical and practical verticals for Big Data and AI, including use cases on Predictive Analytics, Data Visualization, Advanced Machine Learning, Digital Transformation and more. Bringing to the table a diverse set of insights and backgrounds, the speakers ranged from leaders in this space such as IBM, Microsoft, and SAS; as well as emerging start-ups like GoldSpot Discoveries, Dessa, Modiface and many more that are already making waves in this space.

While their experiences ranged, one thing that all of the speakers had in common was a focus on the increasing need to use data.  Similar to how we as humans need oxygen and water to survive, increasingly, all companies will need to harness and maximize their data to remain competitive.

The Increasing Importance of Big Data and AI in Business Operations

“The business plans of the next 10,000 startups are easy to forecast: take X and add AI” – Kevin Kelly

In his keynote speech Matthew Fritz, Director of Data Science at Samsung Electronics, emphasised the importance of prescriptive engines and their capacity to optimally and precisely make the best decision on a business’ behalf. Underlining the meaning behind this development, Fritz asserted that Deep Learning has the opportunity to unlock NBDB (Never Been Done Before) opportunities for businesses across sectors.

However, while the potential of technologies like Deep Learning are immense, many companies suggested that finding sufficient talent that can uncover this potential is a challenge. Today, the search for skilled AI and data science professionals can be something like hunting for a unicorn. While Data Science teams tend to be multidisciplinary, they often possess three key characteristics and in-demand skillsets: Programming Skills, Statistics expertise, and Domain expertise. The proverbial “unicorn” is someone who has knowledge of all three and, as the metaphor suggests, this type of individual is anything but easy to find in today’s competitive environment.

What does this mean for Data Scientists?

The shortage of these “unicorns” is not only heightening the demand for talent with data analysis skills, but is also functioning to create a demand for a new role in this field: the “Citizen Data Scientist”. A recent Gartner report defines a citizen data scientist as “a person who creates or generates models that leverage predictive or prescriptive analytics, but whose primary job function is outside of the field of statistics and analytics.” While a bit of an anomaly, citizen data scientists source and organize data and then initiate the analytics journey, however much of their success is dependent on the further integration of AI and big data into analytics.

Despite AI’s infinite capabilities, it is increasingly imperative for organizations to understand the basic ways in which AI can support or enhance their business processes. This is something that was made very clear by Carrie Shaw, Chief Product and Marketing Officer at Quandl during the conference.
Referencing something called “alternative data”, Shaw spoke to its relevance, using an example of how it can provide insight into the investment process. With alternative data coming from varied sources such as sensors, mobile devices, satellites, public records and, of course, the internet, large batches of data can be collected about an investment company (data that is not officially published by the company but can be derived through these alternate sources). As a result, collecting and analysing this data can provide unique, specific and timely insights into a number of investment opportunities. This is where the citizen data scientist comes in: possessing a wide range of advanced analytics skills, and using those skills to uncover insights, trends and value.

Big Data and AI: Accelerating the demand for talent

The conference also touched on other important topics including the need to develop proper regulations and standards around AI to avoid malpractices. While some initiatives surrounding regulation are already underway, one thing was clear, and immediately so: the increasing rise and use of AI across verticals will inevitably accelerate the demand for digitally-skilled talent with the ability to source, analyze and understand the value behind that data. And with an anticipated shortage of 216,000 digitally-skilled workers in Canada by 2021, investing in initiatives and policies that focus on growing the supply of this talent is key.

The opportunities behind AI are immense, and in many ways, unlike anything we have seen before. With the capacity to change and revolutionize everything from a specific sector (like finance, for example), to our systems of governance, the opportunity to “Take your “X” and add AI to it” is an increasing possibility.

Canada has a significant role to play in this space, and with developments like the kickoff of the SCALE.AI supercluster in Quebec, or even the set up of DeepMind’s first lab outside of the UK in Edmonton, we are quickly moving towards not only participating in this fast-emerging field, but leading it. Of course, other factors like investment in R&D, or global economic trends undoubtedly play a central role in our voyage; however, the demand for talent with the relevant digital skills to steer this journey is a clear and impending reality.

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