Analyzing business intelligence maturity

Abstract-Business intelligence (BI) has become a well-known umbrella term both amongst academics and practitioners. Researchers have studied how companies can take advantage of BI and what challenges companies are facing when working with BI. However, research is mostly focused on large companies, despite the importance of small-and medium sized companies (SMEs) in both society and economically. This paper presents results of an in-depth qualitative case study on challenges faced by SMEs when adopting BI. The challenges are categorized according to a BI maturity model adopted as unit of assessment. The contribution of the results presented is two-folded; 1) It increases current literature regarding challenges when adopting BI in SMEs, and 2) It serves as guidance for SMEs on common pitfalls that ought to be avoided.

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“…Some research has been published (e.g. [13,14,15]. In addition, [16] researched determinants and challenges to BI adoption in 20 polish SMEs.…”

Section: Introductionmentioning

confidence: 99%

Challenges with BI adoption in SMEs

Gudfinnsson

Strand

2017

2017 8th International Conference on Information, Intelligence, Systems &Amp; Applications (IISA)

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“…The business model to be implemented should describe how the company creates, delivers, and captures the expected value. Business models within the scope of industry 4.0 include a large number of subcomponents as described by [23][24][25]. The proposed taxonomy includes the following elements:…”

Section: • Businessmentioning

confidence: 99%

A Taxonomy of Industry 4.0 and Related Technologies

Öztemel¹,

Gürsev²

2020

Industry 4.0 - Current Status and Future Trends

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Industry 4.0 and related technologies will remain at the top agenda of manufacturing systems until respective digital transformation is completed. In order to increase the speed of the transformation and the respective performance, a taxonomy of industry 4.0 is proposed in this chapter. The taxonomy is defined through four aspects including strategic understanding, managerial practices, technological infrastructure and developments, as well as human intervention with respective skills and competencies. Each aspect of these is defined and further subcategorized in order to reveal the real dynamics of industry 4.0 and respective implementations. Generating the taxonomy would also easy the categorization of the respective efforts and make the assessment processes to be carried out more effectively. It is also believed that the proposed taxonomy will be the source of generating a maturity model of industry 4.0. Note that the proposed taxonomy and respective components are defined by reviewing 620 papers, maturity models, and industry 4.0 projects.

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“…These measurements on the analytics results can be used as an indicator to measure reliability and accuracy of data quality [39]. The indicators are used to analyze both strengths and weaknesses of Public Enterprise as a whole or within specific service functions [36]. Business intelligence maturity model which has technical stack that should be considered because Big Data Analytics involves large volume, variety and velocity of data, and analyzing theses data requires excellent technology aspect with good implementation of big data warehouse, data quality and understanding of performance management [6].…”

Section: Financial Economic and Statisticsmentioning

confidence: 99%

Business intelligence addressing service quality for big data analytics in public sector

Manikam

Sahibudin

Kasinathan

2019

IJEECS

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<span>With the inauguration of Big Data Analytics initiative nationally, many nations have participated and paved way for BDA ecosystem. The initiative is a catalyst to further encourage economic growth in Public Sectors. Some of the common key deliverables identified are increasing productivity involving information communications technology, cost savings, shared benefits, and encourage innovation. The objectives can be further elaborated by driving big data analytics demands in various public sectors agency, adopting big data analytics framework supporting the building of big data industry. This has encouraged talents and startup companies inspiring their capabilities by developing various technology platform, collaborate and innovate amongst public and private sectors, and further strengthen data governance by creating policy and procedures. With the establishment of big data analytics framework, performance measurement can be enforced effortlessly using the principles of business intelligence maturity model and the technological stack comes with it. Various data sources can be used to benchmark service quality using advanced analytics and data science techniques.</span>

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Analyzing business intelligence maturity

Cited by 40 publications

References 22 publications

Challenges with BI adoption in SMEs

Challenges with BI adoption in SMEs

A Taxonomy of Industry 4.0 and Related Technologies

Business intelligence addressing service quality for big data analytics in public sector

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