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’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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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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* Descriptive

* Comparative

* Prescriptive

== Descriptive ==

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The CSC big data maturity tool acts as a comparative tool to benchmark an organization's big data maturity. A survey is undertaken and the results are then compared to other organizations within a specific industry and within the wider market.<ref>{{Cite web|url=http://csc.bigdatamaturity.com/|title=CSC Big Data Maturity Tool: Business Value, Drivers, and Challenges|last=Inc.|first=Creative services by Cyclone Interactive Multimedia Group, Inc. (www.cycloneinteractive.com) Site designed and hosted by Cyclone Interactive Multimedia Group|website=csc.bigdatamaturity.com|language=en|access-date=2017-05-21}}</ref>

=== TDWI big data maturity model <ref name=":0" /> ===

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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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'''

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The majority of prescriptive BDMMs follow a similar modus operandi in that the current situation is first assessed followed by phases plotting the path towards increased big data maturity. Examples are:

=== Info-tech big data maturity assessment tool ===

=== Info-tech big data maturity assessment tool <ref>{{Cite web|url=https://www.infotech.com/research/ss/leverage-big-data-by-starting-small/it-big-data-maturity-assessment-tool|title=Big Data Maturity Assessment Tool|website=www.infotech.com|language=en|access-date=2017-05-21}}</ref> ===

This maturity model is prescriptive in the sense that the model consists of four distinct phases that each plot a path towards big data maturity. Phases are:

* Phase 1, undergo big data education

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* Phase 3, pinpoint a killer big data use case

* Phase 4, structure a big data proof-of-concept project

=== Info-tech big data maturity assessment tool <ref>{{Cite web|url=https://www.infotech.com/research/ss/leverage-big-data-by-starting-small/it-big-data-maturity-assessment-tool|title=Big Data Maturity Assessment Tool|website=www.infotech.com|language=en|access-date=2017-05-21}}</ref> ===

=== Radcliffe big data maturity model<ref name=":3" /> ===

The Radcliffe big data maturity model, as other models, also consists of distinct maturity levels ranging from:

* 0 – "In the dark"

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* 4 – "Strategic leverage"

* 5 – "Optimize and extend"

<ref name=":3" />

=== Booz & Company's model<ref name=":2" /> ===

This BDMM provides a framework that not only enables organizations to view the extent of their current maturity, but also to identify goals and opportunities for growth in big data maturity. The model consists of four stages namely,

* Stage 1: Performance management

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* Stage 3: Value proposition enhancement

* Stage 4: Business model transformation

<ref name=":2" />

=== Van Veenstra's model ===

=== Van Veenstra's model <ref>{{Cite journal|last=van Veenstra|first=Anne Fleur|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}}</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

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* New solutions

* Transformation

=== Van Veenstra's model <ref>{{Cite journal|last=van Veenstra|first=Anne Fleur|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}}</ref> ===

== Critical evaluation ==

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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 ==