Big data maturity model: Difference between revisions

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{{short description|Aspect of computer science}}

{{cleanup- reorganize|date=May 2017}}

[[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>

'''Big Datadata Maturitymaturity Models'''models (BDMM) ''' are the artefactsartifacts used to measure Bigbig Datadata 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 Bigbig Datadata 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" />.

== 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:

#* To provide a capability assessment tool that generates specific focus on big data in key organizational areas

#* To help guide development milestones

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

Key organizational areas refer to “People"people, Processprocess and Technology”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|websiteaccess-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=|dead2015-url=03-16|accessurl-datestatus=dead}}</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’san organization's big data programs.<ref name=":2">{{Cite journal|last=El-Darwiche et. al.|firstdisplay-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|last=|first=|date=2014|websiteaccess-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=|dead2017-url=08-02|accessurl-datestatus=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”"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-internetInternet-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 and analytics maturity model (IBM model) ===

=== 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 Levelslevels'''

The model consists of the following maturity levels:  

* Ad-hoc

* Foundational

* Competitive Differentiating differentiating

* Break Away. away

'''Assessment Areasareas'''

Maturity levels also cover areas in matrix format focusing on: Businessbusiness Strategystrategy, Informationinformation, Analyticsanalytics, Cultureculture and Executionexecution, Architecturearchitecture and Governancegovernance.

=== Knowledgent Big Data Maturity Assessment<ref>{{Cite webnews|url=httpshttp://bigdatamaturitywww.knowledgentibmbigdatahub.com/blog/big-data-analytics-maturity-model|title=Home {{!}} Big Data & Analytics Maturity AssessmentModel|websitework=bigdatamaturity.knowledgent.comIBM Big Data & Analytics Hub|access-date=2017-05-21|language=en}}</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.

=== Knowledgent big data maturity assessment ===

== Comparative Models ==

Consisting of an assessment survey, this big data maturity model assesses an organization’sorganization'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.<ref>{{Cite web|url=https://bigdatamaturity.knowledgent.com|title=Home {{!}} Big Data Maturity Assessment|website=bigdatamaturity.knowledgent.com|access-date=2017-05-21|archive-url=https://web.archive.org/web/20150214173709/https://bigdatamaturity.knowledgent.com/|archive-date=2015-02-14|url-status=dead}}</ref>

== 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 ===

=== 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 Bigbig Datadata maturity tool acts as a comparative tool to benchmark an organization’sorganization'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 Bigbig Datadata Maturitymaturity Model <ref name=":0" />model ===

The TDWI Bigbig Datadata Maturitymaturity Modelmodel is a model in the current big data maturity area and therefore consists of a significant body of knowledge.<ref name=":0" />

'''Maturity Stagesstages'''

The different stages of maturity in the TDWI BDMM can be summarized as follows:

'''''Stage 1: Nascent'''''

The nascent stage as a pre–big data environment. During this stage:

* The organization has a low awareness of big data or its value;

* 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 Organizationorganization 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''adoption'''

During the pre-adoption stage:

* The organization start to investigate big data analytics.

'''''Stage 3: Early Adoption''adoption'''

'''''The Chasm''"chasm"'''

There is then generally a series of hurdles it needs to overcome. These hurdles include:

* Obtaining the right skill set to support the capability, including Hadoop and advanced analytical skills;

* Political issues, i.e. big data projects are conducted in areas within the organization and trying to expand the effort or enforcing more stringent standards and governance lead to issues regarding ownership and control.

'''''Stage 4: Corporate Adoption''adoption'''

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.''visionary'''

Only  a  few  organizations  can  be  considered  as  visionary  in  terms  of  big  data and big data analytics. During this stage an organization:

* Isis able to execute big data programs as a well-oiled machine with highly mature infrastructure

* Hashas a well-established  big data program  and  big data  governance  strategies.

* Executesexecutes its big data program as a budgeted  and  planned  initiative  from an organization-wide perspective.

* Employees whose employees share a level of excitement  and  energy  around  big  data  and  big  data  analytics.

'''Research Findingsfindings'''

TDWI<ref name=":0" /> did an assessment on 600 organizations and found that the majority of organizations are either in the Prepre-Adoptionadoption (50%) or Earlyearly Adoptionadoption (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 ===

=== 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 Bigbig Datadata Maturitymaturity. Phases are:

* Phase 1, Undergoundergo Bigbig Data Educationdata education

* Phase 2, Assessassess Bigbig Data Readinessdata readiness

* Phase 3, Pinpointpinpoint a Killerkiller Bigbig Datadata Use Caseuse case

* Phase 4, Structurestructure a Bigbig Datadata Proofproof-of-Concept Project.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 Bigbig Datadata Maturitymaturity Model<ref name=":3" />model ===

The Radcliffe Bigbig Datadata Maturitymaturity Modelmodel, as other models, also consists of distinct maturity levels ranging from:

* 0 – "In the Dark dark"

* 1 – "Catching up "

* 2 – "First Pilot pilot"

* 3 -– "Tactical Value value"

* 4 – "Strategic Leverage leverage"

* 5 – "Optimize & Extendand extend"

<ref name=":3" />

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

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 Managementmanagement

* Stage 2: Functional Areaarea Excellenceexcellence

* Stage 3: Value Propositionproposition 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|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 solutions

* 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=Paper}}</ref> ===

== Critical Evaluationevaluation ==

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 Datadata 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" />.

== Also See ==

* [[Big data|Big Data]]

* [[Analytics]]

* [[Digital Mastery]]

== OverviewSee also ==

* [[Maturity model|Maturity Model]]

* [[Data management|Data Management]]

* [[Capability Maturity Model]]

== References ==

{{Reflist}}

[[Category:Big data]]