Having completed this session, you will be equipped with the awareness of the FOSS Python libraries, Splink, and Great Expectations, and how these can be integrated into Databricks to improve data quality and record linkage.

Something is shifting in the Nordics — not just in how we use data, but in who shapes what comes next.

Data transformation doesn't happen in a strategy deck. It happens in the decisions people make, the systems they challenge, and the cultures they're brave enough to change. But how do you actually do it inside a large complex industrial organisation, and make it stick?

In this very first Women in Data Nordic virtual breakfast, we're bringing you a rare and candid look at exactly that. Christina Finlay, Senior Director of Data & AI for Industrial Operations at Volvo Cars, will share what her team actually changed over the course of 12 months: the obstacles, the decisions, the progress, and the lessons learned.

What you'll take away

• A grounded, honest account of transforming data access and collaboration inside one of the Nordics' most iconic organisations
• The real conditions that enabled change, and what nearly got in the way
• How laying the right data foundations unlocks the AI and advanced analytics ambitions your organisation is chasing
• Practical lessons you can bring back into your own organisation, regardless of sector or scale

And then we turn the lens on us. Because WID Nordic is here and it is live. Join us for a morning of insight, inspiration and collective ambition as we connect, learn and begin shaping what comes next for Women in Data® Nordics - together, from the start.

Having completed this session you will be equipped with the skills and knowledge to successfully navigate complex business problems through drawing on your existing data discovery skills to seek out answers to ambiguous questions.

We will cover how to support your stakeholders to narrow down their brief through discovery and play back, how to use the tools available within a modern data warehouse to find the data that you need, and how to start small and build big to answer ambiguous questions. 

Having completed this session, you will be equipped with the skills and knowledge to effectively leverage Large Language Models (LLMs) to transform text data into structured insights.

Using our Monzo journey as a case study, we will walk through the core development lifecycle of our LLM-powered intent classifier. Used to identify why customers contact Monzo, helping to detect customer problems and dynamically route to the right help, faster.

We will explore how to extract a clean taxonomy from unstructured historical chat data, build a robust two-stage classification model, and achieve scalable, grounded evaluation by combining human and LLM-labeled data with LLM judges.

Women retire, on average, with 113,000 less pension wealth than men, largely because of predictable life events: lower pay, career breaks, part time work, and unequal parental leave.

The good news: there are practical steps you can take at any age to reduce your own gap.

Having completed this session, you will be equipped with the skills and knowledge to:

• Check your State Pension forecast and National Insurance record, understand gaps and the role of caring credits.
• Spot the “silent” pension gaps that happen when you work part time, have multiple jobs, or earn below auto enrolment thresholds (and know how to opt in).
• Prepare for maternity/shared parental leave and career breaks by understanding how workplace pension contributions typically work during paid leave and how to plan for unpaid periods.
• Run a simple “1% rule” reality check (e.g., how a small contribution increase across your career can help offset time out of the workplace).
• Do a quick pension admin tidy up: trace old pensions, update beneficiary nominations, and check your investment approach and charges.
• Start (or restart) money conversations at home so retirement saving is treated as a shared family asset and plan.