Big data maturity model: Difference between revisions

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{{short description|Aspect of computer science}}

{{cleanup reorganize|date=May 2017}}

'''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'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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The stages or phases in BDMMs depict the various ways in which data can be used in an organization and is one of the key tools to set direction and monitor the health of an organization's big data programs.<ref name=":2">{{Cite journal|last=El-Darwiche |display-authors=etal |date=2014|title=Big Data Maturity: An action plan for policymakers and executives|journal=World Economic Forum}}</ref><ref name=":3">{{Cite web|url=http://www.radcliffeadvisory.com/research/download.php?file=RAS_BD_MatMod.pdf|title=Leverage a Big Data Maturity model to build your big data roadmap|date=2014|access-date=2017-05-21|archive-url=https://web.archive.org/web/20170802005853/http://www.radcliffeadvisory.com/research/download.php?file=RAS_BD_MatMod.pdf|archive-date=2017-08-02|url-status=dead}}</ref>

An underlying assumption is that a high level of big data maturity correlates with an increase in revenue and reduction in operational expense. However, reaching the highest level of maturity involves major investments over many years.<ref name=":0">{{Cite journal|last=Halper|first=Fern|date=2016|title=A Guide to Achieving Big Data Analytics Maturity|journal=TDWI Benchmark Guide}}</ref> Only a few companies are considered to be at a "mature" stage of big data and analytics.<ref>{{Cite web |date=December 15, 2023 |title=How to summit "data maturity mountain" and make data your superpower |url=https://www.fastcompany.com/90998039/how-to-summit-data-maturity-mountain-and-make-data-your-superpower |access-date=October 8, 2024 |website=Fast Company |author1=Altair }}</ref> These include internet-based companies (such as [[LinkedIn]], [[Facebook]], and [[Amazon.com|Amazon]]) and other non-Internet-based companies, including financial institutions (fraud analysis, real-time customer messaging and behavioral modeling) and retail organizations ([[Clickstream|click-stream]] analytics together with self-service analytics for teams).<ref name=":0" />

== Categories ==

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The corporate adoption stage is characterized by the involvement of end-users, an organization gains further insight and the way of conducting business is transformed. During this stage:

* End-users might have started operationalizing big data analytics or changing their decision -making processes

* Most organizations would already have repeatedly addressed certain gaps in their infrastructure, data management, governance and analytics

'''Stage 5: Mature / visionary'''