Sometimes we forget the humans behind the tech in our ever busy world. DSF is fortunate enough to know some incredible tech leaders across the world and has the privilege of hearing them present at our events. That being said, our Speaker Spotlight sets the stage to get to know our speakers on a more personal level and connect them with our growing community. Read the mini interview below!
A bit about Lydia
Lydia embarked on her professional journey on the bustling trading floor of an investment bank, where she discovered a fascination for understanding company business plans and using this understanding to build financial models. This realization paved the way for a fulfilling career in analytics, where Lydia utilizes the power of data to unravel business intricacies and guide informed decision-making.
Currently serving as the Head of Analytics at Stuart, Lydia crafts data-driven solutions to address key challenges within the UK business. Recent projects span the identification of key drivers influencing delivery costs and strategic recommendations for business expansion.
Lydia has navigated diverse data roles. At Ocado, she managed stock levels and optimized pricing. At Meta, she developed expertise in experimentation and metric design. At GHGSat, Lydia guided a team in creating a product for investors to assess methane emissions in portfolios.
Beyond her corporate roles, Lydia advocates for equal access to data-driven insights for charities. As a director at Stemettes, she contributes to inspiring and supporting girls, young women, and non-binary youth in STEM. Additionally, Lydia sits on the Scoping Committee for DataKind, an organization dedicated to aiding charities, local government, and social enterprises in harnessing the power of data science.
How did you start out in your tech career?
My initial foray into anything data-related occurred during my teenage years when I established a website dedicated to computer games. My first hands-on experience with data was building a database to manage contact forms.
Professionally my first job was in investment banking, where I constructed financial models for companies. Assessing their profitability and ability to repay debts. During this period I discovered that understanding companies deeply really interested me. Subsequently, I transitioned to an analytics role at Ocado, my first full time data role.
What are the signs of success in your field?
The greatest reward is doing a piece of analysis and seeing it being used to make a big difference to the business.
A common pitfall to avoid is completing a well-crafted analysis that goes unused. This may occur if the analysis isn’t immediately relevant to the business, if it doesn’t address the right questions for stakeholders to gain confidence, or if the recommendations aren’t communicated effectively. Therefore, success, from my perspective, is not solely in the completion of the analysis but in witnessing the implementation of the work, yielding tangible and visible results.
What is the best and worst thing about your job role?
The thing I love most about analytics is really understanding the business from the inside out. Seeing how different parts of the business connect and how they work together to make the business as a whole work, and using that knowledge to find ways to make things better.
A significant challenge arises when making recommendations. In this role, implementation often involves convincing others to take action from your recommendations. The need for strong communication and influencing skills is essential to deliver business impact.
What can you advise someone just starting out to be successful?
Make the most of your learning opportunities. Everyone you collaborate with brings a unique skill set and knowledge base. Engage with individuals you encounter, especially at events like the Data Science Festival, to understand their strengths and learn from them. Identify the aspects they excel in and strive to acquire those skills.
How do you switch off?
For a quiet evening in, I enjoy getting a cup of tea and reading on my sofa. If I have a day free, I explore London on my bike, to find beautiful parts of town.
What advice would you give your younger self?
Understand the ‘why’ behind your actions. If you know why you are doing things it becomes much easier to deliver great work.
“Eat the frog” – spend 2-3 hours each day on your most important project, even if it’s scary.
What is next for you?
Support my team to be even more successful and show the value analytics can bring when we are partners in projects.
If you could do anything now, what would you do? Why?
I love learning new things, so something that allows me to use my current skills and learn new ones.
As a bonus: designing puzzles on a laser cutter.
What are your top 5 predictions in tech for the next 5 years?
- Large language models: Over the next few months we will find out how well LLMs can do analysis tasks. There are lots of open questions about how they will change data roles.
- Product analytics and Analytics Engineering roles being defined at more companies. Product Analysts are analysts embedded in software engineering teams, using analytical skills to drive the strategy, measurement and execution of the team. Analytics engineering is data engineering but with a business focus. There is so much potential in combining the deep skills for data engineering with the knowledge of how data is being used.
- Data contracts, to ensure high quality data inputs, becoming a standard part of the data stack. A data contract is like an API between a data producer (e.g. an Eng team) and the data warehouse. It specifies what the data delivered into the data warehouse is and the expected format.
Data contracts ensure that when the prod system changes (because it always will), the data can maintain its consistency and effectiveness without lots of work needed to adjust data pipelines.
- Data documentation and Semantic layers. These ensure everyone is using the same definitions for key metrics, increasing internal consistency. Semantic layers enable this to be done programmatically reducing errors that creep in with code duplication.
A good semantic layer can supercharge AI, making it much more likely that you will get the right data back from a complex database.
- Shift in data roles and skills needed: Data work is increasingly about driving results, meaning that communication and influencing skills are ever more important.
Watch Lydia’s session at the Data Science Festival here.
Thank you to all our wonderful speakers for taking part in our Speaker Spotlight!
Want to become a DSF Speaker? Apply here!