On an average day in Oonagh O’Shea’s job, she puts her analytical, problem-solving and mathematical skills to the test.
Working at Accenture Digital, Oonagh O’Shea is tasked with finding advanced analytics solutions for the life sciences industry, dealing with pharmaceutical and supply chain businesses.
As someone who loves to tackle a good puzzle, she enjoys applying her talents to suss out solutions to complex problems using data and analytics. Here, she tells us what an average day in her working life entails.
What is your role within Accenture?
I’m a senior manager in Accenture Digital, responsible for advanced analytics in life sciences. My role involves working across our digital and industry teams to bring innovation to our clients, with data and analytics at the heart.
If there is such a thing, can you describe a typical day in the job?
I’m not sure there is any such thing! If I had to define a typical day though, it would involve meeting with our clients to review their challenges and define analytics solutions to address priorities. I then work across our analytics teams to develop and deliver these solutions. This could involve, for example, working with data engineering to build the appropriate underlying data structures, and applying a range of algorithms, including unsupervised and supervised machine-learning methods, to solve the problem at hand. Finally, I translate and measure the business impact of these solutions, ensuring our clients continue to take a value-led approach to implementation.
What types of project do you work on?
I work with the life sciences industry, applying advanced analytics across the supply chain and global business services for pharmaceutical companies. For example, we implement forecasting and demand planning that’s driven by machine learning to prevent over- or under-stocking. We apply multivariate analytical techniques to optimise manufacturing processes and cycle times, improve product quality, and prevent downtime. We also leverage sensor data to power factory operations, applying predictive analytics to reduce maintenance costs, reduce breakdowns and increase line efficiency.
What skills do you use on a daily basis?
I use a mix of business and technical skills every day. Primarily, I focus on problem-solving; breaking down technical outputs into business relevant insight. I also continue to develop my core mathematical skillset across enterprise and open-source analytics platforms.
What is the hardest part of your working day?
Switching off. As somebody with a vehement love of puzzles, it can be tempting to bring those home!
Do you have any productivity tips?
Make lists and review them regularly. I take 10 minutes on my commute each morning to review what is in store for the day and jot down my priorities. I keep track of these throughout the day and take the same 10 minutes in the evening to review the outcomes achieved. It’s simple yet so effective.
When you first started this job, what were you most surprised to learn was important in the role?
Change management. I like to believe I always understood the importance of value-driven analytics; ensuring that we develop analytics solutions with measurable outcomes in sight, which is, of course, fundamental. As my role evolved, however, I recognised a third dimension: if core users aren’t ready for change, then the solution – regardless of potential value associated – is at risk.
How has this role changed as data analytics has grown and evolved?
As with most sectors, the volume of data and capability to process has grown immensely. With the proliferation of the internet of things leading to increased sensorisation and the capacity to implement real-time analytics at scale, the industry is at a turning point. I have seen the industry embrace analytics. While 10 years ago we were still educating people on the potential for analytics, it is now about how we can push the boundaries of analytics and continuously integrate new approaches into everyday operations.
What do you enjoy most about the job?
I get to work with a hugely talented bunch of individuals to apply logic and solve problems every day.