Big data maturity model: Difference between revisions

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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|url=|journal=Masters Thesis – Tampere University of Technology|volume=|pages=|via=}}</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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Key organizational areas refer to “People, Process and Technology” and the subcomponents include<ref>{{Cite web|url=http://ibmdatamag.com/2014/09/measuring-maturity-of-big-data-initiatives/|title=Measuring maturity of big data initiatives|last=Krishnan|date=2014|access-date=2017-05-21|archive-url=https://web.archive.org/web/20150316120424/http://ibmdatamag.com/2014/09/measuring-maturity-of-big-data-initiatives/|archive-date=2015-03-16|url-status=dead}}</ref> alignment, architecture, data, [[data governance]], delivery, development, measurement, program governance, scope, skills, sponsorship, [[statistical model]]ling, technology, value and visualization.

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 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|url=|journal=World Economic Forum|volume=|pages=|via=}}</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|url=|journal=TDWI Benchmark Guide|volume=|pages=|via=}}</ref> Only a few companies are considered to be at a “Mature” stage of big data and analytics. 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 of Big Data Maturity Models ==

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* Stage 4: Business model transformation

=== Van Veenstra's Model <ref>{{Cite journal|last=van Veenstra|first=Anne Fleur|date=|title=Big Data in Small Steps: Assessing the value of data|url=http://www.idnext.eu/files/TNO-whitepaper--Big-data-in-small-steps.pdf|journal=White Paper|volume=|pages=|via=}}</ref> ===

The prescriptive model proposed by Van Veenstra aims to firstly explore the existing big data environment of the organization followed by exploitation opportunities and a growth path towards big data maturity. The model makes use of four phases namely:

* Efficiency