Data-Driven Test-Automation Frameworks

Data-Driven Test-Automation Frameworks: How Ubisoft is Generating and Executing Automated Tests from Game Data.


Using a data-driven test approach to enable a test-automation framework provides benefits beyond the simple execution of automated tests. In this presentation, we will look at how these techniques can be used to increase test coverage while also providing valuable data that can be used to better understand application stability and readiness.

Guest Speaker:

Sean Wilson, Worldwide QA/QC Development Director for Ubisoft.
Certificates: PMP, PSM I, PSO I, PSK I

Sean started as a manual tester on a financial treasury application somewhen in the last millennium. His career took him on a winding journey through automated testing, development, project management, and agile evangelism before he abandoned mainstream software and went where he could play games and get paid for it. In his current role as the WorldWide QA/QC Development Director for Ubisoft, Sean is focused on evolving the approach to Quality Assurance through a better application of technology.

Innovation at Lyft

Innovation is crucial for continued success in any organization. From rideshare to self-driving vehicles, Lyft is consistently finding new ways to improve lives through transportation and technology. 

Join Pankaj Agarwal, Optimus’s Managing Partner, and Peter Lukomskyj, Lyft’s Head of Growth Markets and General Manager in British Columbia, to discuss Innovation at Lyft.


In this webinar we will cover: 

  • Technology trends and risks
  • Advantages of data
  • How Lyft measures success
  • What differentiates Lyft from other technology companies

Webinar Details

Topic: Innovation at Lyft
Date: February 25, 2021
Time: 9:00 – 9:45 AM PST
Location: Zoom webinar


About Our Guest


peter Innovation at LyftPeter Lukomskyj is a 20+ year business and technology leader who has helped grow several local Vancouver tech companies. After successfully launching Lyft in BC in early 2020, he is now responsible for leading the company’s growth market expansion efforts. He has a passion for bringing innovations to market, creating lean operating frameworks, and building and leading high-performance teams. Prior to joining Lyft, Lukomskyj’s accomplishments include helping scale Elastic Path through a period of sustained growth, and helping Quickmobile place as #1 on Deloitte Canada’s Fast 50. He is a Professional Engineer, Chartered Professional Accountant, holds an MBA from Queen’s University and was named a “Forty under 40” by Business in Vancouver.


Lukomskyj lives in Vancouver, Canada with his wife and two children. Outside of transportation, he is an active advisor and mentor to several Vancouver-based startups. When he’s not in the office, he can be found enjoying everything the west coast has to offer, including skiing, cycling, hiking and sailing. He is passionate about maintaining the liveability of the region, while growing it to become a global economic centre.




Game Changers: The Role of Big Data in the Future of Credit Unions

In 2002, Billy Beane was the manager of the Oakland Athletics in Major League Baseball. Oakland was a small market club with a similar sized budget and it struggled to be competitive.

Because Oakland didn’t have the money of big market teams like the New York Yankees or Los Angeles Dodgers, Beane knew he couldn’t hope to attract the high-priced talent – the superstars – to play in Oakland.

Enter Paul Depodesta, aged 27, an economics graduate from Harvard, with an analytical mind and a love of baseball. His arrival on the doorstep of the Oakland A’s gave birth to data analysis in professional sports.

He analyzed player stats, using computer algorithms, and his results allowed Oakland to sign inexpensive players that other teams dismissed. The A’s were propelled into the stratosphere of success, thanks to big data.

The A’s finished the 2002 season with 103 wins, the same number as the New York Yankees – but with a budget about a tenth the size.

This is the “secret sauce” in data analytics: the ability to take substantial amounts of information – in the case of Oakland, endless baseball player statistics – look for patterns and capitalize on what is found.

Credit Unions, Machine Learning and Data Analytics

Credit unions in Canada are rapidly embarking on the same exploration. Using machine learning and data analytics, these financial firms are finding ways to improve service to their clients while, at the same time, discovering nuggets of information from the vast amounts of data they collect, that can then be turned into business opportunities.

Virtually every customer transaction within a credit union is electronic, and the amounts of data being collected are staggering. The need to analyze this information is what drives credit unions today to embrace machine learning and data analytics.

Matthew Maguire is the Chief Data Officer at Co-Op Financial Services, a California-based company that operates an interlinked system of ATM machines throughout the U.S. and Canada. He argues that machine learning and data analysis are critical for mid-sized credit unions as they work to reinforce current customer relationships and build new ones.

“Data is coming in from different places and the challenge is… how do you make it all connect?[i]” he said.

Credit unions are moving quickly into data analysis. Through machine learning, which unearths customer transaction patterns by using algorithms, credit unions are learning a great deal about their customers and are designing strategies to capitalize on that in order to drive sales.

But, for credit unions, data enables other capabilities. Patterns of fraud can be easier to spot and shut down through data analysis.

When a client invests with a credit union, regulations require the client to complete what’s called a Know Your Client form, which essentially draws a profile of risk tolerance and investment objectives. If the client’s portfolio strays from that profile and becomes riskier, big data can alert the financial institution and the problem can be corrected before any monetary loss accrues to the client – or to hundreds of thousands of clients.

Chris Catliff is the president and CEO of Blueshore Financial, a B.C.-based credit union with more than $3 billion in assets. His vision of the future of credit unions is predicated on the power of data analytics in combination with machine learning.

He envisions the day very soon when a client approaching a branch receives a text message saying the client is already checked in at the branch. As they walk through the door, their customer profile and picture pop up on a screen [ii] at a concierge desk and they’re greeted by name.

Blueshore’s ATM machines will respond to a customer’s biometrics and offer a transaction based on a pattern of previous transactions. Up-sell opportunities will present themselves, so staff can suggest options – situations that might never occur without data analysis.

Service, he said, “has to be electronic transactions with the introduction of superior, human touch at various critical points. It’s high tech and high touch.”

Explore Your Data Potential

Like the members they serve, every credit union is unique. It is imperative for a credit union to work with data specialists who can marry the individual needs of each credit union with high levels of expertise across big data, data analysis and machine learning.

One of our strengths here at Optimus is our track-record in the areas of data gathering, analysis, machine learning, dashboarding and data visualization, through which we help our clients tailor data mining and analysis to their business goals.

At the end of the day, it’s all about staying competitive and, like the Oakland Athletics, reaching the pinnacle of success by embracing and employing new strategies to achieve that success.





Nothing Found

Sorry, no posts matched your criteria