Big data maturity model: Difference between revisions

Revision as of 23:32, 10 March 2021

Revision as of 21:36, 6 May 2021

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'''Big data maturity models''' (BDMM) are the artifacts used to measure big data maturity.<ref name=":1">{{Cite journal|last=Braun|first=Henrik|date=2015|title=Evaluation of Big Data Maturity Models: A benchmarking study to support big data assessment in organizations|journal=Masters Thesis – Tampere University of Technology}}</ref> These models help organizations to create structure around their big data capabilities and to identify where to start.<ref>Halper, F., & Krishnan, K. (2014). ''TDWI Big Data Maturity Model Guide.'' TDWI Research.</ref> They provide tools that assist organizations to define goals around their big data program and to communicate their big data vision to the entire organization. BDMMs also provide a methodology to measure and monitor the state of a company’scompany's big data capability, the effort required to complete their current stage or phase of maturity and to progress to the next stage. Additionally, BDMMs measure and manage the speed of both the progress and adoption of big data programs in the organization.<ref name=":1" />

The goals of BDMMs are:

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* Descriptive

* Comparative

* Prescriptive

== Descriptive ==

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=== TDWI big data maturity model ===

The TDWI big data maturity model is a model in the current big data maturity area and therefore consists of a significant body of knowledge.<ref name=":0" />

'''Maturity stages'''

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* There is little to no executive support for the effort and only some people in the organization are interested in potential value of big data

* The organization understand the benefits of analytics and may have a data warehouse

* An organization’sorganization's governance strategy is typically more IT-centric rather than being integrative business-and-IT centric

'''Stage 2: Pre-adoption'''

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* Ease of application (ease of use, comprehensibility)

* Big data value creation (actuality, relevancy, performance)

The TDWI and CSC have the strongest overall performance with steady scores in each of the criteria groups. The overall results communicate that the top performer models are extensive, balanced, well-documented, easy to use, and they address a good number of big data capabilities that are utilized in business value creation. The models of Booz & Company and Knowledgent are close seconds and these mid-performers address big data value creation in a commendable manner, but fall short when examining the completeness of the models and the ease of application. Knowledgent suffers from poor quality of development, having barely documented any of its development processes. The rest of the models, i.e. Infotech, Radcliffe, van Veenstra and IBM, have been categorized as low performers. Whilst their content is well aligned with business value creation through big data capabilities, they all lack quality of development, ease of application and extensiveness. Lowest scores were awarded to IBM and Van Veenstra, since both are providing low level guidance for the respective maturity model’smodel's practical use, and they completely lack in documentation, ultimately resulting in poor quality of development and evaluation.<ref name=":1" />

== See also ==