A Review on Big Data Maturity Models

Al-Sai, Zaher Ali; Abdullah, Rosni; Husin, Mohd Heikal

doi:10.1109/jeeit.2019.8717398

Cited by 14 publications

(15 citation statements)

References 13 publications

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“…We based our review on the two recent systematic literature reviews. The first one provides a comprehensive overview of the models specifically for big data and ADA [15]. The other study reviewed and comparatively analyzed the maturity models on 'analytics' [3].…”

Section: Data Analytics Maturity Modelsmentioning

confidence: 99%

“…There are a number of existing maturity models that propose a set of ADA capabilities that organizations need to acquire and use. However, these models are typically domain-specific, do not prescribe the necessary capabilities, nor have been empirically and rigorously validated [15], [16]. To address this critical gap, we have developed a maturity model for advanced data analytics capabilities; entitled ADA-CMM.…”

Section: Introductionmentioning

confidence: 99%

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ADA-CMM: A Capability Maturity Model for Advanced Data Analytics

Korsten¹,

Aysolmaz²,

Türetken³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Despite the rising importance of advanced data analytics, there is limited guidance on how organizations should leverage it. The benefits that an organization can gain through advanced data analytics depends on the organization's ability to gain and use relevant capabilities. This study introduces a capability maturity model (ADA-CMM) for advanced data analytics to help organizations assess their current state of capabilities for managing advanced data analytics. We used the Delphi method to develop the maturity model and performed a survey to evaluate its validity. The results confirm that the maturity level of the advanced data analytics capabilities of organizations is positively related to the business value that they can capture from their use, which in turn found positively related to organizational performance. ADA-CMM can be used by organizations as a self-assessment tool and to create a roadmap for improving their relevant capabilities.

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Section: Data Analytics Maturity Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ADA-CMM: A Capability Maturity Model for Advanced Data Analytics

Korsten¹,

Aysolmaz²,

Türetken³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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show abstract

“…Many researchers and business consulting firm have introduced the BDDM to help organization to improve a capability in big data technology through assessment tools. Some researchers have conducted systematic reviews of existing BDMMs [35][36][37]. Braun [35] compared multiple maturity models (MMs) and provided the evaluation score for the top two highest performing MMs, i.e., TDWI [38] and International Data Corporation (IDC 2013).…”

Section: Review Of Existing Bdmmsmentioning

confidence: 99%

“…The comparison of BDMMs with major characteristics are discussed and we extracted as shown in Table 1.Braun [35] also provided a negative report on lack of theoretical soundness, rigorous testing, and wide acceptance. Al-Sai and Abdullah [37] reported the limitation that most of models have been developed in industry, by either technology vendors or consulting partners that might expose them to biases Most of the currently used BDMMs are old, and a few have been developed recently. This article introduces the novel online mobile adaptive BDMM for SMEs.…”

Section: Review Of Existing Bdmmsmentioning

confidence: 99%

Innovative Mobile Application for Measuring Big Data Maturity: Case of SMEs in Thailand

Limpeeticharoenchot

Cooharojananone

Chanvanakul

et al. 2020

Int. J. Interact. Mob. Technol.

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A Big Data maturity model (BDMM) is one of the key tools for Big Data assessment and monitoring, and a guideline for maximizing the usage and opportunity of Big Data in organizations. The development of a BDMM for SMEs is a new concept and is challenging in terms of development, application, and adoption. This article aims to create the novel online adaptive BDMM via responsive web application for SMEs. We develop the BDMM API and a responsive web application for easy access via mobile phone. We developed a model by analyzing the factors impacting the success of implementing Big Data Analytics (BDA) in SMEs based on literature reviews. The model was verified by conducting a survey of 180 SMEs in Thailand, interviewed against four extracted domains. Then, the scoring and classified levels for the model was developed through Latent Class Analysis (LCA) to depict four levels of each domain and four final maturity levels to create an adaptive model. As the experimental results with 33 users including executive officers, managers, IT and data analytic officers .The user acceptance for our mobile application using TAM indicates that executive officers group and non-executive group satisfied perceived usefulness, perceived ease of use, and intention to use factor. Use cases of the application include SMEs monitoring for their Big Data Analytics capability for improvement, and the Government Agency providing proper support on SMEs’ level of competency.

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“…For instance, the primary stages of development usually include categories such as "aware, exploring, optimizing, and transforming" (Dhanuka, 2016). Similarly, the major dimensions of the big data maturity models tend to be about various organizational factors related to these maturity levels such as business strategy, vision of big data analytics, value and metrics, culture and execution, data governance, trust and privacy, organizational structure, employee skills, data sources and data management (Al-Sai and Abdullah (2019)). An early form of an integrated big data maturity framework that aims to combine these common points is "Transforming Data with Intelligence (TDWI) Big Data Maturity Tool", as summarized in Table 1.…”

Section: Big Data Maturity Modelsmentioning

confidence: 99%

Big data maturity models for the public sector: a review of state and organizational level models

Okuyucu

Yavuz

2020

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Purpose Despite several big data maturity models developed for businesses, assessment of big data maturity in the public sector is an under-explored yet important area. Accordingly, the purpose of this study is to identify the big data maturity models developed specifically for the public sector and evaluate two major big data maturity models in that respect: one at the state level and the other at the organizational level. Design/methodology/approach A literature search is conducted using Web of Science and Google Scholar to determine big data maturity models explicitly addressing big data adoption by governments, and then two major models are identified and compared: Klievink et al.’s Big Data maturity model and Kuraeva’s Big Data maturity model. Findings While Klievink et al.’s model is designed to evaluate Big Data maturity at the organizational level, Kuraeva’s model is appropriate for assessments at the state level. The first model sheds light on the micro-level factors considering the specific data collection routines and requirements of the public organizations, whereas the second one provides a general framework in terms of the conditions necessary for government’s big data maturity such as legislative framework and national policy dimensions (strategic plans and actions). Originality/value This study contributes to the literature by identifying and evaluating the models specifically designed to assess big data maturity in the public sector. Based on the review, it provides insights about the development of integrated models to evaluate big data maturity in the public sector.

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A Review on Big Data Maturity Models

Cited by 14 publications

References 13 publications

ADA-CMM: A Capability Maturity Model for Advanced Data Analytics

ADA-CMM: A Capability Maturity Model for Advanced Data Analytics

Innovative Mobile Application for Measuring Big Data Maturity: Case of SMEs in Thailand

Big data maturity models for the public sector: a review of state and organizational level models

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