Data Management Maturity Model V1.0.pdf

The program centers around the Data Management Maturity (DMM) model, a comp=
rehensive framework of data management practices in six key categories that=
 helps organizations benchmark their capabilities, identify strengths and g=
aps, and leverage their data assets to improve business performance.

Data Management Maturity Model V1.0.pdf
Download File https://eraboflorbo.blogspot.com/?file=3D2wHCvI



Delivering effective institutional support for research data management (RD=
M) is a challenge for any HE institution, regardless of size or research in=
tensity. Typically, support should include both technical and human infrast=
ructure, with ownership of individual elements distributed across the insti=
tution. Ensuring that RDM service development takes as comprehensive a view=
 as possible and engages effectively with relevant stakeholders is key to s=
uccessful research data support.

RISE was created primarily for higher education institutions to help them t=
o take stock of their current RDM support provision and identify areas of f=
ocus for future development. This process is typically administered by some=
one from within the institution with significant experience of the local re=
search support infrastructure, and a good understanding of the wider issues=
 associated with supporting data management. Regardless of who manages the =
service review, input is likely to come from representatives of the Library=
, Research Office and IT, and may include input from other areas. One of th=
e advantages of using RISE is that it provides a means of engaging these st=
akeholders in productive discussion about service development and allows th=
em to reach a shared vision of where the RDM service aims to be.

Probably the most widely established model for service development is the C=
apability Maturity Model (CMM) developed at the Software Engineering Instit=
ute (SEI) at Carnegie Mellon University (Paulk et al., 1993)[ii]. In the CM=
M approach, the scale represents maturity, i.e. the level of organizational=
 capability to reliably perform the process. This maturation reflects the e=
xtent to which each process is institutionalized and managed, ideally with =
quantified measures enabling continuous process improvement.

In many capability models the scale represents maturity, i.e. the level of =
organizational capability to reliably perform the process. This maturation =
reflects the extent to which each process is institutionalized and managed,=
 ideally with quantified measures enabling continuous process improvement.

The model reflects the high level of diversity among UK research institutio=
ns, ranging from the highly research-intensive to those conducting little r=
esearch, or which specialise in certain disciplines. This is not unique to =
the UK. Institutions worldwide are addressing funder and community expectat=
ions of broader research data sharing, but the appropriate level of respons=
e is largely defined by the institutional context. It would be unrealistic =
to expect every institution to provide the same level of service capability=
 across every element of RDM support. RISE aims to help institutions identi=
fy which capabilities are appropriate for them and therefore which areas to=
 prioritise in their service improvement planning.

The level of service capability that it is feasible or desirable to deliver=
 will depend on the institutional context. Where it might be considered ess=
ential that a large research intensive institution provide an in-house data=
 publishing platform, an institution with a modest research capacity may be=
 better to consider outsourcing or sharing aspects of the service, includin=
g the repository platform itself, with other institutions or an external pr=
ovider. The RISE capability model aims to recognise this contextual differe=
nce by providing three possible levels of service capability, using complia=
nce with the main policy expectations of research funders, and legal requir=
ements as a starting point. It should be noted that while the levels offer =
a progression in terms of service capability, RISE does not assume that mor=
e is better. The level of capability offered should be proportionate to cos=
ts that are justifiable, considering local research strategy, available res=
ources, and likely demand for the relevant services.

Aiming to ensure that institutions could use RISE in a variety of contexts,=
 the DCC engaged with 16 UK HE institutions to test its relevance and utili=
ty. Applications ranged from using the tool as a framework for a semi-struc=
tured interview with RDM service managers and selected central support staf=
f, to using it in a group workshop session to discuss data publication need=
s. The RISE outputs from this session informed a more detailed assessment o=
f shortlisted platforms, based on capabilities set out in ReCap, a sister D=
CC model for evaluating data repositories. [vii]

Producing a formal report as part of the RISE process is optional; some use=
rs of the tool have simply found it a useful tool for initiating conversati=
ons between RDM stakeholders to reach a consensus about the service. For ot=
hers, RISE has proved a useful tool for identifying gaps in support provisi=
on and aiding prioritisation decisions, contributing to the development of =
roadmap documents. Working through the RISE framework uncovers useful infor=
mation about the case for service development that can be incorporated into=
 business plans. Use of the RISE framework alongside its sister model ReCap=
 can also help scope high-level requirements for data repository platforms =
to help progress to more detailed discussions around platform selection.



In the European Open Science Cloud (EOSC) pilot project, DCC is working wit=
h partners to develop an integrated competence and capability framework, in=
formed by shared experience in supporting service development and validatin=
g EOSC services. The framework aims to help organisations plan for the effe=
ctive deployment of services that European Research Infrastructures offer r=
esearchers to better enable data science. Institutional research data servi=
ces will also need to be aware of EOSC services and help researchers use th=
em. Joining up competence frameworks in data science and data management, ,=
[xii],[xiii] the EOSC framework will help organisations ensure the right tr=
aining is included in service development roadmaps, and in the career devel=
opment plans for relevant staff.

[vi] Cox, A. M., Kennan, M. A., Lyon, L., & Pinfield, S. (2017). Developmen=
ts in research data management in academic libraries: Towards an understand=
ing of research data service maturity. Journal of the Association for Infor=
mation Science and Technology. Available:

A maturity model is considered as a desired or anticipated evolution from a=
 more ad hoc approach to a more managed process. It is usually defined in d=
iscrete stages for evaluating maturity of organizations or process (Becker,=
 Knackstedt & Po=CC=88ppelbu=C3=9F 2009). A maturity model can also be deve=
loped to evaluate practices applied to individual data products (e.g., Bate=
s and Privette 2012; Peng et al. 2015). A number of maturity models have be=
en developed and utilized to quantifiably evaluate both stewardship process=
es and practices.

This article provides an overview of the current state of assessing the mat=
urity of stewardship of digital scientific data. A list of existing or deve=
loping maturity models from various perspectives of scientific data steward=
ship is provided in Table 1 with a high-level description of each model and=
 its application(s) in Section 3. This allows stewardship practitioners to =
further evaluate the utility of these models for their unique stewardship m=
aturity verification and improvement needs.

Figures 1 and 2 display different perspectives of maturity within the conte=
xt of managing scientific data stewardship activities. They highlight the i=
nterconnectivity and interdependency of different levels of stewardship act=
ivities within individual organizations and different types of maturity for=
 scientific data products through the entire data product lifecycle.

Category of tiered maturity assessment within the context of scientific dat=
a stewardship and examples of existing maturity assessment models. The arro=
ws indicate that the maturity at the initiation point can impact that at th=
e ending point. See Section 3 for a high-level description of each maturity=
 assessment model listed in the diagram.

Category of data product lifecycle-stage-based maturity type and examples o=
f existing assessment models in the form of a matrix. See Section 3 for a h=
igh-level description of each maturity assessment model listed in the diagr=
am.

Table 1 provides a list of existing maturity assessment models, including t=
hose highlighted in Figures 1 and 2. Brief descriptions of these models and=
, where available, their applications are provided in the next section.

McSweeney (2013) reviewed four leading business data management maturity as=
sessment models and concluded that there is lack of consensus about what co=
mprises information management maturity and a lack of rigor and detailed va=
lidation to justify organization process structures. He called for a consis=
tent approach, linked to an information lifecycle (McSweeney 2013).

The Enterprise Data Management Council (EDMC) Data Management Capability As=
sessment Model (DCMM) was released in July 2015 (EDMC 2015). DCMM defines a=
 standard set of evaluation criteria for measuring data management capabili=
ty and is designed to guide organizations to establish and maintain a matur=
e data management program (EDMC 2015; Gorball 2016). A detailed description=
 and comparison of CMMI DMM and EDMC DCMM can be found in Gorball (2016).

The trustworthiness of individual repositories has been the topic of study =
for the data management and preservation community for many years. Based on=
 the Open Archival Information System (OAIS) reference model, ISO 16363 (20=
12) establishes comprehensive audit metrics for what a repository must do t=
o be certified as a trustworthy digital repository (see also CCSDS 2012a). =
Three important qualities of trustworthiness are integrity, sustainability,=
 and support for the entire range of digital repositories in three differen=
t aspects: organizational infrastructure, digital object management, and in=
frastructure and security risk management (ISO 16363 2012; CCSDS 2012b; Wit=
t et al. 2012). A detailed justification for transparency is now recommende=
d in the ISO 16363 repository trustworthiness assessment template.
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