At the recent IAFP annual meeting, James Doyle, Director of Global Accounts, shared Creme Global’s extensive journey and learnings over the past 20 years in data communication and food safety. He explored the transition from aspiration to innovation to impact, emphasizing the importance of trust, discipline, and effective data sharing.
Watch the presentation.
Automatic transcript:
I want to take you through the journey and insights that Creme Global has gathered over nearly 20 years in data sharing, data communication, and collaboration with customers, organizations, and associations worldwide. Our experience can be summarized in three key steps: aspiration, innovation, and impact. We can drive positive change by applying discipline and extracting insights from complex scientific data.
Why Share Data?
The answer lies in the powerful statement, “Reputations are built on the decisions you make.” Trust and credibility are crucial, and the best business decisions are made with the best intelligence.
Sharing complex scientific data, simplifying its complexity, and extracting actionable insights lead to better decision-making. For many of us, this isn’t rocket science. But how do we incorporate a tangible asset to reduce the noise from numerous data sets and bring clarity?
Insight Requires a Lot of Data
Consider the food supply chain: it’s global, complex, and under intense pressure with numerous emerging risks. Dynamism has become the new norm, and accessing all the necessary data to make informed decisions is challenging. Some data is confidential, even within organizations. At Creme Global, we strive to break down these silos and provide technical innovations and solutions.
Embracing Uncertainty
In the dynamic global food supply chain, the need to make quick, informed decisions often leads to uncertainty. We encounter phrases like “Prepare for the unexpected,” which, while visionary, require actionable steps. At Creme Global, we listen to our clients’ needs, hosting aspiration workshops to understand gaps and problems. A critical tool in this process is putting data at the heart of decision-making.
Probabilistic Modeling
Traditional trend analysis, which relies on past performance to predict future outcomes, is limited. The future is uncertain, with multiple possible paths. Our approach involves building models to map out the probability of events occurring, thus providing a distribution of expected values and extremes. This disciplined approach helps experts make informed decisions.
Data Trusts
To manage and share data effectively, we advocate for establishing data trusts. A data trust includes a secure platform for data ingestion and insights wrapped in a legal agreement that defines data governance. This ensures clarity on data ownership, use, and benefits.
Benefits of Data Sharing
Sharing data leads to numerous benefits, including safer food, reduced costs, and maximized revenue. The food industry, in particular, is lean and faces the enormous challenge of providing safe food globally. By sharing data, we avoid duplication, reduce costs, and enhance revenue opportunities.
Implementation and Case Studies
Implementing data trusts involves secure data submission, legal agreements, data anonymization, and aggregation into a master database. The ultimate goal is an advanced visualization dashboard, the communication hub for all insights. This scalable and modular approach facilitates informed decision-making.
The more case studies we can use and the more people can talk openly about the warts and all, things that work and things that don’t, the more beneficial it will be to the audience.
Western Growers
The first one is the Western Growers Green Link Platform. For those who don’t know, the Western Growers Association represents a vast number of farmers in the west of the United States, covering four states.
And primarily in the area of fresh produce. And the Green Link platform itself is, and I think this is a really powerful statement, the first fresh produce online platform for food safety management. Western growers are coming up on 100 years in existence and have a group who step up to the plate and say; we’ve got to do something differently.
We need to change our practices to the betterment of what we’re all trying to do. Underpinning that very same sort of process where all the members and all the growers get access to live dashboards.
So part of that listening, those aspiration workshops, is to try to reduce any barriers to entry. ‘My data is not in that format.’ That’s fine. We’ll work with it. ‘It’s sitting over there’. That’s fine. We’ll contact that person as well.
So by listening and putting in place and identifying those needs or those jobs to be done, we can put in appropriate solutions. We positively impact human behavior by bringing people along that journey from start to finish, so when you present that in a communicative platform or a hub, it’s already fit for purpose.
Benefits
The benefits are advanced food safety, understanding best practices, benchmarking against what’s being done to maximize resources, learning from the data itself, and identifying emerging trends. So, maybe we can move from a state of insight to foresight.
And building trust. Trust is another keyword I’ve mentioned a few times.
fiin, Food Industry Intelligence Network
fiin is the second case study, the Food Industry Intelligence Network. About 11 years ago, there was the horse meat scandal where, from an authenticity point of view, horse meat entered the supply chain fraudulently. Arising from that, the British government enlisted the help of Professor Chris Elliott OBE, Queens University in Belfast, to write a report recommending key steps to affect change and avoid this from happening again.
One of the pillars that he recommended was that industry share data.
This was begun in 2015, and so now we have a bunch of members. Creme Global supports this; we built the infrastructure, maintain and host it, and we’re looking to expand and enhance it.
They submit data, and there’s a law firm involved. Precisely as we described before, we aggregate that data and provide insights. What’s interesting here is that we’re not looking for all the data. What they do say is that I did some tests, and I saw something and that may be an authenticity concern, and that’s more than enough.
I don’t need to see the parts per million or the micrograms or that sort of information because, ultimately, what this shows is your risk mitigation strategy or benchmarking across a series of your peers. Who else is working in this place? What sort of tests are they doing? What’s the frequency that they’re testing at, and what do they see?
Benefits
The benefits are predictability and prevention, so trying to anticipate changes, safer, higher-quality food, and knowledge of the crowd. Especially when you talk about small companies, they must be clever about where they put their resources and what testing they do. And I think small to medium enterprises—not that they’re a forgotten entity, but they have a huge role to play—often need to be brought into the fold and supported as much as possible.
This is an example of one of the dashboards. There’s a lot of detail, and there are hundreds of thousands of data points in the fiin network, but we want to take a step back and provide something like this, a simplified version. Why?
Do not underestimate the power of the human eye. Putting information in front of an expert in the correct format so they can make decisions is a very powerful statement and a very powerful tool for them.
Here is all dummy data, but you’ve got a series of categories, and you can see the trends—something’s moved up, and it’s a higher category of concern since the last quarter. You can do then is double-click and go down and see maybe why what was the driver behind that.
What we end up doing is using a Japanese technique called the five whys, where each click gives you an answer to the next ‘why’. Communicating through visualization or simplified dashboards like this is critical.
What we’re seeing now is that we’re talking to the Quadram Institute and Food Safety Research Network about a microbiological intelligence network.
And what’s very interesting is some of the same members in fiin are also in this network. And we talk about culture: I’ve trained many people in fiin. Never once has someone said to me I’m fearful of sharing data. The culture and the habit of sharing data in fiin have now translated over here. People ask why we don’t share microbiological data, not ‘should we’?. It’s very interesting to see the progression of when people benefit from data, they want to go again and again.
FDA food safety data sharing platform
My last case study is with the regulator, the US FDA, which is the food safety data-sharing platform. Again, very similar to before, it’s about aggregating and anonymizing multiple data sources.
So this is both within government agencies, industry, and academia. There are three real critical bodies you want to benchmark and actually see, maybe cross-correlate or cross-check. Ultimately, it enables the FDA and other stakeholders to gain insights on topics of interest.
This falls under a critical motivation within the FDA. We have the visionary behind the FDA’s New Era of Smarter Food Safety here. Isn’t it wonderful to set a vision and start to see tangible assets that will affect change? And that’s the mission there, ultimately, to accelerate learning.
There’s no quicker way to accelerate or get insights than sharing data. If you’re going to ask people to go and get me more data in the field, it just won’t happen overnight, and it will be extremely costly. There are loads of benefits to this approach – leveraging resources, avoiding duplication, and ‘trust’ comes up again.
Benchmarking is another keyword as well.
This platform is built around a series of data types, and PFAS is one of them, I want to give you a very quick example of an uplift in understanding.
Benefits
This is an example of the PFAS dashboard around FISH. The FDA’s data alone contains 2200 odd data points. By combining it with other data sources overnight, you can move from 2200 to 29,000 data points, and there is no quicker way to build knowledge and gain insight.
Organizational Culture
A strong organizational culture is essential in fostering collaboration and innovation, particularly in enhancing food safety. Companies with leadership and organizational buy-in for data sharing, prioritize data transparency, and foster a culture of open collaboration can significantly improve their ability to forecast risks and mitigate potential safety issues. By integrating real-time data sharing and predictive analytics across departments and the supply chain, organizations can take a proactive approach to food safety, identifying and addressing risks before they escalate. This collaborative culture not only strengthens internal safety protocols but also helps organizations meet evolving regulatory requirements. In an industry facing increasing scrutiny, building a culture that supports data-driven strategies is critical for staying compliant, reducing risks, and capitalizing on emerging opportunities within the market.
Key takeaways
I’m going to end with only three or four critical takeaways from 20-odd years of Creme Global’s experience in this space,
- Articulate The Vision And Benefits
- Scientific Input Is Needed
- Visualization and analytics Are Powerful.
- Start Collecting Data Now
Don’t underestimate the value of the eye, the human brain, once it’s been looked on by an expert eye. Keep going.
Effective and timely communication of complex scientific data is crucial for influencing human behavior and making informed decisions. By embracing uncertainty, implementing data trusts, and sharing data, we can drive positive change and ensure a safer and more efficient food supply chain.
