1. In a normal day, what kinds of data / analytics activities are you involved in?
My role is a mix of leadership and hands-on data science. A typical day will include planning and activities focussed on ensuring we continue to enhance and accelerate data science capability across the organisation, whiteboard sessions to workshop analytical approaches to answer challenging business questions, and coding and analysis to deliver squad objectives.
2. What's the biggest challenges you face?
Balancing delivering at speed with building capability. With stakeholders understandably wanting things delivered quickly it can be a challenge to dedicate sufficient time to capability uplift work that will help the team deliver better and faster in future.
3. What would be the proudest or most enjoyable moment in your analytics career?
When a team member I’d been coaching presented her analysis to me and it was so good I had no suggestions for improvement. It was great to see how much she’d developed both her analytical and presentation skills, and in particular to see how proud she was because she knew she’d delivered valuable insights.
4. What advice would you give to a woman wanting to get into the analytics industry?
Learn to code. SQL is a great place to start to help you understand data structures and basic joins and aggregate functions, then consider languages like R or Python where you can start applying more advanced analytical techniques.
Technical skills are base requirements, but what makes a great analyst is the ability to really understand the business problem, come up with an appropriate analytical approach to solve it, and then to communicate the insights to stakeholders in a simple but compelling way. So on top of technical skills it’s important to work on soft skills like consulting and communication.
5. What do you think the future holds for women in analytics?
A fantastic opportunity to help shape decisions and outcomes. The field is growing and while it’s currently weighted towards males, diversity (not just in gender) will help make sure we minimise the bias that’s often unintentionally present in data, and therefore in data-driven insights.