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Future of Data, Analytics and AI

Welcome back to our Insight Centre!

We had a great time Tuesday night discussing the future of Data, Analytics, and AI in the next 5 years, and we wanted to share with you some of the insights given by our panelists.

First, let us introduce the panelists:

Panelist at Data, Analytics and AI Event

He is a Certified Data Management Professional (mastery level), Certified Google GCP Data Engineer, holds an honours B. Math in Computer Science from Waterloo, and has over 23-year experience in software, IT, and data management. Neil has deep expertise in Agile Analytics with 50 proof-of-concepts across over 10 industries.

He has spoken about Analytics and Information Management in numerous public forums in the US and Canada including: Enterprise Data World; FSOSS (Free and Open Source Software Symposium); CMA IT Symposium; Mobile Data Collaboration Conference; Qlik Connections; and numerous universities including University of Toronto, Waterloo, Western, Wilfrid Laurier, Ryerson, Trent, Brock, Queens, Windsor, and McMaster.

Most recently Neil has launched a new podcast about the human and technology dynamics surrounding Analytics and Information Management.

Joseph's primary role is to develop pricing models by leveraging advanced analytics based on a combination of transactional data, survey data, and other available data sources. Joseph has experience in developing predictive models, designing R shiny tools, and implementing pricing strategies.

He has earned a Master in Science degree specializing in Business Analytics from Ivey Business School, and earned a Bachelor of Business Administration degree from Wilfrid Laurier University.

After the recent acquisition of, Darren has worked with the Start-Up to bring their AI solutions to TD. The tasks that Darren has most recently been working on include Deep Learning, AnalyticsOps, Distributed Computing, Hardware Configuration for AI, and much more. Because Darren is an Enterprise Architect, he is helping all lines of business integrate AI and digital solutions into their traditional business models. Darren has developed an AI powered application using TensorFlow and the Elastic Stack to change the ways of traditional Retail Banking. This solution is currently Patent Pending. Darren's passion is finding out where he can add a flare of AI into any business problem.

He has earned an Honours degree in Science and Computer Science from the University of Toronto, and a Master of Science specializing in Business Analytics from the Ivey Business School.

Dr. Maidens is currently an Assistant Professor in the Department of Mechanical and Industrial Engineering at Ryerson University and a member of Ryerson's Data Science Lab. He also serves as Chief Data Scientist at Eko Devices, a Silicon Valley based medical technology company, where he builds AI to automatically assess heart health.

He is broadly interested in control, optimization, signal processing, statistics and machine learning. His research focuses on developing scalable algorithms for the optimal design of experiments, safety verification in dynamical systems, and inference in time series data. He has applied these algorithms to solve problems in medical imaging, automated anesthesia, and cardiology. He received his PhD in Electrical Engineering and Computer Sciences from UC Berkeley in 2017.

Here are the insights from our speakers:

As we approach the era of increased automation, a large number of us are starting to worry about job security, as AI might be able to replace them. However, our experts believe that we should not fear this, but see it as an opportunity for innovation and creation of new and more valuable, knowledge driven jobs. AI and analytics might just take all those boring, repetitive, tedious tasks out of our hands and let us focus on the more thought provoking and creative decision-making processes.

Though the adoption of new technologies is on the rise, many companies and organizations risk falling behind. In fact, Darren said that since the acquisition Layer 6, TD started to use the copious amounts of data they collect for value driving purposes. The financial services sector is one that is skeptical of new, emerging technologies and one thing yet to be adopted is cloud storage. Banks don’t seem to have enough confidence in them to store their highly sensitive information.

In the healthcare industry, things seem to be moving a little faster. Dr. Maidens who works at Eko, a Silicon Valley company that specializes in facilitating the fight against cardiovascular diseases by using devices and machine learning, said that the company has collected heart sounds from all around the world to learn patterns and detect potential illnesses and diseases that are not detectable to the human ear. Eko devices, including an electronic stethoscope and a ECG device, might be able to replace a sound examination of the heart and lungs into something much more sophisticated. This could help millions of individuals detect diseases and treat them when it’s still possible. Now, the questions that need to be answered are “how are we going to regulate this?” and “how do we let computers make safer decisions?”

In pricing prediction realms, Joseph said that the pricing was very behind when it comes to AI, one example is the restaurant industry. In fact, sometimes clients don’t even have data or dedicated teams who deals with the data. This makes their work valuable, especially in the world of ever rising prices.

One common problem in the world of data is gatekeeping of data among Information Technology professionals, Neil said, this was especially true within larger organizations. There are many corporate politic issues, as the people who are handing over the data from which insights are driven might not give away those insights. Nonetheless, there is progress on data preparation and primarily how visualizations tools such as dashboards are being more heavily utilized. Neil also added that in order to keep up with the competition, there is a need of constant innovation, making sure that it’s implemented in the right, and that it isa good fit within the organization it’s being implemented in.

What do you think?

What are other questions you’d like to ask about the future? Feel free to leave comments so we can ask our next expert about their take on the future!

Neil talking about data

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