Aligning Operational Decisions to Enterprise Objectives Through a Dynamic Enterprise Architecture Approach

2012 ◽  
Author(s):  
B. Chadha ◽  
M. Pemberton ◽  
A. Crockett ◽  
J. Sharkey ◽  
J. Sacks ◽  
...  
Keyword(s):  
Decision Support ◽  
Enterprise Architecture ◽  
Business Models ◽  
Rate Of Change ◽  
Operational Decisions ◽  
Business Decision ◽  
Business Executive ◽  
Decision Systems ◽  
Architecture Framework ◽  
Enterprise Architecture Framework

As the rate of change in both business models and business complexity increases, enterprise architecture can be positioned to supply decision support for executives. The authors propose a dynamic enterprise architecture framework that supports business executive needs for rapid response and contextualized numerical decision support. The classic approaches to business decision making are both over simplified and insufficient to account for the dynamic complexities of reality. Recent failures of historically sound businesses demonstrate that a more robust mathematical approach is required to establish and maintain the alignment between operational decisions and enterprise objectives. We begin with an enterprise architecture (EA) framework that is robust enough to capture the elements of the business within the structure of a meta model that describes how the elements will be stored and tested for completeness and coherence. We add to that the analytical tools needed to innovate and improve the business. Finally, dynamic causal and agent layers are added to account for the qualitative and evolutionary elements that are normally missing or over simplified in most decision systems. This results in a dynamic model of an enterprise that can be simulated and analyzed to answer key business questions and provide decision support. We present a case study and demonstrate how the models are used within the decision framework to support executive decision makers.

scholarly journals Mengukur Performa Enterprise Architecture Framework Menggunakan Fuzzy Tsukamoto

MATICS ◽  
2016 ◽  
Vol 8 (2) ◽  
pp. 54
Author(s):  
Fakri Fandy Nur Azizi
Keyword(s):  
Decision Support ◽  
Support System ◽  
Enterprise Architecture ◽  
Architecture Framework ◽  
Consistency Requirement ◽  
Governance Strategy ◽  
Enterprise Architecture Framework

Enterprise Architecture (EA) adalah deskripsi dari misi stakeholder yang menggambarkan rencana pengembangan sebuah sistem atau sekumpulan sistem untuk mencapai sebuah misi organisasi melalui performansi optimal dari proses bisnis dalam sebuah lingkungan TI yang efisien. Untuk bisa menerapkan EA dalam sebuah organisasi, dibutuhkan kerangka kerja yang bersifat fundamental dan satu set alat pendukung yang digunakan untuk mengembangkan suatu EA. Pengukuran performa EA framework dirasa perlu, untuk mengetahui EA framework yang applicable pada kondisi saat ini.  Sehingga dibutuhkan sebuah <em>decision support</em> untuk membantu memilih EA framework berdasarkan kriteria penilaian dari sisi artifact, governance, strategy, consistency, requirement, guidelines, dan continual. Pada makalah ini dibahas pembuatan decission support system untuk mengukur performa EA framework menggunakan Sistem Inferensi Fuzzy Tsukamoto. Parameter yang digunakan untuk batasan fungsi keanggotaan fuzzy berdasarkan data yang diperoleh dari pakar yaitu artifact, governance, strategy, consistency, requirement, guidelines, dan continual. Akurasi sistem dihitung berdasarkan hasil perbandingan dari keluaran sistem dengan hasil penilaian pakar.

A Comparative Analysis of the Integration of SOA Elements in Widely-Used Enterprise Architecture Frameworks

2013 ◽  
Vol 9 (2) ◽  
pp. 54-70 ◽  
Author(s):  
Ayed Alwadain ◽  
Erwin Fielt ◽  
Axel Korthaus ◽  
Michael Rosemann
Keyword(s):  
Decision Support ◽  
Comparative Analysis ◽  
Enterprise Architecture ◽  
Service Oriented Architecture ◽  
It Alignment ◽  
Architecture Framework ◽  
Service Oriented ◽  
Architecture Frameworks ◽  
Enterprise Architecture Framework ◽  
Over Time

In recent years, enterprise architecture (EA) has captured increasing interest as a means to systematically consolidate and manage various enterprise artefacts in order to provide holistic decision support for business/IT alignment and business/IT landscapes management. To provide a holistic perspective on the enterprise over time, EA frameworks need to co-evolve with the changes in the enterprise and its IT over time. In this paper the authors focus on the emergence of Service-Oriented Architecture (SOA). There is a need to integrate SOA with EA to keep EA relevant and to use EA products to help drive successful SOA. This paper investigates and compares the integration of SOA elements in five widely used EA frameworks: Archimate, The Open Group Architecture Framework (TOGAF), Federal Enterprise Architecture Framework (FEAF), Department of Defence Architecture Framework (DoDAF) and the Ministry of Defence Architecture Framework (MODAF). It identifies what SOA elements are considered and their relative position in the overall structure. The results show that services and related elements are far from being well-integrated constructs in current EA frameworks and that the different EA frameworks integrated SOA elements in substantially different ways. The results can support the academic EA and SOA communities with a closer and more consistent integration of EA and SOA and support practitioners in identifying an EA framework that provides the SOA support that matches their requirements.

scholarly journals Driving digital transformation of comprehensive primary health services at scale in India: an enterprise architecture framework

2021 ◽  
Vol 6 (Suppl 5) ◽  
pp. e005242
Author(s):  
Sunita Nadhamuni ◽  
Oommen John ◽  
Mallari Kulkarni ◽  
Eshan Nanda ◽  
Sethuraman Venkatraman ◽  
...  
Keyword(s):  
Primary Care ◽  
Health Services ◽  
Primary Healthcare ◽  
Enterprise Architecture ◽  
Digital Health ◽  
Security And Privacy ◽  
Primary Health ◽  
Architecture Framework ◽  
Primary Health Services ◽  
Enterprise Architecture Framework

In its commitment towards Sustainable Development Goals, India envisages comprehensive primary health services as a key pillar in achieving universal health coverage. Embedded in siloed vertical programmes, their lack of interoperability and standardisation limits sustainability and hence their benefits have not been realised yet. We propose an enterprise architecture framework that overcomes these challenges and outline a robust futuristic digital health infrastructure for delivery of efficient and effective comprehensive primary healthcare. Core principles of an enterprise platform architecture covering four platform levers to facilitate seamless service delivery, monitor programmatic performance and facilitate research in the context of primary healthcare are listed. A federated architecture supports the custom needs of states and health programmes through standardisation and decentralisation techniques. Interoperability design principles enable integration between disparate information technology systems to ensure continuum of care across referral pathways. A responsive data architecture meets high volume and quality requirements of data accessibility in compliance with regulatory requirements. Security and privacy by design underscore the importance of building trust through role-based access, strong user authentication mechanisms, robust data management practices and consent. The proposed framework will empower programme managers with a ready reference toolkit for designing, implementing and evaluating primary care platforms for large-scale deployment. In the context of health and wellness centres, building a responsive, resilient and reliable enterprise architecture would be a fundamental path towards strengthening health systems leveraging digital health interventions. An enterprise architecture for primary care is the foundational building block for an efficient national digital health ecosystem. As citizens take ownership of their health, futuristic digital infrastructure at the primary care level will determine the health-seeking behaviour and utilisation trajectory of the nation.

The Business Transformation Enterprise Architecture Framework

2021 ◽  
Vol 13 (1) ◽  
pp. 74-101
Author(s):  
Antoine Trad
Keyword(s):  
Research Method ◽  
Enterprise Architecture ◽  
Real Life ◽  
Market Development ◽  
Decision Making Process ◽  
Strategic Development ◽  
Business Transformation ◽  
Architecture Framework ◽  
Base Functions ◽  
Enterprise Architecture Framework

This chapter's author based his cross-functional research on an authentic and proprietary mixed research method that is supported by intelligent neural networks combined with a heuristics motor, named the applied mathematical model (AMM). The proposed AMM base functions like the human empiric decision-making process that can be compared to the behaviour-driven development. The AMM is supported by many real-life cases of business and architecture transformation projects in the domain of intelligent strategic development and operations (iSDevOps) that is supported by the alignment of various standards and development strategies that biases the standard market development and operations (DevOps) procedures, which are Sisyphean tasks.

scholarly journals Developing the indonesian government enterprise architecture framework appropriate for Indonesian government agencies

2019 ◽  
Vol 8 (3) ◽  
pp. 152
Author(s):  
Asbartanov Lase ◽  
Benny Ranti
Keyword(s):  
New Zealand ◽  
Enterprise Architecture ◽  
Research Process ◽  
Government Agencies ◽  
Implementation Cost ◽  
Institutional Level ◽  
Indonesian Government ◽  
Architecture Framework ◽  
Government Enterprise ◽  
Enterprise Architecture Framework

<span>This research was conducted to develop the Indonesian Government Enterprise Architecture (IGEA) framework which is suitable for Indonesian government agencies. Due to their complexity and expensive implementation cost, existing EA frameworks such as TOGAF and Zachman have so far not been the choice for building GEA by some countries including Australia and New Zealand. Those countries have built their own GEA namely Australia’s AGA and New Zealand’s GEA-NZ, respectively. Learning from this experience, the authors did a research to build Indonesia’s GEA or IGEA. This paper explains the research process which starts from mapping or comparing TOGAF, AGA, and GEA-NZ frameworks to get the underlying foundation for building GEA, analyzing framework artifacts, to building IGEA by adding specific Indonesian regulations and policies such as RPJMN and Nawacita. This IGEA framework is expected to become a reference for developing EA not only at institutional level but also the most important thing at national or cross institutional level, in order to increase the effectiveness of government IT spending.</span>

scholarly journals Another Look at Enterprise Architecture Framework

2018 ◽  
Vol 6 (2) ◽  
pp. 172 ◽  
Author(s):  
Shuichiro Yamamoto ◽  
Nada Ibrahem Olayan ◽  
Shuji Morisaki
Keyword(s):  
Enterprise Architecture ◽  
Architecture Framework ◽  
Key Features ◽  
Enterprise Architecture Framework

<p><em>Although there were many comparison literatures of EA frameworks, these literature use qualitative criteria based on intuitive practitioner’s experience. The paper first defines 36 concrete features of EA frameworks using six categories and six interrogatives.</em> <em>Then</em><em> </em><em>we concretely compare</em><em> </em><em>typical</em><em> </em><em>EA</em><em> </em><em>frameworks based on the key features. The result shows the easiness and concreteness of the proposed EA comparison framework.</em><em></em></p>

The Business Transformation Framework and Enterprise Architecture Framework for Managers in Business Innovation

2019 ◽  
pp. 20-49
Author(s):  
Antoine Trad
Keyword(s):  
Knowledge Management ◽  
Software Engineering ◽  
Enterprise Architecture ◽  
Global Business ◽  
Business Transformation ◽  
Knowledge Based ◽  
Architecture Framework ◽  
Enterprise Architecture Framework ◽  
Global Software Engineering ◽  
Technical Recommendations

The KMGSE offers a real-life case for detecting and processing an enterprise knowledge management model for global business transformation, knowledge management systems, global software engineering, global business engineering and enterprise architecture recurrent problems solving. This global software engineering (GSE) subsystem is a driven development model that offers a set of possible solutions in the form of architecture, method, patterns, managerial and technical recommendations, coupled with an applicable framework. The proposed executive and technical recommendations are to be applied by the business environment's knowledge officers, architects, analysts and engineers to enable solutions to knowledge-based, global software engineering paradigms' development and maintenance.

The Use of an Enterprise Architecture Framework to Guide the Management of Big Data in Health Organisations

2021 ◽  
pp. 39-50
Author(s):  
Monica Nehemia ◽  
Tandokazi Zondani
Keyword(s):  
Big Data ◽  
Enterprise Architecture ◽  
Business Processes ◽  
High Rate ◽  
Infrastructure Development ◽  
Multiple Sources ◽  
Technology Infrastructure ◽  
Public And Private ◽  
Architecture Framework ◽  
Enterprise Architecture Framework

Big data has gained popularity in recent years, with increased interest from both public and private organisations including academics. The automation of business processes led to the proliferation of different types of data at various speeds through information systems. Big data is generated at a high rate from multiple sources that can become complex to manage with challenges to collect, manipulate, and store data with traditional IS/IT. Big data has been associated with technical non-technical challenges. Due to these challenges, organisations deploy enterprise architecture as an approach to holistically manage and mitigate challenges associated with business and technology. An exploratory study was done to determine how EA could be used to manage big data in healthcare facilities. This study employs the interpretive approach with documentation as the analysis. Findings were governance, internal and external big data sources, information technology infrastructure development, and big data skills. Through the different EA domains, big data challenges could be mitigated.

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