An expert’s post-pandemic perspective on data semantics
Data semantics is needed now more than ever as countries struggle under the impact of the global pandemic. By utilizing data semantics, data sharing, analysis and presentation can be easily understood and used by various people to improve their situation in a post-pandemic world.
With this, Public Spectrum caught up with an expert in the field of data semantics. Dr. Renato Iannella, the Lead Enterprise Data Architect from the Airservices Australia, shares his views on how data semantics can influence the discourse and approach of the global community into embracing post-pandemic challenges.
How can semantics be useful in effective delivery of government services post-pandemic?
Dr. Renato Iannella: The post-pandemic era means we need to exploit data resources to extract the best value whilst minimising costs. The semantic approach is one of many information frameworks that could be applied for these purposes. The idea is that data can exposed to many different information management techniques to determine the best-case outcomes.
What are your thoughts on easy access to relevant government data and information?
Dr. Renato Iannella: I think “open data” should be a requirement as the value-add from the public has shown to be economically and socially beneficial. The use of semantic technologies in open data is essential to gain that deep understanding and transparency.
Why is the Semantics Approach important in government data management?
Dr. Renato Iannella: The “semantic approach” to data management is exemplified when data relationships need to be exposed (more than just data facts) and sharing of data needs alignment to common vocabularies. This means new insights in how data is related and described can become evident and lead to change. For example, semantic systems can infer the type and category of “data things” and classify them accordingly.
How did the Australian government fair in data management during the COVID-19 pandemic?
Dr. Renato Iannella: Government data is typically siloed in agencies so there is a lost opportunity to bring the data together and infer new relationships that can help in current and future decision making. For example, an agency approving major infrastructure projects may result in the impact to aviation services which could be automatically inferred.