For Lyndsay Weir, the key to success with data analytics is getting to the “so what?”, beyond the charts and spreadsheets.
“You need to understand what 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 and 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 team work
Weir has a team of data scientists and analytics professionals who build out the models helping support data analysis. But she also manages the group insight team, which has people with a mix of skillsets that 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 being 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 and I really enjoy it,” she says.
Weir has previously worked for Nestle in data roles, but also elsewhere in marketing, core IT and SEO.
“My background is very varied and I’ve got IT knowledge, data knowledge, branding and commercial knowledge and 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 who want particular dashboards or models. This means they can be stuck dealing with a very siloed wishlist of requirements from across the business.
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 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. Out team’s job is to support on the big projects that will really transform,” she says.
“We are about connecting all the dots and driving that change there. So, 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
Part of 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 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 ones that 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 said.
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.
So often data is seen as a cost centre, when it should be seen as a value centre Lyndsay Weir, Barilla
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 and 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 the data and analytics executives is to manage expectations to create the buy-in from other execs, and do storytelling around the data science journey, and making 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, the actual demand for staff with those skills could be 10 times higher.
For Weir, solving this is about increasing the pipeline of people going into data and STEM careers, both through university degrees but also 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.
“That pipeline is crucial and one of the main ways we can increase that pipeline is getting more women into data. Last figures I looked at, one in four graduates in data science are women and when you get to leadership levels in data its shockingly low,” she says, adding that it is even lower in C-suite data roles.
“We need to help these people and help girls and women understand how exciting a career in data can be. People in the industry need to be encouraging, need to be helping.”
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’s really exciting things that can come out of it,” Weir says. “Highlighting the opportunities is key.”
To get a career in data you need a university degree, you need pretty good and expensive technology, a decent laptop, software and time. And there are plenty of good candidates out there that lack at least 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 absolutely 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 organisation, leaders, champions and peers to take this seriously.”