Data Management and Privacy Governance Lab Data Collec9on Data Prepara9on Data Storage Data Analysis & Usage On the Horizon: Smart Agriculture and Big Data Dr. Rozita Dara Assistant Professor School of computer Science University of Guelph
an all encompassing term for any collec>on of data sets so large and complex that it becomes difficult to process using on hand data management tools or tradi>onal data processing applica>ons Source: heps://ipp.cifs.cornell.edu/sites/ipp.cifs.cornell.edu/files/shared/wiedmann%20ipp%20summit.pdf
Four Vs of Big Data hep://www.ibmbigdatahub.com/sites/default/files/infographic_file/4- Vs- of- big- data.jpg
Agriculture Data Growth heps:// csironewsblog.files.wordpress.c om/2013/06/smart- farming- infographic_final- png.jpg
Supply Chain Complexity A cup of Starbucks coffee can depend on 19 countries: coffee, milk, sugar, paper cup, and other factors.
emerging space presents new challenges, and not just for the pharmaceutical animal health company but rather for all GAP index shortfall and to enhance food security as a result of a rapidly growing world population. GLOBAL DEMAND FOR MEAT 2005 vs 2050 (in tonnes) 2005 2050 181M 143M 64M 106M 100M 82M 62M 102M 13M 25M BEEF MUTTON PORK POULTRY EGGS Figure 2: Global demand for meat in 2050 (adapted from FAO, 2012; Gates Notes 2013) Global broiler produc>on market stands at approximately 82 million tones of meat.
hep://image.slidesharecdn.com/kpmgbigdatainhealthcare- 13454169797539- phpapp01-120819175826- phpapp01/95/big- data- in- healthcare- 6-728.jpg?cb=1345399180
Supply Chain Complexity A cup of Starbucks coffee can depend on 19 countries: coffee, milk, sugar, paper cup, and other factors. hep://www.fao.org/3/a- ae930e/ae930e09.htm
5. What are the potential revenue streams? 6. What are the challenges to us accomplishing our goal? Data Driven Business Model Third Party Involvement Issues Data Erasure Issues 1 Target Outcome Using big data to improve the PLF process management and for targeted delivery of drugs to individual animals. Data Collection Issues 4 Key Offering Data acquisition: Capturing and recording multiple attributes of each animal such as age, pedigree, growth rates, etc. Aggregation: Integrate data from different devices. Descriptive analytics: Temporal trend analysis, for example, monitor animals size and weight gain. Predictive analytics: Predict the estimated real-time process output. Prescriptive analytics: Enable interventions to ensure target trajectory is met. 5 Revenue Model Potential revenue streams include usage fees, purchase of sensor devices, subscription fees. Data use may support other business core products providing market insight. 2 Offering Data: Continuous sensing of outputs (process responses) at appropriate scale and frequency, with data fed back to the process controller. Information: A target value and trajectory for each process output such as growth rates, behaviour patterns. Knowledge: Actuators and a predictive controller for the process inputs. Consent Quality Issues 3 Data Source Internal: Batch data collected by sensor devices such as herd/flock camera systems, automatic weighing devices, vocalisation monitors, cough monitors, electronic identification ear tags (EID) and pedometers. External: Data obtained from collaboration with related parties, for example, feed manufacturers working with weight-monitoring PLF companies. User Access and Control Issues Figure 3: PLF-DDBM Innovation Blueprint hep://cambridgeservicealliance.eng.cam.ac.uk/resources/monthly%20papers/2015julycasestudyioaht_hqp.pdf
Enabling Technology: Decision Support System Weather Decision Lab data Farm Data Extract Transform Load (ETL) Ac>on Maps Report
Big Data Challenges 1. Poor integra>on into prac>>oners workflow 2. Low level of uptake by the stakeholders 3. Cost and >me required to develop the data management sysem 4. Inadequate infrastructure (e.g. IT) 5. Interoperability: technical and opera>onal 6. Poor evalua>on of stakeholders needs 7. Lack or limited commitment of the subject maeer experts 8. Data availability and quality 9. Analy>cal challenges 10. Scalability
Data Prepara9on Fa9gue
Solu9ons: Building the Team Subject maeer expert: biologist, engineer, health specialist, Vet, someone who knows the problem domain Data scien>sts: sta>s>cian, data mining expert Privacy specialist: engineer, requirement engineer, policy analyst, lawyer Stakeholders: end user (e.g. farmer), policy maker, consumer, general public Story teller: end user, subject maeer expert, marketer
Thank you!