IEEE Technology and Society Magazine - June 2020 - 72

In addition, data will go through a life cycle from creation to disposal, which will also have to be described
and categorized.
We should anticipate that many standards and specifications will be required to properly frame data value
chains. The DSTF could to create new systems and
approaches to meaningfully involve a broader range of
stakeholders. Codes, standards, guidelines, best practices, and model technical regulations will be required
to cover both the technical and governance layers.
The structuring of the DSTF would need to reflect the
new realities of the digital age. Classical forms of governance do not apply. Technology moves so fast that by
the time decision makers gather to prepare, discuss,
approve, ratify, and implement a convention or new
agreement, the landscape has changed entirely. Analog
policymaking won't work in a digital world. In order to be
responsive, the DSTF would need to develop standards a
lot faster than the two to three years generally required
in traditional setting bodies. Once developed, one would
expect some of the standards to be "evergreen," that is
to be updated on an ongoing basis in order to reflect
new technologies and approaches and remain relevant.
Traditional standards setting bodies require a published
standard to be reviewed every five years [11].

Ontology, Semantics, Definitions, and
Terminology
When industrial sectors were mostly vertical in nature,
standards development organizations developed standards in silos. As a result, a multiplicity of domain-specific sema ntics, including product ter minolog y,
classification, and properties were created and maintained, sometimes for many decades. With digitization,
information is being generated and exchanged across
sectors. This leads to a demand for universal semantics,
which should follow a common ontological foundation.
Big data analytics are, by definition, higher-level functions and will need to be based on a common ontology.
It is a prerequisite for interoperability.

Technical Standards for Data Value Chains
The Internet and the World Wide Web will provide the
infrastructure backbone on which data value chains
will be built. As outlined in a recent CIGI paper entitled
"Standards for the Digital Economy," data value chains
are composed of three segments: data collection and
grading; data access, exchange and storage; and data
analytics and solutions [13]. Detailed standards, specifications, and guidance are needed to achieve interoperability and make it possible for data collected in one
data collaboration platform to be used by another within the single data zone. As technology evolves quickly,
data value chain standards should be updated as

needed and a preference may be given to organizations
that commit to evergreen standards.

Data Collection and Grading
Digital cooperation projects will require data from a multiplicity of sources to be successful. Existing datasets in analog format will be used in addition to digitized datasets
stored in various databases in a multiplicity of formats. Traditional organizational data originates from various operational systems such as enterprise resource planning (ERP),
customer relations management (CRM), finance, and
human resources (HR) systems-of-record. In addition to traditional datasets, digital collaboratives will increasingly rely
on streaming data from IoT devices, industrial sensors,
cameras, clickstreams, servers, and user app activity.
Metadata standards will be required to provide information about the characteristics of the data collection
apparatus and about data set attributes. These will be
needed in order to precisely describe the features of
available datasets; categorize and apply a grade to the
data to make inferences about its quality; and to label
datasets and ensure they are tagged with appropriate
intellectual property (IP) and copyright mechanisms
for traceability.

Data Access, Sharing, Exchange and Storage
This second segment of the data value chain is needed to make data accessible. It will serve as the interface to connect data sets with data users. New data
collaboration platforms will be created to manage and
track data flows on behalf of participants making data
available. And they will manage data access for AI and
machine learning organizations looking to generate
new insights.
Depending on the needs and constraints of participating organizations, the operations of this segment
could be decentralized across a supply chain (for
example, through data access models based on credentials) or centralized by physically pooling available data into data lakes, commons, trusts, marts,
pools, libraries, and so on. Standards will be re--
quired to describe and frame these different data
access methods.
In addition to choices about data access modalities,
interoperability issues will have to be addressed by data
access organizations. Central to interoperability is the
choice of an appropriate Application Programming Interface (API) to allow for data transmission, use, and tracking.
In 2018, there were more than 450 different data platforms available in the global marketplace, but the number
could soon reach close to 1000 different available platforms. Standards will be needed to set performance
requirements of APIs to be used in the single data zone
and ensure interoperability between platforms.

IEEE TECHNOLOGY AND SOCIETY MAGAZINE

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JUNE 2020

IEEE Technology and Society Magazine - June 2020

Table of Contents for the Digital Edition of IEEE Technology and Society Magazine - June 2020