scholarly journals Data Governance Maturity Model for Micro Financial Organizations in Peru

2017 ◽  
Author(s):  
Stephanie Rivera ◽  
Nataly Loarte ◽  
Carlos Raymundo ◽  
Francisco Dominguez
Keyword(s):  
Maturity Model ◽  
Data Governance

Coordinating Enterprise Services and Data

2013 ◽  
pp. 241-274
Author(s):  
Keith R. Worfolk
Keyword(s):  
Data Management ◽  
Portfolio Management ◽  
Best Practice ◽  
Maturity Model ◽  
Data Governance ◽  
Enterprise Information ◽  
Capability Maturity ◽  
Architecture Framework ◽  
Master Data Management ◽  
Enterprise Soa

The critical inter-dependencies between Enterprise Services and Enterprise Data are often not given due consideration. With the advent of Cloud Computing, it is becoming increasingly important for organizations to understand the relationships between them, in order to formulate strategies to jointly manage and coordinate enterprise services and data to improve business value and reduce risk to the enterprise. Enterprise Services encompass Service-driven applications deployed on-premises in the enterprise data centers as well as in the Cloud for the “extended enterprise.” Enterprise Data Management encompasses the cross-application enterprise-level perspective of data in an information-sharing enterprise, and the critical business data that is created, maintained, enriched, and shared outside the traditional enterprise firewall. This chapter discusses and proposes best practice strategies for coordinating the enterprise SOA & EDM approaches for mutual success. Primary coordination aspects discussed include: Service & Data Governance, Master Data Management, Service-driven & EDM Architecture Roadmaps, Service Portfolio Management, Enterprise Information Architecture, and the Enterprise Data Model. It recommends a facilitative Service-driven Data Architecture Framework & Capability Maturity Model to help enterprises evaluate and optimize overall effectiveness of their coordinated Service-driven & EDM strategies.

The development of the data science capability maturity model: a survey-based research

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mert Onuralp Gökalp ◽  
Ebru Gökalp ◽  
Kerem Kayabay ◽  
Altan Koçyiğit ◽  
P. Erhan Eren
Keyword(s):  
Data Science ◽  
Gap Analysis ◽  
Process Capability ◽  
Capability Maturity Model ◽  
International Electrotechnical Commission ◽  
Maturity Model ◽  
Data Governance ◽  
Content Type ◽  
Capability Maturity ◽  
Development Framework

PurposeThe purpose of this paper is to investigate social and technical drivers of data science practices and develop a standard model for assisting organizations in their digital transformation by providing data science capability/maturity level assessment, deriving a gap analysis, and creating a comprehensive roadmap for improvement in a standardized way.Design/methodology/approachThis paper systematically reviews and synthesizes the existing literature-related to data science and 183 practitioners' considerations by employing a survey-based research method. By blending the findings of this research with a well-established process capability maturity model standard, International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 330xx, and following a methodological maturity development framework, a theoretically grounded model, entitled as the data science capability maturity model (DSCMM) was developed.FindingsIt was found that organizations seek a capability/maturity model standard to evaluate and improve their current data science capabilities. To close this research gap, the DSCMM is developed. It consists of six capability maturity levels and twenty-seven processes categorized under five process areas: organization, strategy management, data analytics, data governance and technology management.Originality/valueThis paper validates the need for a process capability maturity model for the data science domain and develops the DSCMM by integrating literature findings and practitioners' considerations into a well-accepted process capability maturity model standard to continuously assess and improve the maturity of data science capabilities of organizations.

scholarly journals Business intelligence addressing service quality for big data analytics in public sector

2019 ◽  
Vol 16 (1) ◽  
pp. 491
Author(s):  
Sadesh Manikam ◽  
Shamsul Sahibudin ◽  
Vinothini Kasinathan
Keyword(s):  
Big Data ◽  
Service Quality ◽  
Business Intelligence ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Cost Savings ◽  
Maturity Model ◽  
Data Governance ◽  
Public Sectors

<span>With the inauguration of Big Data Analytics initiative nationally, many nations have participated and paved way for BDA ecosystem. The initiative is a catalyst to further encourage economic growth in Public Sectors. Some of the common key deliverables identified are increasing productivity involving information communications technology, cost savings, shared benefits, and encourage innovation. The objectives can be further elaborated by driving big data analytics demands in various public sectors agency, adopting big data analytics framework supporting the building of big data industry. This has encouraged talents and startup companies inspiring their capabilities by developing various technology platform, collaborate and innovate amongst public and private sectors, and further strengthen data governance by creating policy and procedures. With the establishment of big data analytics framework, performance measurement can be enforced effortlessly using the principles of business intelligence maturity model and the technological stack comes with it. Various data sources can be used to benchmark service quality using advanced analytics and data science techniques.</span>

A Study on the Introduction of Big Data Governance into Cultural Governance

2019 ◽  
Vol 41 (2) ◽  
pp. 75-106
Author(s):  
Sunyoung Kim ◽  
Byungwoong Kwon
Keyword(s):  
Big Data ◽  
Data Governance ◽  
Cultural Governance

Data Governance

2020 ◽  
pp. I-VIII
Author(s):  
Kristin Weber ◽  
Christiana Klingenberg
Keyword(s):  
Data Governance
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