ROLE OF DATA SCIENCE IN FOOD CHAIN SAFETY DECISION … · 2020-01-27 · 3 7 COUNTRIES FORM THE...

ROLE OF DATA SCIENCE IN FOOD CHAIN SAFETY DECISION MAKING: CURRENT STATUS AND FUTURE TRENDS ÁKOS JÓŹWIAK NATIONAL FOOD CHAIN SAFETY OFFICE (NÉBIH), HUNGARY

ROLE OF DATA SCIENCE IN FOOD CHAIN SAFETY DECISION MAKING: CURRENT STATUS AND FUTURE TRENDS

40 years of RASFF: “All you need is RASFF?” – theme: smarter data = better analysis 13.12.2019. Brussels

2 ROLE OF DATA SCIENCE IN FOOD CHAIN SAFETY DECISION MAKING: CURRENT STATUS AND FUTURE TRENDS

Increasing

volume &

complexity

of the food

chain

3

7 COUNTRIES FORM

THE CORE OF THE

AGRI-FOOD TRADE

NETWORK


Figure 1. The world’s food trade grows faster than the food product ion. (A) (Log-linear scale). The world’s food production (thin red line),measured in current Billion US$, doubles in ca. 30 years, while the amount of food transported on the IFTN (linearly fitted small squares, blue)increases by ca. 10-fold in the same time. (B) (Linear scale). Food ingredients flow at an increasing rate from countries to countries, as shown by theexponentially increasing [world export]/[world production] ratio calculated from the above data (small squares fitted by an exponential curve). Notethat this ratio is unaffected by the US$ inflation rate. Data obtained from UN databases [6,23].doi:10.1371/journal.pone.0037810.g001

Figure 2. The complete Internat ional Agro-Food Trade Network in 1998. The IFTN is based on reported export, involving N= 207 countries(nodes) drawn as disks and M= 10645 trade fluxes (those worth more than 1 million US$), drawn as directed edges/links. The top 44 countries withthe largest total trade activity (import+export) and the top 300 largest food-trade fluxes were colored according to their betweenness values (seeMaterials and Methods). The rest of the countries and edges are drawn with gray. The sizes of the colored disks are proportional to the logarithm oftheir total trade activity, ln(Ei+Ii). The thickness of the directed links is proportional to the log value of the trade flux in that direction, ln(Wij). Thestructure of the IFTN was similar throughout 1998–2008.doi:10.1371/journal.pone.0037810.g002

The Interwoven Nature of the Human Food Web

PLoS ONE | www.plosone.org 2 May 2012 | Volume 7 | Issue 5 | e37810

Source: Ercsey-Ravasz M, Toroczkai Z, Lakner Z, Baranyi J (2012) Complexity of the International Agro-Food Trade Network and Its Impact on Food Safety. PLoS ONE 7(5): e37810. doi:10.1371/journal.pone.0037810

Each trading with over

77% of all the countries

in the world

4

THE WORLD WE KNOW IS MORE LOCAL THAN GLOBAL


Source: Pankaj Ghemawat and Steven A. Altman: DHL GLOBAL CONNECTEDNESS INDEX 2016. The State of Globalization in an Age of Ambiguity


Increasing

volume &

complexity

of the food

chain

Growth of the

amount of data

available

for analysis

6

INFORMATION BOOM

• From the dawn of the civilization to 2003 humans produced 5 exabytes of data in total

• IBM has estimated in 2016 that 2.5 exabytes (2.5 million terabytes) of data are produced every day

• Now it is around 5 exabytes daily

• We are in the middle of a transition from a society of facts to a society of data

• Numbers are being generated much faster than we have any specific use for


Source: https://www-01.ibm.com/software/data/bigdata/what-is-big-data.html

7

POST-NORMAL SCIENCE

• Policy-related scientific problems:

• uncertain facts

• disputes over ethics and values

• urgent decisions needed

• that may have far-reaching consequences

• Policy makers are required to make difficult and firm decisions based on data characterized by high levels of uncertainty.



Increasing

volume &

complexity

of the food

chain

Growth of the

amount of data

available

for analysis

Better

evidence-based

decision

making?

9

COMPUTATIONAL SCIENCE AS A SOLUTION

• Computational science:

• Able to detect patterns which can not be detected by a smaller set of data

• Those emerging patterns can be surprising & counter-intuitive

• 'more is different’

• What do we need to achieve this?


10

DATA ANALYSIS FRAMEWORK


DATA COLLECTION

& REPORTING DATA MODELLING

IT ARCHITECTURE DATA ANALYSIS

Commission &

EFSA

ME

MB

ER

ST

AT

ES

Data People

EFSA ADVISORY FORUM DATA COLLECTION AND MODELLING TASK FORCE


12

MAIN OBJECTIVE OF THE TASK FORCE

The main objective of the Task Force is to overview the data collection and reporting

processes and the data model and IT infrastructure used, from a strategic perspective,

and to formulate recommendations at a strategic level.


13

DATA COLLECTION


Inventory of MS reporting

needs/tasks was mapped (>120)

’microbiological hazards’

’IMSOC’

’chemical hazards’

’traceability’

'animal health’

’ECHA’

’food composition’

14

DATA COLLECTION

Important hubs identified:

IMSOC, SIGMA, chemical monitoring, foodborne pathogens

Important modules identified:

food composition

traceability

Many similar/overlapping/parallel report flows

Many connections could/should be improved


15

DATA MODELLING

A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to properties of the real world entities.

Data models are specified in a data modelling notation, which is often graphical in form.

Why data modelling is important?

Data is an asset of your organization

Needs to be understood to be managed

Don’t need to look at the detail right away (or sometimes ever)

An aid to understanding

Provides a common vocabulary


16

DATA MODELLING Suggestions for the extension of existing formats and formation of new ones


Wechsel der

Textebene

im Menü über:

Start

Listenebene

erhöhen/verring

ern

Formatvorlag

en

Ansicht // Anzeigen // Haken bei Führungslinien setzten

Folie in

Ursprungsform

bringen über

Menu:

//

Zurücksetzen

Wechsel des

Folienlayouts

im Menü über:

//

Layout

Aufzählung erste Ebene:

•Erste

Ebene

Aufzählung zweite

Ebene:

Aufzählung dritte Ebene:

verring

Connections

Fußzeile Seite 2

Foodborne outbreaks and epizootic

diseases (FBO data format, EFSA)

SSD2 for controls data

Tracing

Food

consumption

registered and approved

establishments/ holdings

SIGMA

Safeguard

Measures

rele

as

eq

uan

tities

or

trad

eo

f

ve

terin

ary

pro

du

cts

/

ph

arm

ac

eu

tica

lso

rp

lan

t

pro

du

ctio

np

es

ticid

es

/ pro

du

cts

Vector

distributions

parameter

1:N

EFSA param

catalogue

parameter

Sample

information

Country of

origin

N:N

product

1

M

Country of sample

FBO national code

Vector *

catalogue

Zoonosis *

catalogue

* parameter

1

N

parameter

parameterN:1

1:N

parameter

1:N

N:1

Food

vehicle

pro

du

ct

N:1

1:N

N:1

Matrix =

FoodEx2

N:M

Product = FoodEx2

Food vehicle

N:M

Product

Food vehicle FBO national code

Country of sampling

N

1

Ncausative

agent

disease

1

Production

Type

Food vehicle

N

1

brandname 1

prodBrandName NProduct FoodEx2N

N Product FoodEx2

animals

Pesticides/ veterinary

products*

Veterinary

residues* NVeterinary

products*1

parameter

N:1

1:N

Product

ID

Food vehicle

1:1parameter

Product

ID

Causative agent

17

DATA ARCHITECTURE


Traditional paradigm: give me all your data

EU

MS MS

MS

MS

MS

MS

MS MS

MS

MS

MS

MS

MS

18

DATA ARCHITECTURE


This pattern is repeated in every stage

EU

MS MS

MS

MS

MS

MS

MS MS

MS

MS

MS

MS

MS MS

Dep

Agency

Municipality

Industry Lab

Other stuff

More stuff

MS

Dep

Agency

Municipality

Industry Lab

Other stuff

More stuff

19

DATA ARCHITECTURE

Too many data models (pipelines)

Data models become obsolete

High cost of redesigning

Added complexity

No added value

Reporting data becomes a task with no added value

Data submitted is a subset of data already processed

Inefficient use of resources

The data is repackaged, wasting resources

Clunky reporting mechanism (Sending files. Trying to understand error messages…)


20

FURTHER CHALLENGES


Source: IBM

21

DATA ARCHITECTURE


Current paradigm

Stuff Database Insights

Analytics Machine

Learning

AI Models

Schemas

Quality BI Pipelines

22

DATA ARCHITECTURE


So, what if…

Stuff Insights

Analytics Machine

Learning

AI

BI

23

DATA ARCHITECTURE


So, what if…

Data Lake (Unstructured data)

Data Warehouse (structured)

Data Exploration Data Analytics Data Discovery

Analytical Sandboxes

Data Sources

Reports

Insights

The living opinion

Results

STUFF Aggregation level

interoperability

Predictions

”Smart Ecosystem” or

Connected database

DATA DRIVEN ORGANIZATIONS


25

DATA DRIVEN ORGANIZATION

Data is in the core of business activities

It drives the strategy

Organizational, procedural, capacity building changes

data-informed culture, agile working, room for experiments…

more expertise on data is needed → education


26

PEOPLE

Creation and development of (big) databases is not only an IT problem

The ability of analysis and evaluation of input data and results: high-level knowledge

of food chain science is needed enabling interpretation and validation.


Source: https://xkcd.com/688/

27

DATA LITERACY


We need data literate people

Data literacy: spectrum of related skills

MIT: the ability to

read

work with

analyse

and argue with data

ARE WE READY?


29

ARE WE READY?

We need to invest in data sharing and exploring capacities

We need careful strategic planning for multiple timelines

Building the future may destroy some of the current investments/achievements

IT systems become obsolete after 7-10 years → build from scratch is better than patching

Striving for ”full” standardization vs Connected Databases

Expectation Management & Change Management


30

ARE WE READY FOR THE FUTURE?


REACTIVE

Ability to share information and react to the current events

RAPID ANALYSIS

Ability to quickly assess the situation, and conduct an apidemiological

ivestigation

PREDICTIVE

Ability to predict risks and prevent adverse events from occurring, based on past data

REALTIME

Ability to predict risks and prevent adverse events from occurring, based on

past and real time data

FOOD SCIENTISTS WILL NOT BE REPLACED BY AI…

CONTACT Ákos Józwiak [email protected]


...THEY WILL BE REPLACED BY FOOD SCIENTISTS

USING AI

40 years of RASFF: “All you need is RASFF?” – theme: smarter data = better analysis 13.12.2019. Brussels

ROLE OF DATA SCIENCE IN FOOD CHAIN SAFETY DECISION … · 2020-01-27 · 3 7 COUNTRIES FORM THE...

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