“You need to understand what the business question is and you need to get to the action. I say this to my teams, but whenever they produce anything – a report, a tool, a dashboard – my question is always, ‘So what?’,” she says.
While coming up with that “so what?” might not always be easy, for Weir it’s one of the best parts of her job because those insights can have such a big impact for the business.
“There’s nothing more rewarding than seeing the output of something you’ve worked hard on become a reality, whether that’s a new product innovation, a new campaign or a big corporate decision. That fuels you and it’s really exciting. No two days are the same, which is why I love it even more,” she says.
Weir is the chief of analytics and insight at pasta company Barilla. The company was founded in 1877 when Pietro Barilla Senior opened a bread and pasta shop in Parma, Italy.
Nearly 150 years later, the still privately owned pasta company has a turnover of more than €4bn and 8,000 staff, exporting products to over 100 countries. And it’s invested in building out a team of data science and analytics to help support business decision-making, something it probably would not have needed in nineteenth-century Italy.
Data science and teamwork
Weir has a team of data scientists and analytics professionals who build out the models that help support data analysis. But she also manages the group insight team, which has people with a mix of skills who aim to take that data and turn it into business insights. “These people are the bridges between dashboards, reporting and commercial decisions,” she says.
Weir also has a capabilities team tasked with putting in place the fundamental data standards to ensure that data is shown in the same format each time, and that the same language is used across the company so they are all heading towards a common goal.
“It’s a nice, almost circular function I have here. It can go from raw data through to business results, or it can go backwards from business vision through to what we need to build to get there. I really enjoy it,” she says.
Weir previously worked for Nestlé in data roles, but also elsewhere in marketing, core IT and search engine optimisation.
“My background is very varied – I’ve got IT knowledge, data knowledge, branding and commercial knowledge. I think that’s helped me get into the position I’m in today because I can flex the technical side, I can flex the corporate side, but bringing both together is truly where I think the magic happens,” she says.
One problem that analytics teams can run into is that, after some early successes, they risk being overwhelmed by the demands of individual departments that want particular dashboards or models. This means they can get stuck dealing with a very siloed wishlist of requirements from across the business.
So often data is seen as a cost centre, when it should be seen as a value centre
Lyndsay Weir, Barilla
In contrast, Weir’s team aims to prioritise what it is delivering based not just on what’s being asked for by various departments, but also on the company’s long-term ambitions and building the tools to support that.
“Instead of having a portfolio of 100 projects on the go at once, we have very few but we try to make them big impact. Our team’s job is to support the big projects that will really transform,” she says.
“We are about connecting all the dots and driving that change there. Of course, we build some reports and some dashboards, but we try to do things bigger and cross-function and top-down.”
Top-level buy-in
The key to success with analytics is being part of the wider plan, she says.
“You need to have top-level buy-in. If you are not at the table where the company’s long-term plans are being built, you will not have any idea of what data analytics forecast tools can be useful in the big picture,” she says.
“You’ve got to have an understanding of the technical, but you need to have a corporate and business understanding,” she says. Otherwise, the analytics team could end up building fantastic tools, but they have no impact on the actual business problems.
“Some people find it hard to step away from the deep technical knowledge to learn more of the corporate side, and that can be a barrier,” she says.
This is where the “so what?” comes in. There’s no point going into a meeting to simply go through a dashboard – instead, you ought to be using the data to find the “so what?” and build an action plan, she says.
“The biggest learning is to double down on the action rather than just the visualisation or the reporting,” she says.
That does not mean ignoring the technical side of things, however. When Weir joined Barilla, she spent two years working on marketing data governance, naming conventions, taxonomies structure and cataloguing. “It looked really unsexy, but it made us be able to go fast where we are today,” she says. “It’s about finding that middle ground, which is so hard to do, and I check myself all the time.”
Part of the role of data and analytics executives is to manage expectations to create buy-in from other executives, do storytelling around the data science journey, and make sure the output is usable.
“So often, data is seen as a cost centre, when it should be seen as a value centre. And too often, people are being asked to prove the value of the investment behind the tool or data or the science or the people, instead of asking, ‘What did this tell us and what impact did that have on the business?’,” she says.
Building the data talent pipeline of the future
One area that data science struggles is with recruitment and retention. While universities only supply around 10,000 data specialist graduates per year, demand for staff with those skills could be 10 times higher.
For Weir, solving this is about increasing the pipeline of people going into careers in data and science, technology, engineering and maths (STEM), both through university degrees and through placements or mentoring opportunities. This would help “give people those skillsets and build talent that might not have had the traditional start into data science – a bit like myself”, she says.
“Instead of having a portfolio of 100 projects on the go at once, we have very few but we try to make them big impact. Our team’s job is to support the big projects that will really transform”
Lyndsay Weir, Barilla
“That pipeline is crucial, and one of the main ways we can increase that pipeline is by getting more women into data. In the last figures I looked at, one in four graduates in data science were women, and when you get to leadership levels in data it’s shockingly low,” she says, adding that it is even lower in C-suite data roles.
That includes everything from how companies write job adverts through to outreach and how the industry showcases what a career in data can be.
“It’s not all just sat in a dark room programming away. There are really exciting things that can come out of it,” says Weir. “Highlighting the opportunities is key.”
To get a career in data you need a university degree, a decent laptop, software and time. But there are plenty of good candidates out there who lack the means to have all these things, says Weir, who works with organisations including Women in Data and Wild Hearts apprenticeships foundation.
“This is very similar to the background I had. I think the pipeline can be increased and it’s on all of us in data today to make that difference. When people are in the role, it’s about enriching people to give them the right opportunities and experience, and to help them to do things they enjoy doing,” she says.
“It’s going to be a big collective effort, and we need organisations, leaders, champions and peers to take this seriously.”
The role of retention in gender parity
Our mission at Women in Data is to achieve gender parity at every level in the data and tech industries. Women are hugely under-represented across all data and tech fields – in 2024, for every four male analysts and data scientists who join this industry there are 0.68 women and retaining that talent is becoming an ever-increasing concern. Today, 50% of women are still leaving the industry by the midpoint in their career and most organisations are failing to meet their own targets for female senior leadership.
Having more senior female leaders will drive innovation, foster inclusion, attract and retain talent, challenge bias, improve business performance and better meet the needs of customers.
The annual Women in Data State of the Nation report highlighted what the industry can do to encourage women to maintain and develop their careers. Including mentoring and coaching opportunities – internal and external – formal leadership skills development; additional technical skills training; and access to external groups and networks.
Alongside these, women are looking for flexible working practices and closure of the gender pay gap and for companies to implement equitable performance and promotion processes.
Addressing the barriers to success for women working in data and tech requires a long-term approach that considers all obstacles. The industry cannot do it alone and we need the commitment of academia, government, businesses and advocacy organisations to change the demographic landscape in the data and tech sectors.
