Big data maturity model for Malaysian zakat institutions to embark on big data initiatives

Sulaiman, Hidayah; Cob, Zaihisma Che; Ali, Nor’ashikin

doi:10.1109/icsecs.2015.7333084

Cited by 17 publications

(15 citation statements)

References 14 publications

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“…Many parties agree that the application of Big Data technology brings benefits to the organization's goals and performance [22] [23]. However, its implementation is not without obstacles.…”

Section: Discussionmentioning

confidence: 99%

The Maturity Measurement of Big Data Adoption in Manufacturing Companies Using the TDWI Maturity Model

Retrialisca

Chotijah

2020

JISEBI

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Background: Big data technology has been used in several sectors in Indonesia. Adoption of big technology provides great potential for research, especially achievement in the implementation of big data in manufacturing companies. The Data Warehousing Institute (TDWI) Maturity Model is a tool that can be used to measure the state of "As-is" implementation of big data using 5 main dimensions. Maturity level shows the level of organizational ability to adjust big data technology currently.Objective: This study aims to measure the level of maturity in the implementation of big data technology in manufacturing companies PT. XYZ. This measurement is considered very important because it can know the process of managing data that is structured and has a high volume of data and provides more transparent reporting. This can help the company in making decisions that provide good information, so the company can increase the trust of stakeholders.Methods: This study uses qualitative methods to analyze research data using TWDI Maturity Model tools. Interview technique is used to retrieve respondent data where interview preparation guidelines are made by paying attention to 5 dimensions and 50 indicators in TDWI.Results: The research showed that the implementation of big data technology in the company as a whole has reached the level of corporate adoption. Infrastructure, data management, and analytics dimensions have reached the corporate adoption level while the organizational and governance dimensions are still at an early adoption level.Conclusion: To measure the maturity level of adoption of big data technology in manufacturing companies can use qualitative methods with TDWI Maturity model tools, interview guides for data retrieval by considering the 5 dimensions and 50 indicators that exist in TDWI.

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“…Many parties agree that the application of Big Data technology brings benefits to the organization's goals and performance [22] [23]. However, its implementation is not without obstacles.…”

Section: Discussionmentioning

confidence: 99%

The Maturity Measurement of Big Data Adoption in Manufacturing Companies Using the TDWI Maturity Model

Retrialisca

Chotijah

2020

JISEBI

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“…The backward snowballing method was referenced by a list of related works that were first identified using the database search method. Both search methods were used during the search phase to avoid bias [42][43][44].…”

Section: Stage 2: Conducting the Reviewmentioning

confidence: 99%

Big Data Maturity Assessment Models: A Systematic Literature Review

Al-Sai

Husin

Syed-Mohamad

et al. 2022

BDCC

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Big Data and analytics have become essential factors in managing the COVID-19 pandemic. As no company can escape the effects of the pandemic, mature Big Data and analytics practices are essential for successful decision-making insights and keeping pace with a changing and unpredictable marketplace. The ability to be successful in Big Data projects is related to the organization’s maturity level. The maturity model is a tool that could be applied to assess the maturity level across specific key dimensions, where the maturity levels indicate an organization’s current capabilities and the desirable state. Big Data maturity models (BDMMs) are a new trend with limited publications published as white papers and web materials by practitioners. While most of the related literature might not have covered all of the existing BDMMs, this systematic literature review (SLR) aims to contribute to the body of knowledge and address the limitations in the existing literature about the existing BDMMs, assessment dimensions, and tools. The SLR strategy in this paper was conducted based on guidelines to perform SLR in software engineering by answering three research questions: 1) What are the existing maturity assessment models for Big Data? 2) What are the assessment dimensions for Big Data maturity models? and 3) What are the assessment tools for Big Data maturity models? This SLR covers the available BDMMs written in English and developed by academics and practitioners (2007–2022). By applying a descriptive qualitative content analysis method for the reviewed publications, this SLR identified 15 BDMMs (10 BDMMs by practitioners and 5 BDMMs by academics). Additionally, this paper presents the limitations of existing BDMMs. The findings of this paper could be used as a grounded reference for assessing the maturity of Big Data. Moreover, this paper will provide managers with critical insights to select the BDMM that fits within their organization to support their data-driven decisions. Future work will investigate the Big Data maturity assessment dimensions towards developing a new Big Data maturity model.

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“…However, the proposed MM does not provide a structural method to assess the data science maturity of the businesses. While the study of Sulaiman et al (2015) proposes a big data MM to gauge the readiness of zakat institutions to embark into the big data evolution, the scope of this MM is limited to a specific institution and does not entirely embrace the data science domain. The studies of Lukman et al (2011), Cosic et al (2012) and Raber et al (2012) propose a business intelligence MM.…”

Section: Background and Related Workmentioning

confidence: 99%

The development of the data science capability maturity model: a survey-based research

Gökalp

Kayabay

et al. 2021

OIR

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PurposeThe purpose of this paper is to investigate social and technical drivers of data science practices and develop a standard model for assisting organizations in their digital transformation by providing data science capability/maturity level assessment, deriving a gap analysis, and creating a comprehensive roadmap for improvement in a standardized way.Design/methodology/approachThis paper systematically reviews and synthesizes the existing literature-related to data science and 183 practitioners' considerations by employing a survey-based research method. By blending the findings of this research with a well-established process capability maturity model standard, International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 330xx, and following a methodological maturity development framework, a theoretically grounded model, entitled as the data science capability maturity model (DSCMM) was developed.FindingsIt was found that organizations seek a capability/maturity model standard to evaluate and improve their current data science capabilities. To close this research gap, the DSCMM is developed. It consists of six capability maturity levels and twenty-seven processes categorized under five process areas: organization, strategy management, data analytics, data governance and technology management.Originality/valueThis paper validates the need for a process capability maturity model for the data science domain and develops the DSCMM by integrating literature findings and practitioners' considerations into a well-accepted process capability maturity model standard to continuously assess and improve the maturity of data science capabilities of organizations.

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Big data maturity model for Malaysian zakat institutions to embark on big data initiatives

Cited by 17 publications

References 14 publications

The Maturity Measurement of Big Data Adoption in Manufacturing Companies Using the TDWI Maturity Model

The Maturity Measurement of Big Data Adoption in Manufacturing Companies Using the TDWI Maturity Model

Big Data Maturity Assessment Models: A Systematic Literature Review

The development of the data science capability maturity model: a survey-based research

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