Panel discussion: Application of big data to food safety and food innovation

The first panel discussion of the European Food Sure Summit 2024 focused on how big data can be used effectively in food safety and food innovation. We've summarised some of the key takeaways.
Panel discussion: Application of big data to food safety and food innovation
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The first panel discussion of the European Food Sure Summit 2024 focused on how big data can be used effectively in food safety and food innovation. The speakers in the panel were:

  • @Roy Kirby, Partner, FoodSafERM
  • @Kimberly Coffin, Global Technical Director - Supply Chain, LRQA 
  • @Hugo Gutierrez, Global Food Safety Expert and Film Maker, Independent 
  • Sarah Jane Bellocchi – Networked Ingredients Functional Consultant, TraceGains 
  • Willem Fitjen – Innovation Director, Aviko BV

The discussion focused on the key considerations when exploring the use of big data, what "big data" really means and how to successfully integrate new data systems into existing company operations. We've outlined some of the key takeaways below: 

1. There is no value to data without an end goal/target: what problem are you trying to solve? What is the data going to be used for? Collecting and formatting data without understanding exactly how and why it is needed is of no use. The food industry has been collecting a lot of data for a long time, but identifying how to use that data has been more of a challenge. 

More data isn't necessarily a good thing, it's about getting the right data.

2. Human analysis and critical thinking cannot be overlooked: critical thinking needs to be applied to both what data we are gathering and how we are analysing it - a beautifully created dashboard won't help solve problems. The conclusions we take from the data are what matters the most - and this is where humans have to apply judgement and not be over confident. Acknowledging the limitations of our data and looking for ways to fill in the gaps is crucial to achieve credibility. We need to be able to explain to other stakeholders how we have interpreted the data.

Consider identifying the difference between verified data (e.g. audits, test results etc.) and unverified (e.g. questionnaires from a supplier pool). How much credibility can we put on unverified data? 

3. Introducing new data systems needs to take employee needs into consideration: Change management is hard; new data systems need to match employee needs: how can we make life easier for the people inputting the data? How can we bring employees in different departments along the digital transformation journey? This is where identifying the problems that need to be solved comes into play. Even though implementing system changes across an organisation is challenging, it becomes easier if employees are bought into the vision. How will people at all points of the supply chain use this data and how will it help extend their focus to problem solving?  

People will still be responsible for data collection, input and analysis - we need to bring the whole ecosystem along with us. 

The panel finished by considering the key question:

What happens if we don't do it? 

Rather than worrying about everything that might happen, we need to consider the alternative: what if we don't do it? Communicating the need for change is important to its success.

Additionally, we should use this question to help us prioritise. Do we have a long list of KPIs when we do something? Sometimes it might be better to consider what will happen if we don't do it to determine whether we actually need to, what needs to be prioritised and how to execute those things successfully.  

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