Big data maturity model: Difference between revisions

Revision as of 02:35, 19 June 2017

Revision as of 08:12, 20 June 2017

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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>

== Overview ==

'''Big Data Maturity Models''' (BDMM) are the artefacts 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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# To help guide development milestones

# To avoid pitfalls in establishing and building a big data capabilities

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|first=|date=2014|website=|archive-url=|archive-date=|dead-url=|access-date=}}</ref> alignment, architecture, data, [[data governance]], delivery, development, measurement, program governance, scope, skills, sponsorship, [[Statisticalstatistical model|statistical modelling]]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 et. al.|first=|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|last=|first=|date=2014|website=|archive-url=|archive-date=|dead-url=|access-date=}}</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" />. 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 ==

Big data maturity models can be broken down into three broad categories namely:<ref name=":1" />:

# Descriptive

# Comparative

# 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.

'''Maturity Levels'''

The model consists of the following maturity levels:  

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=== 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" /> ===

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* Phase 2, Assess Big Data Readiness

* Phase 3, Pinpoint a Killer Big Data Use Case

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

=== Radcliffe Big Data Maturity Model<ref name=":3" /> ===

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* 3 - Tactical Value

* 4 – Strategic Leverage

* 5 – Optimize & Extend

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

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* Effectiveness

* New Solutions

* Transformation.

== Critical Evaluation ==

Current BDMMs have been evaluated under the following criteria :<ref name=":1" />:

* Completeness of the model structure (completeness, consistency)

* The quality of model development and evaluation (trustworthiness, stability)

* 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’s practical use, and they completely lack in documentation, ultimately resulting in poor quality of development and evaluation.<ref name=":1" />.

== Also See also ==

* [[Big data|Big Data]]

* [[Analytics]]

* [[Digital Mastery]]

* [[Maturity model|Maturity Model]]

* [[Data management|Data Management]]

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== References ==

{{Reflist}}

[[Category:Big data]]