How to prove the ‘data collaborative’ concept?
TM Forum members are exploring potential synergies with the OPAL Project and other data collaborative projects to jointly address common challenges and opportunities.
02 Oct 2019
How to prove the ‘data collaborative’ concept?
In the first of this two-part series, TM Forum Distinguished Fellow Jenny Huang shared her experience judging the Taiwan Presidential Hackathon, pointing to synergies between that event and the Open Algorithms for Better Decisions Project (better known as the OPAL Project). A goal of each is to explore ‘data collaboratives’ that go beyond the public-private partnership model to help organizations securely exchange data for the greater good. At TM Forum Action Week in September, Huang led an exploratory session to discuss potential synergies between OPAL, TM Forum and other related projects to jointly address common data collaborative challenges and opportunities. In this article, she teams up with Dave Milham, TM Forum’s Chief Architect, to share the results and next steps.
Managing and using the vast amounts of data generated in the digital world is a huge challenge for enterprises, communications service providers (CSPs) and governments, alike. For governments, applying data science to the development of public services, decision-making and risk management is a new field, so they are looking to the private sector for models and joint development.
The Taiwan Presidential Hackathon and the International Track are examples of a data collaborative that aims to accelerate optimization and improve effectiveness of public services by closely working with industries, governmental organizations and citizens. This public, private and citizen engagement is based on data-driven principles and underpinned by a physical platform called Civil IoT Taiwan. The platform consists of several data owners and data stewards addressing issues such as air quality, water resources, weather condition and disaster management, as well as overall data integration and governance. Collectively, the platform provides high-quality, public IoT-based information to enable citizen engagement and co-create innovative solutions based on specific community/regional needs.
But there are many market challenges to extract value out of the massive amount of data produced by platforms like Civil IoT (see graphic below). The beneficiaries, such as governments and civic communities, often use different language than enterprises or CSPs, for example, so it can be difficult for the private sector to understand which kinds of data or combinations of it may be useful to the beneficiaries.
Managing and using the vast amounts of data generated in the digital world is a huge challenge for enterprises, communications service providers (CSPs) and governments, alike. For governments, applying data science to the development of public services, decision-making and risk management is a new field, so they are looking to the private sector for models and joint development.
The Taiwan Presidential Hackathon and the International Track are examples of a data collaborative that aims to accelerate optimization and improve effectiveness of public services by closely working with industries, governmental organizations and citizens. This public, private and citizen engagement is based on data-driven principles and underpinned by a physical platform called Civil IoT Taiwan. The platform consists of several data owners and data stewards addressing issues such as air quality, water resources, weather condition and disaster management, as well as overall data integration and governance. Collectively, the platform provides high-quality, public IoT-based information to enable citizen engagement and co-create innovative solutions based on specific community/regional needs.
But there are many market challenges to extract value out of the massive amount of data produced by platforms like Civil IoT (see graphic below). The beneficiaries, such as governments and civic communities, often use different language than enterprises or CSPs, for example, so it can be difficult for the private sector to understand which kinds of data or combinations of it may be useful to the beneficiaries.
In addition, there is no clear business model for CSPs to use when processing existing data for public good. For example, there are no agreed methods of ensuring data privacy or correlating with other sectors to inform actions, policy and regulations. Other questions must be answered as well: How much should a CSP charge for data? How should they convert raw data into statistical information? How can they ensure that data processing shields personal information?
In the TM Forum, we are working to address some of these challenges through Catalyst proofs of concept. For example, we are working in the transportation sector and with environmental organizations. But many of these efforts are siloed, and there are few standards for exchanging data. A data collaborative could bring all this knowledge together and provide a more systematic approach to addressing challenges.
This provides an opportunity for TM Forum to work with groups like OPAL, which is a non-profit alliance consisting of partners from the MIT Media Lab, Imperial College London, Orange, the World Economic Forum and Data-Pop Alliance, to prove the concept. OPAL aims to serve as “a trusted enabler to unlock the potential of data collected by private organizations by bringing the code to the data through open algorithms and safe and fair technological and governance systems.” The overall goal is to help organizations make better decisions in support of sustainable development goals around the world.
Until now, most of the Forum’s work has focused primarily on automation through processes and supporting application program interfaces (APIs). But introduction of the EU’s General Data Protection Regulation (GDPR) and the adoption of artificial intelligence (AI), machine learning and information-heavy approaches to networking requires revised technical approaches such as OPAL’s, which focuses more specifically on data – its collection, processing and governance – and on supporting APIs.
In Dallas, TM Forum members identified potential synergy with projects like OPAL on much of the work taking place inside the Open Digital Architecture project, which is part of the Open Digital Framework. For example, work on 5G service implementation; security and privacy; AI and data analytics; autonomous networks; Open APIs and the Ecosystem Business Architecture could all potentially benefit from OPAL’s data-centric API-based approach, which focuses on decentralizing data processing. This complements TM Forum’s Open API architecture, which members are testing in 5G Catalyst projects.
Addressing the challenges together
In the TM Forum, we are working to address some of these challenges through Catalyst proofs of concept. For example, we are working in the transportation sector and with environmental organizations. But many of these efforts are siloed, and there are few standards for exchanging data. A data collaborative could bring all this knowledge together and provide a more systematic approach to addressing challenges.
This provides an opportunity for TM Forum to work with groups like OPAL, which is a non-profit alliance consisting of partners from the MIT Media Lab, Imperial College London, Orange, the World Economic Forum and Data-Pop Alliance, to prove the concept. OPAL aims to serve as “a trusted enabler to unlock the potential of data collected by private organizations by bringing the code to the data through open algorithms and safe and fair technological and governance systems.” The overall goal is to help organizations make better decisions in support of sustainable development goals around the world.
Until now, most of the Forum’s work has focused primarily on automation through processes and supporting application program interfaces (APIs). But introduction of the EU’s General Data Protection Regulation (GDPR) and the adoption of artificial intelligence (AI), machine learning and information-heavy approaches to networking requires revised technical approaches such as OPAL’s, which focuses more specifically on data – its collection, processing and governance – and on supporting APIs.
In Dallas, TM Forum members identified potential synergy with projects like OPAL on much of the work taking place inside the Open Digital Architecture project, which is part of the Open Digital Framework. For example, work on 5G service implementation; security and privacy; AI and data analytics; autonomous networks; Open APIs and the Ecosystem Business Architecture could all potentially benefit from OPAL’s data-centric API-based approach, which focuses on decentralizing data processing. This complements TM Forum’s Open API architecture, which members are testing in 5G Catalyst projects.
How to get involved
TM Forum members are considering additional Catalyst proofs of concept to demonstrate the power of data collaboratives. For example, members would like to look at the requirements for AI and machine learning, data and privacy compliance, and the trend toward decentralized data processing in the context of 5G, and their potential impact on TM Forum’s current API architecture. The project could be based on OPAL’s conceptual architecture and its current open source implementation.
Another example is similar but would focus on data integration with vertical sectors and using data for good in the context of smart cities. The idea would be to show how to integrate approaches (from TM Forum, FiWare and OPAL, for example) and the impact on data standards, personal privacy and related architectures. We can also consider using the open source Acumos AI platform in data collaboratives and knowledge exchanges.
TM Forum has already begun exploring the concept of a data-driven smart city in a multi-phased project called Smart city: Service level management for smart city ecosystems and trusted IoT. Watch the video below to learn more, and if you are interested in exploring these important concepts further, please contact Dave Milham.