IEEE Technology and Society Magazine - June 2020 - 73

Operators of data collaboration platforms will need
to manage four core functions: data integration; systems interoperability; data provisioning; and data quality control. Tasks will include managing authentication
and data access filters among participants; managing
data integration; administering data cleansing and
aggregation functions to meet privacy and other regulatory requirements; managing data cloud, residency, and
retention policies; designing and operating appropriate
data dashboards for access and queries; monitoring
data flows and transactions and managing smart contracts between participants; enforcing rules regarding
data reuse and data transfers; managing connections
with other APIs; and reporting on activities and outcomes. In some cases, data access organizations may
also manage the IP and the licensing of algorithms and
solutions on behalf of all participants.

Data Analytics and Solutions
This third segment of activities will be undertaken by a
number of organizations from civil society, governments, academic and research organizations, and small
and medium-sized enterprises (SMEs) engaged in AI and
machine learning. Analytics functions could operate in
a central location in an "IoT lab" in order to foster collaboration between participants. They could also operate in a decentralized way with each organization
negotiating appropriate access rights to data in order to
access data and determine how best to use it.
By relying on IoT labs or commercialization incubators as vehicles for generating data insights, supply
chain participants would be able to articulate to AI specialists the most urgent problems to solve. They could
provide guidance on data availability and quality and
test solutions and insights as they get developed.
Organizations engaged in data analytics will need
standards to ensure that algorithms and solutions
respect applicable regulations, ethical guidelines, and
are seen as trustworthy.

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Data Governance Standards
Advances in digitization allow organizations to gather
and store more data, enabling smarter and quicker decisions, but they are also giving rise to a new series of
issues. How do organizations collect and distribute the
right data at the right time? How should organizations
deal with data ownership and copyright? How should
personal information be treated? What rules should
organizations follow regarding data residency? What are
acceptable practices for the use of automated decision
systems relying on artificial intelligence?
Although some of these issues can be handled solely
by organizations, many are framed by governments
through enabling laws, regulations, and policies. As
JUNE 2020

regulators are not equipped to keep pace with rapid
technological advancement, the DSTF would create a
series of standards committees to develop and maintain
the necessary foundational data governance standards.
These standards would frame how digital cooperation
initiatives and big tech platforms operate in the single
data area. The standards committees would be composed of representatives from governments, industry,
civil society, and academics. They would operate
through the established standards development process.
Jurisdictions participating in the DSTF should follow
an approach similar to the one in place in the European
Commission whereby they are accountable to make
standardization requests to the DSTF regarding data governance issues that should to be standardized, giving a
clear mandate to the DSTF to launch standards committees as needed. Regulators would be called on to play a
central role in the development of data governance standards to ensure they meet their policy objectives. Once a
standard is developed, there would then be a presumption of conformity on the part of governments to adopt
the standards and incorporate them by reference in regulations. This process would establish minimal standards that nation states would agree to adhere to. The
establishment of such minimal standards is paramount
in order to establish a single data area.
Data governance issues that will likely require standardization support include:

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Guidance on asserting ownership/IP/copyright over
data collected by organizations and on tagging and
tracking data use in the single data area;
Guidance on data valuation for the purposes of
financial reporting and taxation;
Ensuring compliance to relevant privacy/digital identity requirements;
Ensuring compliance with relevant labor rights;
Facilitating personal data portability between data
collaboration platforms
Ensuring compliance with human rights regulations
and requirements;
Establishing and enforcing relevant safe use, ethics,
and trustworthiness principles to data analytics, AI,
machine learning, and solutions;
Choosing and applying appropriate cyber security
controls to data collection/access/analytics architecture, which may include encryption;
Defining appropriate data sovereignty and residency
requirements to meet requirements under the single
data area;
Guidance on appropriate professional credentials
and accountability for Chief Data Officers accountable for data governance in organizations as well as
data engineers (responsible for data collection and

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IEEE Technology and Society Magazine - June 2020

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