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Cardoso, E. & Su, Xiaomeng (2019). Towards a Lean Assessment Model for Evaluating the Maturity Level of Business Intelligence and Analytics Initiatives in Higher Education. 25th International Conference on European University Information Systems (EUNIS 2019).

E. A. Cardoso and X. Su,  "Towards a Lean Assessment Model for Evaluating the Maturity Level of Business Intelligence and Analytics Initiatives in Higher Education", in 25th Int. Conf. on European University Information Systems (EUNIS 2019), Trondheim, 2019

@misc{cardoso2019_1713565187006,
	author = "Cardoso, E. and Su, Xiaomeng",
	title = "Towards a Lean Assessment Model for Evaluating the Maturity Level of Business Intelligence and Analytics Initiatives in Higher Education",
	year = "2019",
	howpublished = "Digital",
	url = "http://www.eunis.org/eunis2019/"
}

TY  - CPAPER
TI  - Towards a Lean Assessment Model for Evaluating the Maturity Level of Business Intelligence and Analytics Initiatives in Higher Education
T2  - 25th International Conference on European University Information Systems (EUNIS 2019)
AU  - Cardoso, E.
AU  - Su, Xiaomeng
PY  - 2019
CY  - Trondheim
UR  - http://www.eunis.org/eunis2019/
AB  - BI and analytics market is one of the fastest growing markets in the technology segment. Currently, it is ever more relevant to periodically assess the progress of Business Intelligence (BI) initiatives in terms of delivering the expected value to business users. Although Higher Education Institutions (HEI) are seldom directly business driven, such assessment is equally relevant. There is a growing global competition for both qualified students, and the best faculty that excel both in the teaching programs and research projects. In this setting, the alignment of information systems and the business needs is key to standing out from competing HEI. Business intelligence and analytics have been instrumental for many years in delivering this alignment. However, the development of such initiatives is seldom a straightforward path. Many initiatives stall or fail for a number of reasons. It is well acknowledged that organizations that successfully deploy BI systems follow an iterative path, starting with a basic usage of data and analytical tools, and progressing to a growing sophistication of their BI applications, until the BI data-driven culture becomes embedded in the organization’s activities and decision making. The design of maturity models tries to map this progressive path, in which an organization starts with a basic or initial stage of maturity and progresses towards a more mature state. Maturity is therefore related to this notion of evolution or progression.
Maturity models (MM) play an important role by reducing the uncertainty of how BI managers perceive the maturity of the BI systems in their organizations. Some existing MM enable the possibility to benchmark the performance of one’s BI systems against the average performance of other organizations in the same industry. Furthermore, a MM establishes an evolution path that, with a set of recommendations, helps organizations to know what to do next if they want to achieve a higher level of maturity.
Several BI maturity models have already been created. Many of them focus on a specific set of processes, such as project management or learning management, and often they are not directed toward any particular application or business domain. This approach allows the same maturity model to be used across many different industries. However, such approach tends to be complex, with large amount of assessment questions and a terminology set that is not particularly overlapping with the vocabulary and definitions in a particular domain. Past survey experience shows that such complexity and discrepancy resulted in difficulties in assessing correctly, either due to lack of understanding of key concepts or due to complications in locating the expert(s) who has the capabilities of in-depth understanding of many diverse questions (Cardoso et al. 2013). In addition, some of the unique or highly important information needs of HEI as a specific domain cannot be addressed in detail. Finally, the field of BI and Analytics (BI&A) is going through rapid changes, with a shift from heavy Data Warehouse focus to increasing emphasis on issues such as IoT and Natural Language Processing. Such changes are not sufficiently captured and reflected in many of the current BI MM.
In this paper, we will study the relevant BI MM in literature. Based on the literature study, we will develop a lean assessment model to be used by HEI in Europe to evaluate the maturity of their BI and analytics initiatives. This model could then be used to benchmark European universities in the context of future activities of the EUNIS BI Special Interest Group (SIG-BI).
ER  -