Senior Lecturer, Computer Science
Dr Philip Woodall specialises in data management and data quality. He has extensive experience working with international public and private organisations from various sectors, including transport, utilities, e-commerce, public sector, manufacturing, and aerospace, to improve their data management and data quality practices.
He is author of “Total Information Risk Management: Maximizing the Value of Data and Information Assets”, and has published numerous academic articles in leading international journals and conferences. He is an editor of The International Journal of Information Quality.
Philip spent a decade at the University of Cambridge where he led various data management research projects. He has also worked in the software industry, and gained his Ph.D. in Computer Science from Keele University.
- Data management: how to effectively and accurately acquire, transfer, store, process, analyse, and report data (be it from an information system, website or paper-based document).
- Data quality: 1) how to detect, assess, and measure data errors, 2) how to improve and correct data, and 3) how to sustain good data over time.
- Example applications:
- Data tagging: novel approaches to indicate what data is accurate in existing information systems (e.g. databases)
- Smart cities, homes, Internet of Things (IoT): accurate collection of data, meaningful integration (semantic interoperability), master data management and data governance.
- Digital twins: ensuring accurate data input from the real life phenomenon
- E-commerce and logistics: evolution of enterprise information architectures due to rapid sector growth
- Health informatics: accurate, consistent, and integrated patient data, smart sensor data, data for automated diagnoses. Trade-offs between data availability (e.g. for emergencies) and levels of accuracy.
- Data analytics: repurposing existing data to drive insights beyond the original intended use of the data
- Business intelligence: automatically extracting and leveraging data from public sources for business intelligence (e.g. inferring supply chain maps using online news articles), dealing with unstructured data
- Online data: fake news, disinformation, rapid propagation of inaccurate data: often arising in social media, it can lead many of us to believe, and sometimes act on, facts that are simply not true (e.g. the Pizzagate shooting).
A full list of publications: https://www.researchgate.net/profile/Philip_Woodall
PDF access: folder of publications
P. Woodall, V. Giannikas, W. Lu, and D. McFarlane, ‘Potential Problem Data Tagging: Augmenting information systems with the capability to deal with inaccuracies’, Decision Support Systems, vol. 121, pp. 72–83, 2019, doi: 10.1016/j.dss.2019.04.007.
P. Woodall, A. Borek, and A. K. Parlikad, ‘Data quality assessment: The Hybrid Approach’, Information & Management, vol. 50, no. 7, pp. 369–382, Nov. 2013, doi: 10.1016/j.im.2013.05.009.
P. Woodall, M. Oberhofer, and A. Borek, ‘A Classification of Data Quality Assessment and Improvement Methods’, International Journal of Information Quality, vol. 3, no. 4, pp. 298–321, 2014, doi: 10.1504/IJIQ.2014.068656.
A. Borek, A. K. Parlikad, J. Webb, and P. Woodall, Total Information Risk Management: Maximizing the Value of Data and Information Assets, 1st edition. Morgan Kaufmann, 2013. https://www.elsevier.com/books/total-information-risk-management/borek/978-0-12-405547-6
Q. Lu et al., ‘Developing a Digital Twin at Building and City Levels: Case Study of West Cambridge Campus’, J. Manage. Eng., vol. 36, no. 3, p. 05020004, 2020, doi: 10.1061/(ASCE)ME.1943-5479.0000763.
P. Wichmann, A. Brintrup, S. Baker, P. Woodall, and D. McFarlane, ‘Extracting supply chain maps from news articles using deep neural networks’, International Journal of Production Research, vol. 58, no. 17, pp. 5320–5336, Sep. 2020, doi: 10.1080/00207543.2020.1720925.
P. Woodall, ‘The Data Repurposing Challenge: New Pressures from Data Analytics’, Journal of Data and Information Quality, vol. 8, no. 3–4, p. 11:1-11:4, 2017, doi: 10.1145/3022698.
A. Borek, A. K. Parlikad, P. Woodall, and M. Tomasella, ‘A risk based model for quantifying the impact of information quality’, Computers in Industry, vol. 65, no. 2, pp. 354–366, 2014, doi: 10.1016/j.compind.2013.12.004.
DataIQ article: https://www.dataiq.co.uk/articles/articles/using-top-quality-data-make-better-decisions
Youtube video on data quality research: https://www.youtube.com/watch?v=5zVFb-0HFpU