Page 221 - Proceedings of the 2017 ITU Kaleidoscope

Session 3: Accelerating sustainable development through data
Capability maturity models towards improved quality of the Sustainable Development Goals
S3.1
indicators data
Ignacio Marcovecchio (United Nations University, Macao SAR, China), Mamello Thinyane
(United Nations University, Macao SAR, China), Elsa Estevez (National University of the South,
Argentina), Pablo Fillottrani (National University of the South, Argentina)

Achieving the Sustainable Development Goals (SDGs) demands coping with the data revolution
for sustainable development: the integration of new and traditional data to produce high-quality
information that is detailed, timely, and relevant for multiple purposes and to a variety of users.
The quality of this information, defined by its completeness, uniqueness, timeliness, validity,
accuracy, and consistency, is crucial for appropriate decision making; which leads to
improvements in advancing national development imperatives for reaching the goals and targets
of the sustainable development agenda. In this paper, we posit that the more mature the
organizations within the national data ecosystems are, the higher the quality of data that they
produce. The paper motivates for the adoption and mainstreaming of organizational Capability
Maturity Models within the SGDs activities. It also presents the preliminary formulation of a
multidimensional prescriptive Capability Maturity Model to assess and improve the maturity of
organizations within national data ecosystems and, therefore, the effective monitoring of the
progress on the SDG targets through the production of better quality indicators data.
Furthermore, the paper provides recommendation towards addressing the challenges within the
increasingly data-driven domain of social indicators monitoring.

Advanced data enrichment and data analysis in manufacturing industry by an example of laser
S3.2 drilling process*
You Wang (RWTH Aachen University, Germany), Hasan Tercan (RWTH Aachen University,
Germany), Thomas Thiele (RWTH Aachen University, Germany), Sabina Jeschke (RWTH Aachen
University, Germany), Tobias Meisen (RWTH Aachen University, Germany), Wolfgang Schulz
(RWTH Aachen University & Fraunhofer Institute for Laser Technology, Germany)

Nowadays, the internet of things and industry 4.0 from Germany are all focused on the
application of data analytics and Artificial Intelligence to build the succeeding generation of
manufacturing industry. In manufacturing planning and iterative designing process, the data-
driven issues exist in the context of the purpose for approaching the optimal design and
generating an explicit knowledge. The multi-physical phenomena, the time consuming
comprehensive numerical simulation, and a limited number of experiments lead to the so-called
sparse data problems or "curse of dimensionality". In this work, an advanced technique using
reduced models to enrich sparse data is proposed and discussed. The validated reduced models,
which are created by several model reduction techniques, are able to generate dense data within
an acceptable time. Afterwards, machine learning and data analytics techniques are applied to
extract unknown but useful knowledge from the dense data in the Virtual Production Intelligence
(VPI) platform. The demonstrated example is a typical case from laser drilling process.

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