Enterprise Architecture and the Big Data Technical Ecosystem

2017 ◽  
pp. 163-194
Author(s):  
Unhelkar Bhuvan
Keyword(s):  
Big Data ◽  
Enterprise Architecture

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.

scholarly journals Secure big data ecosystem architecture: challenges and solutions

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Memoona J. Anwar ◽  
Asif Q. Gill ◽  
Farookh K. Hussain ◽  
Muhammad Imran
Keyword(s):  
Big Data ◽  
Organizational Context ◽  
Enterprise Architecture ◽  
Security And Privacy ◽  
Complex Data ◽  
Privacy And Security ◽  
Data Intensive ◽  
Security Models ◽  
Individual System ◽  
Data Ecosystem

AbstractBig data ecosystems are complex data-intensive, digital–physical systems. Data-intensive ecosystems offer a number of benefits; however, they present challenges as well. One major challenge is related to the privacy and security. A number of privacy and security models, techniques and algorithms have been proposed over a period of time. The limitation is that these solutions are primarily focused on an individual or on an isolated organizational context. There is a need to study and provide complete end-to-end solutions that ensure security and privacy throughout the data lifecycle across the ecosystem beyond the boundary of an individual system or organizational context. The results of current study provide a review of the existing privacy and security challenges and solutions using the systematic literature review (SLR) approach. Based on the SLR approach, 79 applicable articles were selected and analyzed. The information from these articles was extracted to compile a catalogue of security and privacy challenges in big data ecosystems and to highlight their interdependencies. The results were categorized from theoretical viewpoint using adaptive enterprise architecture and practical viewpoint using DAMA framework as guiding lens. The findings of this research will help to identify the research gaps and draw novel research directions in the context of privacy and security in big data-intensive ecosystems.

AI-driven platform enterprise maturity: from human led to machine governed

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sergey Yablonsky
Keyword(s):  
Big Data ◽  
Organizational Performance ◽  
Value Chain ◽  
Large Scale ◽  
Enterprise Architecture ◽  
Reference Model ◽  
Maturity Model ◽  
Content Type ◽  
Challenges And Opportunities ◽  
Advanced Analytics

PurposeTo be more effective, artificial intelligence (AI) requires a broad overall view of the design and transformation of enterprise architecture and capabilities. Maturity models (MMs) are the recognized tools to identify strengths and weaknesses of certain domains of an organization. They consist of multiple, archetypal levels of maturity of a certain domain and can be used for organizational assessment and development. In the case of AI, quite a few numbers of MMs have been proposed. Generally, the links between AI technology, AI usage and organizational performance stay unclear. To address these gaps, this paper aims to introduce the complete details of the AI maturity model (AIMM) for AI-driven platform companies. The associated AI-Driven Platform Enterprise Maturity framework proposed here can help to achieve most of the AI-driven platform companies' objectives.Design/methodology/approachQualitative research is performed in two stages. In the first stage, a review of the existing literature is performed to identify the types, barriers, drivers, challenges and opportunities of MMs in AI, Advanced Analytics and Big Data domains. In the second stage, a research framework is proposed to align company value chain with AI technologies and levels of the platform enterprise maturity.FindingsThe paper proposes a new five level AI-Driven Platform Enterprise Maturity framework by constructing a formal organizational value chain taxonomy model that explains a vast group of MM phenomena related with the AI-Driven Platform Enterprises. In addition, this study proposes a clear and precise description and structuring of the information in the multidimensional Platform, AI, Advanced Analytics and Big Data domains. The AI-Driven Platform Enterprise Maturity framework assists in identification, creation, assessment and disclosure research of AI-driven platform business organizations.Research limitations/implicationsThis research is focused on the basic dimensions of AI value chain. The full reference model of AI consists of much more concepts. In the last few years, AI has achieved a notable drive that, if connected appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in machine learning, especially in deep neural networks, the entire community stands in front of the barrier of explainability. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models in industry. Our prospects lead toward the concept of a methodology for the large-scale implementation of AI methods in platform organizations with fairness, model explainability and accountability at its core.Practical implicationsAI-driven platform enterprise maturity framework can be used for better communicate to clients the value of AI capabilities through the lens of changing human-machine interactions and in the context of legal, ethical and societal norms.Social implicationsThe authors discuss AI in the enterprise platform stack including talent platform, human capital management and recruiting.Originality/valueThe AI value chain and AI-Driven Platform Enterprise Maturity framework are original and represent an effective tools for assessing AI-driven platform enterprises.

scholarly journals Data Science for Finance: Best-Suited Methods and Enterprise Architectures

2021 ◽  
Vol 4 (3) ◽  
pp. 69
Author(s):  
Galena Pisoni ◽  
Bálint Molnár ◽  
Ádám Tarcsi
Keyword(s):  
Big Data ◽  
Financial Services ◽  
Data Science ◽  
Enterprise Architecture ◽  
Data Driven ◽  
Financial Company ◽  
Desk Research ◽  
Qualitative Literature ◽  
Analyze Data

We live in an era of big data. Large volumes of complex and difficult-to-analyze data exist in a variety of industries, including the financial sector. In this paper, we investigate the role of big data in enterprise and technology architectures for financial services. We followed a two-step qualitative process for this. First, using a qualitative literature review and desk research, we analyzed and present the data science tools and methods financial companies use; second, we used case studies to showcase the de facto standard enterprise architecture for financial companies and examined how the data lakes and data warehouses play a central role in a data-driven financial company. We additionally discuss the role of knowledge management and the customer in the implementation of such an enterprise architecture in a financial company. The emerging technological approaches offer opportunities for finance companies to plan and develop additional services as presented in this paper.

scholarly journals Summary of Potential and Exploitation of Big Data and AI in Bioeconomy

2021 ◽  
pp. 417-423
Author(s):  
Caj Södergård
Keyword(s):  
Big Data ◽  
Precision Agriculture ◽  
Enterprise Architecture ◽  
Earth Observation ◽  
Forest Monitoring ◽  
Maintenance Cost ◽  
Crop Species ◽  
Real Time Processing ◽  
Development Platform ◽  
Speed Up

AbstractIn this final chapter, we summarize the DataBio learnings about how to exploit big data and AI in bioeconomy. The development platform for the software used in the 27 pilots was a central tool. The Enterprise Architecture model Archimate laid a solid basis for the complex software in the pilots. Handling data from sensors and earth observation were shown in numerous pilots. Genomic data from crop species allows us to significantly speed up plant breeding by predicting plant properties in-silico. Data integration is crucial and we show how linked data enables searches over multiple datasets. Real-time processing of events provides insights for fast decision-making, for example about ship engine conditions. We show how sensitive bioeconomy data can be analysed in a privacy-preserving way. The agriculture pilots show with clear numbers the impact of big data and AI on precision agriculture, insurance and subsidies control. In forestry, DataBio developed several big data tools for forest monitoring. In fishery, we demonstrate how to reduce maintenance cost and time as well as fuel consumption in the operation of fishing vessels as well as how to accurately predict fish catches. The chapter ends with perspectives on earth observation, machine learning, data sharing and crowdsourcing.

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