Big data maturity model: Difference between revisions

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[[Big data|Big Data]] maturity can be defined as “the evolution of an organization to integrate, manage, and leverage all relevant internal and external data sources” and involves building an ecosystem that includes technologies, [[data management]], [[analytics]], [[governance]] and organizational components <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>.

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# Prescriptive models.    

=== Descriptive Models ===

Descriptive models assess the current firm maturity through qualitative positioning of the firm in various stages or phases. The model does not provide any recommendations as to how a firm would improve their big data maturity.

==== Big Data & Analytics Maturity Model (IBM model)<ref>{{Cite news|url=http://www.ibmbigdatahub.com/blog/big-data-analytics-maturity-model|title=Big Data &amp; Analytics Maturity Model|work=IBM Big Data & Analytics Hub|access-date=2017-05-21|language=en}}</ref> ====

This descriptive model aims to assess the value generated from big data investments towards supporting strategic business initiatives.

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Maturity levels also cover areas in matrix format focusing on: Business Strategy, Information, Analytics, Culture and Execution, Architecture and Governance.

==== Knowledgent Big Data Maturity Assessment<ref>{{Cite web|url=https://bigdatamaturity.knowledgent.com|title=Home {{!}} Big Data Maturity Assessment|website=bigdatamaturity.knowledgent.com|access-date=2017-05-21}}</ref> ====

Consisting of an assessment survey, this big data maturity model assesses an organization’s readiness to execute big data initiatives.  Furthermore, the model aims to identify the steps and appropriate technologies that will lead an organization towards big data maturity.

=== Comparative Models ===

Comparative big data maturity models aim to benchmark an organization in relation to its industry peers and normally consist of a survey containing quantitative and qualitative information.

==== CSC Big Data Maturity Tool<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> ====

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.

==== TDWI Big Data Maturity Model <ref name=":0" /> ====

The TDWI Big Data Maturity Model is a salient model in the current big data maturity area and therefore consists of a significant body of knowledge. TDWI describes big data maturity as the evolution of an organization to integrate, manage, and leverage all relevant internal and external data sources. The model involves building an ecosystem that includes technologies, data management, analytics, governance, and organizational components. This BDMM provides a methodology to measure and monitor the state of the big data program, identifies the current stage of maturity of an organization, speaks to the effort needed to complete their current stage, as well as the steps required to move to the next stage of maturity. It serves as a kind of odometer to measure and manage the speed of progress and adoption within a company for a big data program.

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TDWI<ref name=":0" /> did an assessment on 600 organizations and found that the majority of organizations are either in the Pre-Adoption (50%) or Early Adoption (36%) stages. Additionally, only 8% of the sample have managed to move past the chasm towards corporate adoption or being mature/visionary.

=== Prescriptive Models ===

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 as follows:

==== 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 4, Structure a Big Data Proof-of-Concept Project.

==== 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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* 5 – Optimize & Extend

==== 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 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

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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. Furthermore, Braun (2015) found that 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’s practical use, and they completely lack in documentation, ultimately resulting in poor quality of development and evaluation<ref name=":1" />.

== Also See ==

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* [[Maturity model|Maturity Model]]

* [[Data management|Data Management]]

* [[Capability Maturity Model]]

== References ==