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=== Descriptive Models ===
===== '''Big Data & Analytics Maturity Model (IBM model)'''<ref>{{Cite news|url=http://www.ibmbigdatahub.com/blog/big-data-analytics-maturity-model|title=Big Data & 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. The model consists of the following maturity levels: Ad-hoc, Foundational, Competitive Differentiating and Break Away.
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.
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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" />▼
▲===== '''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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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; Phase 2, Assess Big Data Readiness; Phase 3, Pinpoint a Killer Big Data Use Case; and 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, 1 – Catching up, 2 – First Pilot, 3 - Tactical Value, 4 – Strategic Leverage and 5 – Optimize & Extend.
===== '''Booz & Company'''<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, Stage 2: Functional Area Excellence, Stage 3: Value Proposition enhancement and finally Stage 4: Business model transformation.
===== '''Van Veenstra''' <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, Effectiveness, New Solutions, and Transformation.
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