1. In a normal day, what kinds of data / analytics activities are you involved in?
Data preparation, coding, interpreting results, consulting with stakeholders to understand the business context behind the data and how/if analytics can assist with answering their questions.
2. What's the biggest challenges you face?
Maintaining momentum and senior advocacy to ensure that actions arising from insights are implemented and incorporated into decisions. Appetite for data driven decision making is varied, and some teams are not comfortable with making decisions based on complex model outputs. This relative maturity needs to be taken into account when engaging with different parts of the business, as well as solution design (e.g. a lack of comfort around neural networks due to the opacity between the prediction and its drivers may necessitate a simpler algorithm)
3. What would be the proudest or most enjoyable moment in your analytics career?
Deciding that analytics was an area I’d like to switch into, and actually being able to execute on the plan!
4. What advice would you give to a woman wanting to get into the analytics industry?
For those who are curious, technically inclined but also interested in human behaviour and optimisation, analytics provides a fulfilling career, opportunities for continuous learning and good work/life balance. As for technical skills, there are plenty of online resources you can access to get an idea of what analytics involves – learning sites like Coursera and Udemy; Kaggle datasets; Tableau public. 2 other critical areas: 1. Good quality data and an appreciation of its real-world context is KEY – do not gloss over this aspect! 2. Storytelling is what brings insights to life; a good narrative will convey the value of your work and why it’s important. And it’s a great way to get noticed.
5. What do you think the future holds for women in analytics?
It’s a great time to be in analytics, as the recognition of its importance is growing in all areas. As machine learning becomes more sophisticated, many activities a data scientist performs now will be automated. However it’s harder to automate an appreciation of how and which techniques are applicable to the problem at hand, as well as the ability to engage with non-analytics stakeholders. These are skills you want to ensure you continue to develop.