Insight Bytes is the official MRIA blog, capturing data, insights, and stories by research professionals from across Canada.

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  • 03/05/2020 5:09 PM | Dan Jackson (Administrator)

    By John Stackhouse

    AI may be the new flu: everyone knows it’s coming but no one thinks it will hit them next.

    In other words, no need for that AI flu vaccine.

    In a new Gallup survey for Northeastern University, 61% of Canadians and 71% of Americans think artificial intelligence will eliminate more jobs than it creates. Roughly half of those respondents think AI will also decrease the number of good jobs out there.

    But the vast majority also think they’re immune to automation.

    Despite a general concern about the impact of AI, only 37% of Canadians and 17% of Americans think their job will be eliminated because of smart technologies.

    The Gallup results were presented at a recent Future of Work discussion at Northeastern’s Toronto campus, put on by the Ontario Chamber of Commerce and RBC.

    Helena Gottschling, RBC’s global head of human resources, told the forum that in financial services, automation is leading to new and different jobs – and a changing mix of skills – which means an ever-growing need for lifelong learning.

    More than half of RBC’s 55,000 Canadian employees are enrolled in a digital learning program, often offered in the form of mobile and modular programs for skills ranging from digital troubleshooting to communications.

    In the Gallup survey, 92% of Canadians (and 95% of Americans) said they want some form of ongoing learning through their careers – not surprising given that two thirds of them worry their skills will be outdated within a decade. 

    Despite those worries, few know what skills they’ll need to thrive in an AI-powered economy, but there was strong agreement across Canada, the US and Britain that people will need so-called soft skills like teamwork, communication, creativity and critical thinking.

    Gottschling agreed with that assessment, saying “the shelf life of tech skills will be shorter, while the shelf life of human skills will be just as long.” 

    A greater concern in the study was a lack of confidence in universities -- for Canadians as well as American and Britons – to prepare graduates for the jobs of tomorrow. A rating of high confidence was lowest (3%) in the US.

    A plurality of respondents viewed employers as best-equipped to provide career-long education and training.

    Gottschling said most employees need solutions that are affordable, time-efficient and relevant to their ambitions. And then there’s mindset. Do people have the will and skill to learn?

    Author: John Stackhouse is the Senior Vice President in the Office of the CEO at RBC. 

    Article source: LinkedIn Pulse

    Image credits: Arundati Dandapani at Distillery District's Christmas Market in 2018

  • 03/05/2020 4:58 PM | Dan Jackson (Administrator)

    By Robert Vagi

    If you are like me, you’re probably reading this article because of the buzzwords in the title. Even though everyone seems to be talking about predictive analytics, machine learning (ML) and AI, definitions for these emerging fields can be hard to come by and, more importantly, few people have a clear vision for how they can benefit their businesses. In this post, we will bring clarity to this topic and give you some ideas for how these emerging fields can be used to benefit your business. 

    Starting at the Beginning: Data Analytics

    We can’t have a conversation about machine learning and AI without first talking about data analytics. This is because, to some degree, both machine learning and AI either are based on data analytics or actually are data analytics. So, what do we mean by “data analytics?”  For the purpose of this post, we define data analytics as deriving insights from data – specifically, from data that is collected via means other than “traditional” market research. This could include purchase history, customer feedback on third party websites, and so on. As you can see, this casts a wide net – nearly any analysis can be considered data analytics.  So, where do predictive analytics, machine learning and AI fit in?

    Meet the Twins: Predictive Analytics and Machine Learning

    In An Introduction to Statistical Learning, the authors argue that analyses are done for one of two reasons: inference and prediction.

    What is the purpose of your analysis?

    Analysis for Inference

    • Focuses on describing relationships
    • Helps us understand how the world works
    • Example: Using ad spend and sales data to identify the most effective ad channels

    Analysis for Prediction

    • Focuses on knowing something that’s not currently in the data
    • Helps us understand what something is or what will happen in the future
    • Example: Predicting the likelihood that a customer will renew his or her subscription using historical transaction data

    The reason predictive analytics and machine learning are twins is that both are used for making predictions. In fact, when people refer to predictive analytics and machine learning, they’re often referring to the same thing (though sometimes not – which we’ll explain shortly). Predictive models can take many forms ranging from regression techniques, to clustering algorithms, to more sophisticated models like neural nets or boosted models.  In short, all these analytics tools are used for knowing something that isn’t observed in the data.

    Where predictive analytics and machine learning can differ are in the specific techniques that people refer to when using these terms. For instance, when speaking of predictive analytics, people are often referring to more traditional, though potentially less accurate, techniques like regression or time series analysis. Machine learning, on the other hand, typically refers to more recent developments in predictive modelling like boosted models or neural nets. These techniques are more complicated and require more data, but are also more accurate than traditional methods.

    The Machines are Alive! Artificial Intelligence and Machine Learning

    The definition of AI is the topic of much debate. However, for the purpose of this post, we’ll define AI as the process of teaching a computer to learn from data and act based on what it’s learned – in essence, to learn and act with minimal human intervention.  In most cases, this process is driven by predictive/machine learning models.  Thinking back to the example of whether someone will renew a subscription, if you want your online systems to identify customers that likely won’t renew and then automatically send them a special offer, you would want to look into an AI solution.

    Applying it to Market Insights: Choosing the Right Approach

    So which one do you need? The answer is found through understanding:

    1. Why are you conducting the analysis? and

    2. How will the results be used?

    To this end, here are some guidelines for each: 


     Good if…

    Data Analytics (analytics for inference)

    • The goal is to make large-scale, strategic decisions
    • You are at a phase in a project where understanding the problem is key

    Predictive Analytics/Machine Learning

    • You need to understand what will happen in the future (i.e. project revenue, customer turnover, etc.)
    • You want to determine a characteristic of your customers that is not currently measured in your data (i.e. segment membership, phase in purchase cycle, etc.)
    • You have a good understanding of the problem and want to act strategically to promote a specific future outcome for individual customers (e.g. increase the probability of a sale, decrease the likelihood of leaving/churn, etc.)

    Artificial Intelligence

    • You have a good understanding of the problem, you have a large quantity of data, and you want to automate the process to save cost or reduce time spent on the task

    Do you have a data problem that you are struggling to solve? Feel free to reach out to us at robertv@phase-5.com.

    Robert Vagi, PhD, is Phase 5’s lead data scientist, based in Minneapolis, USA. Drawing from his background in both quantitative research and education, Rob is passionate about helping clients use data to tackle their most challenging business problems.

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