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.

Challenges of Data Management in Always-On Enterprise Information Systems

2010 ◽  
pp. 443-462
Author(s):  
Mladen Varga
Keyword(s):  
Information Systems ◽  
Data Management ◽  
Data Privacy ◽  
Data Governance ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Degradation Data ◽  
Association Data ◽  
Data Continuity ◽  
Integration Data

Data management in always-on enterprise information systems is an important function that must be governed, that is, planned, supervised, and controlled. According to Data Management Association, data management is the development, execution, and supervision of plans, policies, programs, and practices that control, protect, deliver, and enhance the value of data and information assets. The challenges of successful data management are numerous and vary from technological to conceptual and managerial. The purpose of this chapter is to consider some of the most challenging aspects of data management, whether they are classified as data continuity aspects (e.g., data availability, data protection, data integrity, data security), data improvement aspects (e.g., coping with data overload and data degradation, data integration, data quality, data ownership/stewardship, data privacy, data visualization) or data management aspect (e.g., data governance), and to consider the means of taking care of them.

Challenges of Data Management in Always-On Enterprise Information Systems

2011 ◽  
pp. 1695-1714 ◽  
Author(s):  
Mladen Varga
Keyword(s):  
Information Systems ◽  
Data Management ◽  
Data Privacy ◽  
Data Governance ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Degradation Data ◽  
Association Data ◽  
Data Continuity ◽  
Integration Data

Data management in always-on enterprise information systems is an important function that must be governed, that is, planned, supervised, and controlled. According to Data Management Association, data management is the development, execution, and supervision of plans, policies, programs, and practices that control, protect, deliver, and enhance the value of data and information assets. The challenges of successful data management are numerous and vary from technological to conceptual and managerial. The purpose of this chapter is to consider some of the most challenging aspects of data management, whether they are classified as data continuity aspects (e.g., data availability, data protection, data integrity, data security), data improvement aspects (e.g., coping with data overload and data degradation, data integration, data quality, data ownership/stewardship, data privacy, data visualization) or data management aspect (e.g., data governance), and to consider the means of taking care of them.

scholarly journals Master Data Management Maturity Model for the Microfinance Sector in Peru

2018 ◽  
Author(s):  
Daniel Vásquez Zúñiga ◽  
Romina Kukurelo Cruz ◽  
Carlos Raymundo Ibañez ◽  
Francisco Dominguez ◽  
Javier M. Moguerza
Keyword(s):  
Data Management ◽  
Maturity Model ◽  
Master Data ◽  
Master Data Management

scholarly journals MASTER DATA MANAGEMENT IMPLEMENTATION IN DISTRIBUTED INFORMATION SYSTEM CASE STUDY DIRECTORATE GENERAL OF TAX, MINISTRY OF FINANCE OF REPUBLIC OF INDONESIA

2019 ◽  
Vol 15 (1) ◽  
pp. 18-27
Author(s):  
Aris Budi Santoso ◽  
Yoga Pamungkas ◽  
Yova Ruldeviyani
Keyword(s):  
Information System ◽  
Data Management ◽  
Reference Data ◽  
Maturity Model ◽  
Maturity Level ◽  
Distributed Information System ◽  
Distributed Information ◽  
Master Data ◽  
Master Data Management ◽  
Directorate General

Information system architecture of Directorate General of Tax (DGT) is centralized with distributed data. The main problem are replication of master and reference data which spread among applications which vary on data structure and the synchronization jobs that spread in many locations and not well managed. Therefore, Master Data Management (MDM) needs to be implemented with accordance to characteristic of centralized distributed information system. Master data management maturity evaluation is conducted using MDM maturity model (MD3M) Spruit dan Pietzka, the result is Data Protection, Data Quality and Maintenance topic have maturity level 3 or defined process stage, while Data Model, Usage and Ownership topic have maturity level 2 or repeatable stage. Solutions to solve MDM issues and to enhance the master data management maturity level are proposed based on Data Management Body of Knowledge (DMBOK). DGT’s MDM issues are related to insufficiency of policy and architecture of MDM system. Policy and architectural approach of centralized MDM system is required to solve that issues. Proposed solution involves the use of data virtualization to enable implementation of centralized system of MDM without consolidate all master and reference data into new database.

Digital Preservation Capability Maturity Model (DPCMM)

2019 ◽  
pp. 152-167
Author(s):  
Lori J. Ashley ◽  
Milovan Misic
Keyword(s):  
Information Technology ◽  
Data Management ◽  
Performance Metrics ◽  
Digital Preservation ◽  
Capability Maturity Model ◽  
Maturity Model ◽  
Capability Maturity ◽  
Management Functions ◽  
Digital Assets

This chapter provides an overview of the genesis and development of the digital preservation capability maturity model (DPCMM) which covers a range of governance, operational, and data management functions associated with the management of long-term (10+ years) and permanent digital assets. The model is organized into three domains: infrastructure, repository, and services. In addition to providing a useful framework for analysis and planning among archivists, content owners and records managers, using a capability maturity model (CMM) to convey the requirements associated with preservation and access to long-term digital assets provides a familiar construct for information technology (IT) architects and system administrators. Each of the 15 DPCMM components has five incremental stages of capability called digital preservation performance metrics.

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.

COMPARISON OF OPEN SOURCE TOOLS FOR PROJECT MANAGEMENT

2013 ◽  
Vol 23 (02) ◽  
pp. 189-209 ◽  
Author(s):  
ANDRÉ MARQUES PEREIRA ◽  
RAFAEL QUEIROZ GONÇALVES ◽  
CHRISTIANE GRESSE VON WANGENHEIM ◽  
LUIGI BUGLIONE
Keyword(s):  
Project Management ◽  
Open Source ◽  
Best Practice ◽  
Management Practices ◽  
Small And Medium Enterprises ◽  
Low Cost ◽  
Software Tools ◽  
Tool Support ◽  
Maturity Model ◽  
Capability Maturity

Software projects often fail, because they are not adequately managed. The establishment of effective and efficient project management practices still remains a key challenge to software organizations. Striving to address these needs, "best practice" models, such as, the Capability Maturity Model Integration (CMMI) or the Project Management Body of Knowledge (PMBOK), are being developed to assist organizations in improving project management. Although not required, software tools can help implement the project management process in practice. In order to provide comprehensive, low-cost tool support for project management, specifically, for small and medium enterprises (SMEs), in this paper we compare the most popular free/open-source web-based project management tools with respect to their compliance to PMBOK and CMMI for Development (CMMI-DEV). The results of this research can be used by organizations to make decisions on tool adoptions as well as a basis for evolving software tools in alignment with best practices models.

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