Technologies
Data technology helps to collect, access, use or share data more easily and effectively.
Technology is the part of data infrastructure that people are often the most familiar with. We usually think of technology as either hardware, such as a computer, or software, like the programmes we use on our computers. A ‘data technology’ is probably best described as any tool - hardware or software - that enables us to collect, access, use or share data more easily and effectively.
Technology can help to improve access to data for people around the world, through data portals hosted on the web or open APIs. We can use computers to analyse large amounts of data in a fraction of the time that it takes a human to complete a similar task. We rely on technology to help us deliver useful digital services for customers and citizens.
Ultimately, data technology is a mechanism to derive value from data. If technology is not created to be fit for purpose, or becomes outdated, it could prevent us from creating useful outcomes from data. We must evolve technology in a way that continues to help us use data to make better decisions, while also making sure that what we create is used responsibly and ethically.
Creating or adopting open source technologies is a great way to make sure that data infrastructure is reliable and sustainable. Open source technology is generally more cost effective, especially for re-users, and tends to be more reliable than many proprietary alternatives, as open source technologies usually have communities of collaborators which help to identify problems and contribute to updating the software. Using open source software also makes it far easier for users to switch suppliers, if they find a better fit.
Smart Data schemes rely on a host of different technologies to help improve access to data, to test data-driven propositions, and to ensure data privacy. Open APIs have been a critical piece of data infrastructure for data innovation, from Open Banking to OpenActive. Various 'sandbox' technologies, such as those found in data innovation programmes run by the FCA and the ICO Innovation Hub have helped many businesses and solutions go to market. A wide variety of data privacy technologies reduce the potential risks of increasing access to data, as shown in the guide below.
The PETs Adoption Guide
A privacy enhancing technology (PET) is any technical method that protects the privacy or confidentiality of sensitive information. This broad definition covers a range of technologies, from relatively simple ad-blocking browser extensions to the Tor network for anonymous communication.
The PETs Adoption Guide by the Centre for Data Ethics and Innovation (CDEI) is a question-based flowchart to aid decision-makers to investigate how PETs could be used to support both Smart Data schemes and innovation in Smart Data. For example:
The guide aims to not be overly prescriptive, and does not claim to cover all use cases of PETs. Rather, it seeks to support decision-making around the use of PETs by helping the user explore which technologies could be beneficial to their use case. Whether a particular PET provides a suitable solution will depend on the specific context, and results from the guide should be read as suggestions to explore rather than definitive solutions.
It is also important to understand that using an individual PET does not in itself guarantee an improvement in privacy unless accompanied by a good overall privacy design, and appropriate governance arrangements. CDEI provides good practices for sharing and processing data here.
Other technical resources...
...focussed on privacy enhancing technology:
A step-by-step Python tutorial for developers wishing to understand synthetic data, using the example of synthetic A&E (accident and emergency) health data
Anonymisation: A Short Guide by Eticas Research
The ICO's 'Data protection by design and default' guidance also looks into the role of PETs in minimising personal data use, maximising data security, and empowering individuals
...focussed on API guidance:
Web API design best practices | Microsoft Azure – Very long and detailed, but high-quality, technical introduction to good API development
API technical and data standards | UK Government Digital Service – “The following web-based application programming interface (API) standards guidance will help your organisation deliver the best possible services to users”
Open standards and open APIs | The Open Data Institute – A collection of projects from different sectors which use and apply APIs
...focussed on dataset markup:
Dataset markup | Schema.org
...focussed on data quality assurance:
Lintol – which adopts a ‘plug and play’ approach to data quality
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