Every quality team has data. Walk onto any production floor and you will find sensors logging temperatures, labs generating test results and operators filling out audit forms. The issue isn't an absence of data, it's that most of it is static, siloed, and disconnected the moment decisions are made.

That was the core focus of a panel we hosted at Food Sure 2026: Static Data is the Silent Killer of Modern Quality, with Roberto Buttini (Barilla), Catarina Petisca (Puratos), Corinne Begueria (Bel Group), Nia Owen Cortez (Mondelez), and our very own Siska Lannoo. Here are six things they said worth taking back to your own site.

Insight 1: Stop collecting data that drives zero decisions 

A common trap in digital transformation is digitizing paper forms without re-evaluating their actual purpose.

"We were collecting data that nobody was doing anything with. Zero decisions, zero activities," shared Nia Owen Cortez from Mondelez. "By stopping that, we simplified the job of people at the plant level"

Map what you're already collecting, and prune ruthlessly. If a data point doesn't inform an operational action, a risk assessment or a business decision stop collecting it. That's less noise for plant-floor operators and focuses attention on core safety and quality metrics.

Data cleanup is not a one-time project. Every dataset, from supplier records to camera logs, needs a designated human data owner or it degrades within months.

Insight 2: Annual supplier audits no longer cut it

Static, once-a-year audits are inadequate for managing volatile supply chain risks. Risks carry variables, like weather and seasonality, that move on a monthly basis leaving massive blind spots in between traditional GFSI audit cycles.

"If you are buying wheat flour and you have two harvests a year, does it make sense to check with the supplier once a year, when you have a risk twice a year?" — Catarina Petisca, Puratos 

The alternative isn't more audits. It's continuous visibility. Modern AI tools can aggregate supplier data as it comes in and flag anomalies, rather than waiting for the once-a-year check-in.

Insight 3: Break the silo co-own quality

What happens when quality teams stop wrestling with static spreadsheets? When technology handles data aggregation and automated tracking, the role of the quality professional fundamentally shifts. Quality moves away from being viewed solely as the "food safety police" or a cost center. 

"Typically what I see, as soon as quality teams start to embrace technology, they go back to real quality work," noted Siska Lannoo.

With connected data, quality becomes a shared, cross-departmental mandate co-owned by operations, procurement and leadership. Quality professionals are freed to focus on high-value initiatives like customer transparency, faster incident resolution and proactive risk-prevention. 

Insight 4: The blockers haven't changed but AI might be shifting one of them

The journey from static to dynamic quality is rarely immediate. Unsurprisingly, the obstacles are largely the same ones teams have faced for years. Timeline, budget, and having the right people sitting at the crosspoint of quality, safety and IT.  

" I can see three types of blockers. The first one is a timeline. The second one I see is that the IT systems and tools we need to connect data are usually expensive. The last blocker is a matter of resources to implement such projects." — Corinne Begueria, Bel Group

The one blocker that may actually be shifting: how long implementation takes in the first place. 

"With AI now, technology has become smarter and quicker. It's much easier to connect systems compared to even five years ago. Time to value for me was one of the key drivers when we started Backbone. If it's not from day one you get something out of it, then I don't believe you're doing a good job as a tech vendor anymore." — Siska Lannoo, Backbone

Insight 5: Pitch budget conversations as risk conversations not features. 

Getting executive buy-in for digital quality initiatives remains a primary hurdle due to high CAPEX demands and competing priorities.

"Typically when we have a crisis top management is really eager to give money to solve the issue and avoid it happening again. Often the year after, they're wondering why we’re spending so much resources and cut the budget again." — Corinne Begueria, Bel Group

Position the investment as an ongoing risk-mitigation shield, not a one-off crisis response, is what keeps that support in place.

The panel’s advice: quality leaders must talk about risk appetite. Frame quality tech investments in the same way companies handle cybersecurity or corporate insurance.

“An organization needs to look itself squarely in the mirror and ask what risk profile it wants and answer itself honestly, because if you really want to go for a low-risk profile, that costs money. We've had those discussions at a senior level, even in the board of directors, about what profile we want to have. And the answer we got back was: actually there's no appetite for risk.” — Nia Owen Cortez, Mondelez

The framework to make budget conversations land: 

• Quantify the downside: Map out 10 to 20 years of internal defect history to calculate the financial impact of doing nothing.

• Frame the ROI: Presenting a $2 million data platform investment becomes straightforward when set against an estimated $30M–$40M annual risk exposure from potential product recalls.

• Focus on crisis prevention: Executive leadership frequently invests heavily after a crisis, only to cut cost a year later when everything is forgotten.

Insight 6: Beyond complaints the shift to leading indicators 

This has been a ongoing discussion amongst quality professionals for a while. It sounds good in theory. In practice, nobody on the panel claimed to have it fully solved. 3 examples that inspire:

• Target Set-Point Precision: Roberto Buttini (Barilla) shared how their teams track real-time variance against ideal product "set-points," measuring product consistency before deviations turn into defects.

•  Internal vs external incident ratio: a healthy quality culture actively encourages internal defect reporting. The goal is more internal reports than external complaints, it encourages early detection.

• Behavior-Based Food Safety (BBFS): Connected-worker tools allow plant operators to log near-misses on the shop floor in real time. Initiatives like Puratos’ monthly "Safety Champion" rewards operators for speaking out about risks before products leave the factory. 

But isolated pilots are not enough. As Nia (Mondelez) emphasized:

"You don't really get the shift from a quality control mindset to a quality assurance mindset until you do this at scale."

Rapid fire advice

1. Benchmark outside your own industry, sooner: "The watershed moment came for us as an organisation when we sat down with other industries and we saw what they were doing and went, 'we're years behind." 

2. Have a direction, not a fixed blueprint: Stay flexible as maturity builds in stages, and let the roadmap evolve alongside the technology instead of locking in an end state too early. Work with flexible high value milestones

3. Start with one painful, real example not a platform. Build the case around an actual problem before choosing any system.

4. Make data visible: whether it’s in reporting or consolidation, otherwise it’s unshareable across the business and it becomes hard to prove value. 

Every example on stage points to the same shift. Quality data stops being something you keep for compliance and starts being something you use to drive decisions.

It's a change in who owns what, how fast the organisation can respond when something moves and what the quality function is seen as. The teams already making that shift aren't waiting for perfect data. They're starting with one problem, connecting what's needed to solve it, and building from there.

It's also the exact gap we built Backbone to close: connecting the data that's already sitting in your systems and giving it meaning so it's usable the moment a decision needs to be made.