IDA's Smart Nation: Data Works event at Hotel Fort Canning, Singapore on 28 and 29 October 2014.
To support its Smart Nation vision, the Infocomm Development Authority of Singapore(IDA) today (28 October 2014) launched the Data-as-a-Service (DaaS) pilot at the Smart Nation: Data Works event.
According to IDA's press statement, the pilot seeks to address the challenge of discovering datasets in the private sector through a federated approach. Since there is currently no coherent mechanisms for users to easily discover private sector datasets from data providers, the DaaS platform — also known as the Federated Dataset Registry (FDSR) — is aimed at enabling a mechanism to ease dataset discovery. The platform is collectively made up of individual Dataset Registries (DSR) based on the open source data portal platform CKAN (Comprehensive Knowledge Archive Network).
Steve Leonard, IDA's executive deputy chairman, believes that this platform is an important building block to helping Singapore become a smart nation. This is because it allows the republic to capture and analyse data that can be used to take "meaningful actions to address the urban challenges" in the areas of healthcare and energy.
The DaaS platform is free for use for data providers from all industries. Interested data providers can participate in the pilot -- which will last till 31 March 2016 -- by downloading the DSR software from http://DaaS.sg.
To help kick-start the pilot, IDA has signed a Memorandum of Intent with Amazon Web Services (AWS). Under that agreement, AWS will provide US$3,000 worth of usage credits to the first 25 data providers that host their DSR instance on the AWS cloud during the pilot. Additionally, AWS will provide up to four technical workshops and two business mentoring workshops annually for data providers.
Features of the DaaS pilot
Under the pilot, participating data providers will operate their own instance of DSR which runs on their own infrastructure. To be maintained by the data provider, each DSR comes with a dataset catalogue containing details of data sets such as meta-information and sample datasets.
Data providers are also encouraged to profile their datasets using a set of data quality metrics (DQM), and provide these metrics for users through the dataset catalogue. DQM serves as guidelines that describe the methodology for deriving a set of metrics and consider tools for relaying metrics to end-users. These guidelines can be generated via open-source data quality tools such as the Human Inference's Data Cleaner. With DQM, data providers are able to understand and check the relative quality of their datasets in areas such as reliability, relevance, accessibility, timeliness and ease of use, said IDA.
Sign up for CIO Asia eNewsletters.