TOGAF Framework For an AI-enabled Software House

The integration of artificial intelligence (AI) in software development has revolutionized the industry, leading to faster and more accurate results. However, the implementation of AI requires a robust framework to ensure effective planning, design, implementation, and maintenance of AI-enabled software systems. The Open Group Architecture Framework (TOGAF) provides such a framework, enabling organizations to develop a structured and integrated approach to AI-enabled software development. In this journal, we present a case study of how a software house utilized the TOGAF framework to integrate AI in their software development processes. We discuss the challenges faced by the organization in the integration process and how the TOGAF framework provided a structured approach to overcome these challenges. We also highlight the benefits that the organization realized through the implementation of AI-enabled software systems. The case study presented in this journal demonstrates the applicability of the TOGAF framework in AI-enabled software development, and its potential to enhance the capabilities and competitiveness of software houses. The TOGAF framework provides a structured approach to the integration of AI in software development, ensuring that organizations can effectively leverage the benefits of AI while minimizing the associated risks and challenges

Survey Paper tentang Enterprise Architecture di Sektor Publik

Saat ini, ketika organisasi menghadapi transformasi konstan, mereka harus menyesuaikan strategi dan aktivitas mereka dengan cepat. Situasi ini melibatkan transformasi bisnis yang berkelanjutan. Namun, mengelola transformasi semacam itu bisa menjadi tugas yang menakutkan karena kompleksitas yang penting dalam organisasi. Oleh karena itu, organisasi dapat menggunakan Enterprise Architecture (EA) untuk membantu membimbing mereka dalam mengatasi kompleksitas dan mencapai tujuan transformasi mereka. Tujuan penelitian ini memberikan kontribusi jenis umum EA, seperti definisi, komponen, manfaat, kerangka kerja, dan hambatan penggunaan EA. Hasil studi tersebut diharapkan dapat memberikan gambaran dan tolak ukur dalam merencanakan dan mengembangkan EA yang sukses, terutama di sektor publik yang penting untuk efektivitas dan efisiensi layanan yang dibutuhkan

BIG DATA ANALYTICS FOR SUMMIT GROUP HOSPITAL USING ENTERPRISE ARCHITECTURE AS STRATEGIC APPROACH

Healthcare is known for being a highly intensive engagement with a complex organisation, and it involves numerous levels of stakeholders. A big data analytics solution is required to simplify and improve the process’s overall data efficacy and flow. However, there are many challenges in implementing big data analytics in a healthcare organisation, as evidenced in some situations. The difficulties that must be addressed are high costs, time-consuming processes in establishing strategic management, and becoming a patient-centred organisation with optimal coordination. As a result, certain studies that have been conducted are suggested a feasible approach for big data analytics is by implementing Enterprise Architecture (EA) in health organisations. The TOGAF ADM model has been chosen as the methodology for implementing EA in a healthcare organisation due to the power of its flexible methods in merging artefacts and its focus on processes. When adopting EA, four architecture layers are examined: Business, Data, Application, and Technology (BDAT). The problems and As-Is environment have been explored, implying that healthcare organisations require EA to assure continuous service delivery. Significantly, the proposed approach will aid stakeholders in quickly adopting the business transformation through the use of EA

Investigating the Role of Enterprise Architecture in Big Data Analytics Implementation: A Case Study in a Large Public Sector Organization

Big Data Analytics (BDA) offers capabilities that can support a wide range of business areas across an organization. Organizations are increasingly turning to Enterprise Architecture (EA) to manage BDA implementation complexities. Through a case study in a large public sector organization, how EA supports various stages of BDA implementation is examined. The findings show that EA can address BDA challenges through 18 specific roles, which are categorised into four domains: Strategy (6 roles), Technology (4 roles), Collaboration (3 roles) and Governance (5 roles). While EA appears to have the most prominent role in strategy planning process, our study also identifies factors that can lead to the ineffectiveness of EA roles, such as frequent changes in business strategy. This study offers important implications to research and practice in EA and BDA implementation

Analytical Study on Building a Comprehensive Big Data Management Maturity Framework

Harnessing big data in organizations today realizes benefits for competitive advantage. Generated profound insights are reflected in informed decision making, creating better business plans, and improved service delivery. Yet, organizations are still not recognizing how mature their big data management capabilities are. However, there is no structured approach to assess and build necessary capabilities for valuable big data utilizing, which draws a clear improvement pathway. Existing solutions lack a consistent perception of big data management capabilities, a reliable assessment, and a rigid improvement scheme. This paper contributes in building an analytical study on existing key works in assessing and building big data management capabilities. Drawing upon the results and gaps revealed from this analytical study, the main requirements for building a comprehensive big data management maturity framework are defined. This framework will enable organizations to assess and improve their current capabilities towards effective big data management.https://dorl.net/dor/ 20.1001.1.20088302.2022.20.1.13.

Roles and capabilities of enterprise architecture in big data analytics technology adoption and implementation

Organizations are attempting to harness the power of big data analytics. Enterprise architecture can be used as an instrument to integrate big data analytics into the existing IT landscape and enabling the development of capabilities to create value from these technologies. Yet, there is limited research about the role of enterprise architecture in adopting big data analytics. This paper explores enterprise architecture roles and capabilities for the adoption of big data analytics by conducting a qualitative case study at the Dutch Tax and Customs Administration. The first attempt to adopt big data analytics was focused on integrating analytics into the current complex IT landscape, but this encountered many challenges and resulted in slow progress. To overcome these challenges, a separate department was created to quickly harness the potential of big data analytics. Enterprise architecture was used for impact analysis and to create a transition process. The findings suggest that enterprise architecture was used in different ways at the various stages of adoption and implementation, requiring different roles and a different set of capabilities. Enterprise architecture was found to be contingent on the type of technology and the situation at hand. We recommend more research into the role of the context in enterprise architecture research.Information and Communication Technolog

Technical Training to Nonprofit Managers Influences Using Big Data Technology in Business Operations

This nonexperimental, survey-based online quantitative study on nonprofit managers’ technical training measures the extent of the influence on big data technology use. The unified theory of acceptance and use of technology is a theoretical framework to determine whether business managers are trained to have know-how in using big data technology. This study followed a quantitative methodology to help narrow the gap in research between what is not known in relation to the nonprofit manager’s technical training on the use of big data technology. Today’s data is the most critical asset, but progress toward big data technology-oriented usage needs to be accessed by the nonprofit. Nonprofits need to use big data technology to gain insights into identifying the program activities and monitor them to make better decisions that maximize societal impact. Big data technology allows nonprofit managers to be effective by getting insights into the problem-solving of the social programs where they operate to reduce unemployment, poverty, social exclusion, and low education levels. This study seeks to answer how nonprofit managers differ in technical training (facilitating conditions) using big data technology compared to managers who have not used big data technology to manage business operations. The study may contribute to bridging existing research gaps in managers’ technical training and using big data technology