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Evaluation of Big Data Maturity Models – A Benchmarking Study to Support Big Data Maturity Assessment in Organizations

机译:大数据成熟度模型评估 - 支持组织大数据成熟度评估的基准研究

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摘要

Big Data is defined as high volume, high velocity and high variety information assets, a result of the explosive growth of data facilitated by the digitization of our society. Data has always had strategic value, but with Big Data and the new data handling solutions even more value creation opportunities have emerged. Studies have shown that adopting Big Data initiatives in organizations enhance data management and analytical capabilities that ultimately improve competitiveness, productivity as well as financial and operational results. There are differences between organizations in terms of Big Data capabilities, performance and to what effect Big Data can be utilized. To create value from Big Data, organizations must first assess their current situation and find solutions to advance to a higher Big Data capability level, also known as Big Data maturity. Conceptual artefacts called Big Data maturity models have been developed to help in this endeavor. They allow organizations to have their Big Data methods and processes assessed according to best practices. However, it is a tough job for an organization to select the most useful and appropriate model, as there are many available and each one differ in terms of extensiveness, quality, ease of use, and content.The objective of this research was to evaluate and compare available Big Data maturity models in terms of good practices of maturity modeling and Big Data value creation, ultimately supporting the organizational maturity assessment process. This was done by conducting a benchmarking study that quantitatively evaluated maturity model attributes against specific evaluation criteria. As a result, eight Big Data maturity models were chosen, evaluated and analyzed. The theoretical foundations and concepts of the research were identified through systematical literature reviews. The benchmarking scores suggest that there is great variance between models when examining the good practices of maturity modeling. The degree of addressing Big Data value creation opportunities is more balanced. However, total scores clearly lean towards a specific group of models, identified as top-performers. These top-performers score relatively high in all examined criteria groups and represent currently the most useful Big Data maturity models for organizational Big Data maturity assessment. They demonstrate high quality of model structure, extensiveness and detail level. Authors of these models use a consistent methodology and good practices for design and development activities, and engage in high quality documentation practices. The Big Data maturity models are easy to use, and provide an intuitive tool for assessment as well as sufficient supporting materials to the end user. Lastly, they address all important Big Data capabilities that contribute to the creation of business value.
机译:大数据被定义为高容量,高速度和高多样性的信息资产,这是由于我们的社会数字化促进了数据的爆炸性增长。数据一直具有战略价值,但是借助大数据和新的数据处理解决方案,出现了更多的价值创造机会。研究表明,在组织中采用大数据计划可以增强数据管理和分析能力,从而最终提高竞争力,生产力以及财务和运营成果。在大数据功能,性能以及可以利用大数据的影响方面,组织之间存在差异。为了从大数据中创造价值,组织必须首先评估其当前状况并找到解决方案以提升到更高的大数据能力水平,也称为大数据成熟度。已经开发了称为“大数据成熟度模型”的概念人工制品,以帮助实现这一目标。它们使组织能够根据最佳实践对大数据方法和流程进行评估。但是,对于组织来说,选择最有用和最合适的模型是一项艰巨的工作,因为有很多可用的模型,并且每个模型在广泛性,质量,易用性和内容方面都不同。本研究的目的是评估并根据成熟度建模和大数据价值创造的良好实践比较可用的大数据成熟度模型,最终为组织成熟度评估流程提供支持。这是通过进行基准测试来完成的,该基准测试根据特定的评估标准定量评估了成熟度模型的属性。结果,选择,评估和分析了八个大数据成熟度模型。通过系统的文献综述,确定了研究的理论基础和概念。基准分数表明,在检查成熟度建模的良好实践时,模型之间存在很大差异。解决大数据价值创造机会的程度更加平衡。但是,总分显然倾向于特定的一组模型,这些模型被认为是性能最高的模型。这些表现最佳的人在所有检查的标准组中得分都相对较高,代表了当前最有用的组织机构大数据成熟度评估的大数据成熟度模型。他们展示了高质量的模型结构,广泛性和详细程度。这些模型的作者在设计和开发活动中使用一致的方法和良好实践,并从事高质量的文档实践。大数据成熟度模型易于使用,并为最终用户提供了评估的直观工具以及足够的支持材料。最后,它们解决了所有有助于创造业务价值的重要大数据功能。

著录项

  • 作者

    Braun Henrik Tobias;

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  • 年度 2015
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  • 原文格式 PDF
  • 正文语种 en
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