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Jennifer LaPlante, executive director, DeepSense

Jennifer LaPlante, executive director, DeepSense

How is your organisation using data and analytics to support the corporate vision and purpose?

 

DeepSense supports ocean sector companies with resources, software and computing power to complete AI and machine learning applied academic projects. These projects develop commercially useful predictive machine learning models, analytical prototypes or proof of concepts to optimise or grow ocean-affiliated companies, and the overall ocean economy.

 

Our support is scaled to align with a company’s technology and data maturity, ranging from training sessions to helping companies understand how they can leverage the benefits of machine learning in the future, to helping companies understand possible use cases based on their available data.

 

2020 was a year like no other - how did it impact on your planned activities and what unplanned ones did you have to introduce?

 

We support companies with applied academic research, which is often part of a company’s R&D budget. With economic instability starting at the end of Q1 2020, we saw an immediate retraction in the number of companies looking to invest in exploring potential AI and machine learning projects. We quickly shifted our focus to short-term projects we labeled as “data readiness”. These projects were smaller scale, had fewer resource and time investment, and placed emphasis on preparing companies’ data for future large-scale AI projects.

 

Looking forward to 2021, what are your expectations for data and analytics within your organisation?

 

I hope to partner with dozens of companies to help them explore and develop usable machine learning models. Every year there is an increase in awareness and curiosity about AI. We are moving beyond the buzzword of AI, we are seeing AI/machine learning added to business plans and leaders’ annual objectives. While DeepSense helps companies explore AI, we are also heavily focused on developing the future ocean AI talent required to meet the growing demand. 2021 will be filled with workshops, hands-on sessions and webinars to help students prepare for a career in AI.

 

Is data for good part of your personal or business agenda for 2021? If so, what form will it take?

 

While we do not use the language data for good, all of our projects tend to have a positive economic or environmental impact. Our projects range from helping to identify and protect whales through image analysis, to optimising the analysis of fish levels in close proximity of power generation and optimising land, sea and air transportation. All projects have a direct or indirect impact on reducing environmental impact and environmental sustainability.

 

What has been your path to power?

 

I have a BA (Honours) in Sociology, a Master’s of Business Administration and am in the final stages of completing a Master of Science in Computing and Data Analytics. I started my career in market research, with emphasis on survey design and developed some programming skills while using SPSS.

 

My career evolved as I moved to leadership roles IT and innovation. With a broad mix of arts, business and technology, I have been fortunate to hold roles that lean on all aspects of my formal education. Being able to understand how to communicate with programmers as well as a board of directors is critically important when trying to help companies understand the benefit and requirements for successful AI.

 

What is the proudest achievement of your career to date?

 

I spent a few years creating an innovation outpost while working for the Canadian government lottery. Because new products and services must integrate with all existing technology, testing a new idea in market was impossible without substantial investment. I found a way to work within legislative, regulatory and organisational boundaries to create an entirely new tech stack, independent from current operations and processes to serve as a test-bed for the development of new products and services.

 

Working with the existing IT operations and IT security teams we successfully created an environment to test innovation. Building adjacent to operations to test something new is similar to DeepSense, we help companies try AI without investing full-scale up-front.

 

Tell us about a career goal or a purpose for your organisation that you are pursuing?

 

DeepSense was created to enable the ocean sector to explore, attempt and adopt AI and machine learning. We have the dual purpose of identifying and completing projects with graduate students and ocean sector companies to develop a proof of concept, AI prototype or functional code to help optimise operations and create a new product or service. At the same time, we are focused on demonstrating to students the breadth of companies and industries that will benefit from their skills, increasing supply of talent as demand in the sector for data skills grows.

 

What is your view on how to develop a data culture in an organisation, building out data literacy and creating a data-first mindset?

 

An organisation must have some naturally curious team members to help build out a usable data programme. Subject matter experts are trained in their domain to be good at marketing, product development, communications, etc, but they are not always trained in how to use data. Data literacy is only achieved with very targeted efforts. It is too much for people to make a leap and attribute an example from one domain to be relevant to their own roles and goals. Effort has to be focused on individual teams to understand their unique objectives and barriers, allowing usable training and data solutions to be developed.

 

Please share one tip you have found to be successful when looking to build support among the senior executive for a digital/data transformation

 

I have relied heavily on the Machine Learning Canvas by Louis Dorand. Using the canvas during ideation and planning workshops helps all participants to flesh out a priority AI concept. The model brings clarity to those who still do not fully grasp what is required to train a machine learning model, allows those responsible for data to provide clarity about the current status and quality of future data, and allows strategic decisions to be made about the short and long-term objectives for the models that get developed.

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