how to improve business strategy or
when and how to carry out reengineering of a business process using big data.
As a result, opportunities for improving
business performance can be lost.
For this reason, the BD-IRIS framework needs to be structured in all
seven dimensions. The main innovation is the BD-IRIS methodology dimension, along with the fact that it
takes into account all the dimensions
a big data framework should have
within a single framework. The BD-IRIS methodology represents a guide
to producing a big data ecosystem according to a process, covering the big
data project life cycle and identifying
when and how to use the approaches
proposed in the other six dimensions.
The utility of the framework and its
completeness, level of detail, and accuracy of the relations among the
methodology tasks and the approaches to other dimensions were validated
in 2016 by five expert professionals
from a Spanish consulting company
with experience in big data projects,
and by managers of the two organizations (not experts in big data projects)
participating in our case studies. Lack
of validation is a notable weakness of
the existing frameworks.
This article has explored a framework
for guiding development and implementation of big data ecosystems. We
developed its initial design from the
existing literature while providing additional knowledge. We then debugged,
refined, improved, and validated this
initial design through two methods—
expert assessment and case studies—in
a Spanish metal fabrication company
and the Spanish division of an international oil and gas company. The results
show the framework is considered valuable by corporate management where
the case studies were applied.
The framework is useful for guiding
organizations that wish to implement
a big data ecosystem, as it includes a
methodology that indicates in a clear
and detailed way each activity and
task that should be carried out in each
of its phases. It also offers a comprehensive understanding of the system.
Moreover, it provides control over a
project and its scope, consequences,
opportunities, and needs.
Although the framework has been
validated through two different methods—expert evaluation and case studies—it also involves some notable limitations. For example, the methods we used
for the analysis and validation in the two
case studies are qualitative and not as
precise as quantitative ones and based
on the perceptions of the people involved
in the application of the framework in
the case studies and the consultants who
evaluated it. Moreover, the evaluation
experts were chosen from the same consulting company to avoid potential bias.
Finally, we applied the framework in two
companies in two different industrial
sectors but have not yet tested its validity
in other types of organization.
Regarding the scope of future work,
we are exploring four areas: apply and
assess the framework in companies
from different industrial sectors; evaluate the ethical implications of big data
systems; refine techniques for converting different input data formats into a
common format to optimize the processing and analysis of data in big data
systems; and finally, refine the automatic identification of people in different
social networks, allowing companies to
gather information entered by the same
person in a given social network.
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Sergio Orenga-Roglá ( email@example.com) is a
researcher in the Systems Integration and Re-Engineering
(IRIS) research group at the Universitat Jaume I,
Ricardo Chalmeta ( firstname.lastname@example.org) is an assistant
professor in the Department of Computer Languages and
Systems and Director of the Systems Integration and
Re-Engineering (IRIS) research group at the Universitat
Jaume 1, Castellón, Spain.
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