Data is a fundamental resource for organisations to support their decision making. Leading organisations are applying data analytics to generate business intelligence from the abundance of data from new publically available sources and innovations in data collection methods such as advanced sensors and smart phones. However, if this information is of poor quality, or is poorly managed, then it can become as much a burden as it is a useful resource. Organisations frequently speak of silos of information, garbage in garbage out, and information overload. News sources are littered with embarrassing examples of the negative effects of having used data that was low quality. DIAL’s Data Management theme endeavours to work towards solving these problems and providing organisations with the data they need, when they need it, and at the right level of quality. We aim to discover new and exciting ways of improving data used for organisational decision making including:
1. How to architect an information system to provide enhanced data quality
2. Automating and improving data quality problem detection and correction
3. Understanding which data is most valuable to an organisation and avoiding data overload
4. Facilitating the integration and sharing of data between organisations
In December 2016 the final deliverables from the ITALI project (Information Technology Architectures for Logistics Integration) were handed over to the partner organisation YH Global as part of the project’s conclusion. The focus of the two year project was to develop an IT architecture for a next generation Information Technology (IT) system in warehousing and logistics. YH Global is now implementing this architecture within the whole business and has set about recruiting new staff and setting up new teams to start its development.
The team is also further developing the data state tracking solution (the first paper about this solution was lucky enough to win the best paper award at ICIQ 2016), and has recently found that it can be applied to help mitigate the disruptions caused by various other inaccuracy problems as well those in warehousing operations on which the paper was based. See: Woodall, P., Giannikas, V., Lu, W., McFarlane, D. (2016), Data State Tracking: labelling good quality data to improve warehouse operations. International Conference on Information Quality (ICIQ).
A Knowledge Transfer project which included the development of a software tool for the Total Information Risk Management approach (KT-Box)
Dr Philip Woodall, firstname.lastname@example.org
Dr Alexandra Brintrup, email@example.com
Pascal Wichmann, firstname.lastname@example.org
Woodall, P. (2017). The Data Repurposing Challenge: New Pressures from Data Analytics. ACM Journal of Data and Information Quality, to appear.
Woodall, P., Borek, A., & Parlikad A. K. (2016). Evaluation Criteria for Information Quality Research. International Journal of Information Quality 4, (2), pp.124-148
Woodall, P., Giannikas, V., Lu, W., McFarlane, D. (2016), Data State Tracking: labelling good quality data to improve warehouse operations. International Conference on Information Quality (ICIQ).
Woodall, P., Oberhofer, M., & Borek, A. (2014). A Classification of Data Quality Assessment and Improvement Methods. International Journal of Information Quality, 3 (4), pp.298–321.
Borek, A., Parlikad, A. K., Woodall, P., & Tomasella, M. (2013). A Risk-Based Model for Quantifying the Impact of Information Quality. Computers in Industry, 65 (2), pp.354–366.
Woodall, P., Borek, A., & Parlikad, A. K. (2013). Data Quality Assessment: The Hybrid Approach. Information & Management, 50 (7), pp.369–382.
Borek, A., Parlikad, A., Webb, J., & Woodall, P. Total Information Risk Management: Maximizing the Value of Data and Information Assets. Morgan Kaufmann Publishers.