RMIA VIC Chapter Interview Series: Bree McLennan - The diary of a self-confessed risk nerd

Welcome to the Risk Management Institute's of Australasia's (RMIA) (Victoria Chapter) interview series that showcases the people who break the traditional risk management mould. We search for people who are doing risk management differently, drawing from very different backgrounds and using skill sets in unique ways to provide real and valuable risk management insights and solutions. If you're doing something different, put your hand up and tell us - different is good, different is innovative and different could be the way of the future! Read on to start your journey with us.

Self-confessed data nerd, competitive sprinter, risk management specialist and healthy living enthusiast, Bree McLennan provides us with an insight into the symbiotic relationship between data science and risk management.

Bree walks us through her diverse background that as the story unfolds is anything but boring, and provides us with a great insight into how her exceptional cross dimensional skill set developed.

From the get-go it is evident there is an extremely efficient and analytical scientific mind that yearns to ask and answer the ‘why’ questions. What's also evident is a genuine humanitarian warmth that strives to utilise that diverse skill set to make individuals’ lives better via her work at the Transport Accident Commission (TAC) and high-performance athletics coaching.

To back up the ‘boots on the ground’ experience, Bree casually mentions an extensive list of undergraduate, postgraduate and industry qualifications that must have made FEE Help weep.

The diary of a self-confessed risk nerd.      

Bree please tell us about yourself?

In one headline, I’m a data science and healthy living enthusiast. On one end of the spectrum I’m a fully-fledged computing technology and data and risk nerd, and on the other end of the spectrum I’m an athlete who is passionate about athletic performance coaching. It’s been my life’s work to date getting these two different facets to blend with balance.

Background:

  • Amateur athlete, passionate runner.
  • Athletic performance coach. Owned and operated a small athletic performance coaching business and now currently an owner of a small family business in personal fitness training, strength and conditioning.
  • Broad spectrum IT industry experience ranging from technical support to systems engineering, training facilitation to technical thought leadership.
  • Transferred skills and experience from IT to working with business data in business intelligence.
  • Combined a collection of eclectic professional experiences, became a data scientist and also a risk practitioner.

(To see Bree's full list of qualifications scroll to the end. We had to cut it back because it is soooooooo long!)

 What does a data scientist do?

Since the role of a data scientist is relatively new compared to many other traditional STEM industry roles, the job description varies depending on who you ask.

My current textbook answer to this is; data scientists help businesses and people interpret and manage data. Data scientists understand the spectrum of transforming data into wisdom and they solve problems using expertise in a variety of different niches, including their own business subject matter expertise, and where appropriate they will use and apply tools and methods originating from computer science, mathematics, statistics, machine learning and artificial intelligence.

 My personalised definition of what I routinely do as a data scientist is, I help TAC to identify areas where we can be managing our data better. I look for and communicate actionable opportunities originating in the data which enable us to better understand and clarify support needs for our clients, our providers, and even ourselves as staff and business units. In the spirit of science, I explore and experiment with new tools, algorithms and methods to discover new and value-add ways we can use our data to support improved decision making in our claims management space.

 In the data analytics landscape the key questions I’m always seeking answers to are:

1.   “What might be interesting”? Relating to automated data exploration and data discovery.

2.   “What is likely to happen?” Relating to predictive analytics and forecasting.

3.   “What can we do about it?” Relating to prescriptive analytics and advising on possible outcomes for scenarios.

So when data scientists and risk people get together, risk management turns from art to science?

I like to think a balance between art and science can promote an optimum result when it comes to risk practice. When it comes to assessing risks, opportunities, testing our control framework and simulating consequences, certainly a data-driven approach using modern data science methods offers a great, evidence-based and truthful starting point for a healthy risk discussion. However in our business we’re a social insurer, the majority of our business is about “people” and therefore being emotionally intelligent is an occupational requirement. We acknowledge that most of the time, there are humans directly behind the numbers we crunch, the data we analyse and use in predictive exercises. Algorithms can produce biased results if used carelessly and for us the impacts of such carelessness if it were to occur would flow on to our clients, the people we exist to support and our external partners who help us provide support. All these flow on impacts would detract from our organisation’s mission to be the world’s best social insurer.

In the balance between art and science, as data scientists we provide scientific, data-driven decision support tools to the business. These tools aim to remove the cognitive load in routine business decision making and assist in calling out the likely decision path to follow. Experience teaches us, nothing which involves humans is ever 100% certain and science-based decision support tools while useful, are not perfect and are subject to regular review and continuous improvement to maintain their usefulness and relevance. A human being is still needed to make the ultimate business decision and the tools we create as data scientists are intended to increase the likelihood that the human being making the decision is making the most informed, best possible decision to drive the best possible outcome.

How do you work with risk owners and risk management SMEs to traverse the qualitative gap from I 'believe' this is a risk, to I 'know' it’s a risk?

A few years ago in our journey of upskilling our TAC risk champion network and risk team via the Diploma of Risk Management and Business Continuity course, we came to a startling realisation that a lot of the time what we think is a business “risk” is in fact either an enterprise-wide shared causal factor, a consequence or an issue. It is not an actual “event” which if realised has the potential to “sink the ship” of the business. Learning about the true nature of “cause” and “effect” was thoroughly enlightening for all of us. At the time we realised this, our enterprise risk register had more than 200 enterprise business “risks” documented.

After our educational enlightenment we’ve come to realise and accept that truly there are less than 20 enterprise business risks, and these risks have many shared causal factors and consequences. These risks describe the real events that if were to occur would directly threaten our organisation’s ability to operate and exist.

The benefits of detangling and simplifying our risk register made it so much easier for us to focus our energy, attention and work efforts on the big ticket items we must get right as an organisation, and stop sweating the small, noisy stuff.

These days when we receive queries about “how do I know this is a risk?”, we like to remember and share what we have learned on our learning journey to date and ask more important questions like “can this be more appropriately defined as a causal factor, consequence or an issue, instead?” and “can you demonstrate the effectiveness of the controls you have, versus the real business problem you have?”.

Outside of the TAC world, how do you see the application of data science, and the risk management function and risk owners working together, in for example a commercial sense - the translation of risk's potential cause or effect on the P/L or performance KPIs?

The TAC is a unique kind of business versus many other organisations, where our paradox is we are a social insurer and we do not aim to increase the number of clients we have. A perfect world for us is where no one suffers road trauma on our roads, or in a Victorian registered vehicle. We measure ourselves on the rehabilitation outcomes our clients achieve, how many lives we save and serious injuries we reduce through the “Safe System” approach of safer roads, safer vehicles, safer speeds and safer people, and by how well funded the scheme is to continue providing this noble service to the community.

As a TAC data scientist who is balancing art, science, empathy and logic, in a different business world I see the application of data science to be the same as we apply it at TAC. We apply data science when we seek to better understand the nature of our business, to have better, clearer comprehension of the data landscape which is supporting the business. The balance sheets, performance KPIs and reports may look different between business worlds, however the math, statistics and algorithms largely stay the same. The variable creating the difference here is the business purpose, business rules, and the problems the business is attempting to solve.

Data Scientists are advisers, just like risk practitioners and we want to work toward ensuring that the most optimum business decisions are being made. We must work collaboratively with risk owners, else the advice we have to share will yield no value and contribute nothing in business decision making. In a collaborative context, the data scientists support the risk owners by advising the optimum path forward while the risk owners decide, action, and drive the path to take.

On the flip side of risk there is opportunity, how can you support the identification and achievements of opportunities?   

The world of science is wonderful. There are people out there who are always seeking to improve methods and the world around them for the greater good of all. There are wonderful people like this in the data science industry and I like to think some of those people work here!

As our technology and computing power improves, so does our ability to work with data. In the spirit of scientific endeavour we are always seeking out new algorithms, tools and technology we can apply to our data, sometimes we need to do this in researching options to approach business problems and sometimes we need to pioneer an entirely new approach or tool to inspire the business to keep on track with TAC’s mission and continue to innovate forward. Part of the journey to be world leading is to have the courage to explore and pioneer within new and unfamiliar frontiers and iteratively apply valuable lessons from these experiences to grow and become what we seek.

Qualifications:

  • Bachelor of Information Technology. Majored in Information Systems & Games Design and Development
  • Certificate IV Training and Assessment
  • Information Technology Infrastructure Library (ITIL) - Industry Certification
  • Change Management Foundation Certification - Industry Certification
  • Neuro Linguistics Programming (NLP) Practitioner Certification
  • Recreational Running Coach Level 2 (Athletics Australia)
  • Certificate III Fitness Instructor, Certificate IV Personal Trainer
  • Certificate IV Small Business Management
  • Diploma of Risk Management & Business Continuity
  • Data Science industry certificates & online study: Machine Learning, Deep Learning, Text Mining & Natural Language Processing