371 American Academic Scientific Research Journal for Engineering, Technology, and Sciences ISSN (Print) 2313-4410, ISSN (Online) 2313-4402 https://asrjetsjournal.org/index.php/American_Scientific_Journal/index Faster Time-To-Market: Cloud’s Role in Accelerating Software Deployment Abdurrakhimov Elnur * Cloud Architect and Developer, Simple Booth, Valencia, Spain Abstract Cloud computing has revolutionized software deployment by enabling rapid provisioning, scalability, and automation, significantly reducing time-to-market. This study examines the impact of cloud technologies on deployment cycles, focusing on the advantages of on-demand provisioning, Continuous Integration/Continuous Deployment (CI/CD) pipelines, and elasticity. Case studies from startups, enterprises, and multinational organizations illustrate how cloud adoption has streamlined workflows, improved resource utilization, and enhanced global scalability. The findings highlight a stark contrast between traditional IT environments and cloud-based solutions, emphasizing the role of cloud computing in driving competitive advantage. Despite challenges such as vendor lock-in and compliance complexities, strategic approaches and robust governance, can help organizations fully leverage the benefits of the cloud. This research provides actionable insights for businesses seeking to optimize deployment efficiency and maintain agility in rapidly evolving markets. Keywords: Cloud Computing, Software Deployment; Time-to-Market; Elasticity; Continuous Integration/Continuous Deployment (CI/CD); DevOps; Scalability; Automation; Digital Transformation. 1. Introduction In today’s highly competitive digital economy, time-to-market is a critical factor for success in software development. The speed at which a product can move from concept to launch significantly influences its adoption, revenue potential, and market position. Companies that can deliver new features, updates, and products swiftly are better equipped to meet customer expectations and respond to market demands [1]. This urgency has driven the adoption of agile methodologies and DevOps practices to streamline development processes. ------------------------------------------------------------------------ Received: 5/15/2025 Accepted: 7/1/2025 Published: 7/13/2025 ------------------------------------------------------------------------ * Corresponding author. https://asrjetsjournal.org/index.php/American_Scientific_Journal/index American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 102, No 1, pp 371-381 372 Cloud computing has emerged as a transformative technology that further accelerates software deployment cycles. By providing on-demand access to computing resources, cloud platforms eliminate many traditional bottlenecks, such as hardware procurement and manual server configuration. As noted by Armbrust and his colleagues [2], the elasticity and scalability of cloud services enable organizations to dynamically allocate resources, allowing them to focus on development and innovation rather than infrastructure management. This shift is not only improving deployment timelines but also enhancing the ability to experiment and iterate in real time. Traditional approaches to software deployment often involve significant challenges, such as long provisioning times for physical hardware and resource constraints within IT departments. Setting up on- premises infrastructure typically requires substantial capital investment and can lead to delays due to procurement and configuration processes [3]. Furthermore, these setups lack the flexibility needed to accommodate rapid changes in development needs, often resulting in slower deployment cycles and delayed product launches. The inability to scale resources dynamically also poses a major limitation. Fixed infrastructure can become a bottleneck during high-demand periods, while overprovisioning leads to resource wastage. These challenges hinder organizations from achieving the agility required to maintain a competitive edge in fast-evolving markets. This article aims to address the following key research questions: 1. How does the on-demand nature of cloud resources impact software deployment cycles? 2. What are the key factors enabling faster go-to-market strategies using cloud computing? By answering these questions, the study seeks to provide insights into how cloud technology supports software development teams in reducing time-to-market and achieving greater operational efficiency. The implications of faster time-to-market extend beyond operational efficiency, impacting the overall competitiveness of organizations in the software industry. Companies that leverage cloud computing can deliver products and updates more quickly, enabling them to capture market opportunities and adapt to changing customer needs. Additionally, the reduced reliance on traditional IT infrastructure allows businesses to allocate resources more effectively, fostering innovation and growth [4]. Understanding how cloud computing accelerates deployment cycles is crucial for organizations aiming to adopt modern development practices. It also highlights the strategic value of cloud technology as a driver of digital transformation in the software industry. By exploring the factors that contribute to faster go-to-market strategies, this study aims to provide actionable insights for businesses seeking to enhance their competitive position. 2. Methods and materials This study employs a mixed-methods approach to analyze the role of cloud computing in accelerating software deployment. The primary methodology involves a combination of case study analysis, literature review, and secondary data analysis. The case studies focus on real-world examples of organizations that have successfully American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 102, No 1, pp 371-381 373 leveraged cloud technologies to achieve faster time-to-market. The literature review examines existing research, reports, and academic publications on cloud computing, deployment cycles, and time-to-market strategies. Secondary data from industry reports and cloud service providers are also incorporated to support empirical findings and provide quantitative insights. The data for this study is drawn from multiple reliable sources: ⎯ Case Studies. Detailed case studies of companies from different industries that have adopted cloud solutions for software deployment. These cases highlight the challenges faced before cloud adoption, the strategies implemented, and the outcomes achieved. ⎯ Industry Reports. Reports from leading cloud service providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud, focusing on deployment cycle improvements, scalability metrics, and provisioning times. ⎯ Expert Insights. Interviews and commentaries from industry experts and cloud practitioners, providing qualitative insights into the practical applications and benefits of cloud technologies. ⎯ Academic Publications. Peer-reviewed articles and conference papers on cloud computing, DevOps, and software engineering, sourced from databases like IEEE Xplore, ScienceDirect, and SpringerLink. By triangulating data from these sources, the study ensures a comprehensive and balanced analysis of the subject. The analysis framework is designed to evaluate the effectiveness of cloud technologies in software deployment across three dimensions: 1. Deployment time comparison. Assessing differences in provisioning and deployment times before and after cloud adoption, using metrics such as time-to-deploy (TTD) and time-to-market (TTM). 2. Resource utilization. Evaluating how cloud elasticity and scalability impact resource efficiency and cost-effectiveness during software deployment cycles. 3. Scalability and flexibility. Analyzing how cloud platforms enable organizations to scale resources on- demand and adapt to varying workloads, particularly during product launches or updates. These dimensions are applied to each case study to identify patterns, derive insights, and highlight best practices for leveraging cloud technologies to accelerate software deployment. The findings are then synthesized to provide actionable recommendations for businesses aiming to enhance their deployment strategies. 3. Results The adoption of cloud computing has significantly reduced provisioning times compared to traditional IT infrastructures. In conventional setups, procuring and configuring physical hardware can take weeks or even months, leading to delays in deployment and increased time-to-market. In contrast, cloud platforms offer on- demand access to virtualized resources, enabling rapid provisioning and scalability. American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 102, No 1, pp 371-381 374 Traditional IT environments often require weeks or even months to procure and configure the necessary hardware for software deployment. This delay poses a significant bottleneck in time-to-market strategies, especially in dynamic and competitive industries [5]. Cloud computing, by contrast, offers on-demand provisioning, enabling organizations to reduce deployment times from weeks to mere hours or minutes. As demonstrated in the comparison (Figure 1), traditional deployment processes may take up to 30 days for hardware procurement and setup, while cloud environments can accomplish the same provisioning tasks within a single day. This drastic reduction in provisioning time highlights the transformative impact of cloud technologies on deployment efficiency [6]. Figure 1: traditional vs Cloud-Based deployment timeline For example, a study on AWS Lambda demonstrated that Netflix's serverless architecture could scale to handle thousands of concurrent function executions within seconds. This level of agility is impossible to achieve with traditional infrastructure, where provisioning and managing an equivalent compute environment would require significant time and manual intervention [7]. Furthermore, cloud computing's elasticity allows for automatic scaling of resources to meet fluctuating demand, reducing the need for overprovisioning and enabling more efficient use of resources. This dynamic provisioning capability ensures that applications have the necessary resources when needed, without the delays associated with manual hardware adjustments [8]. The integration of DevOps practices, Continuous Integration/Continuous Deployment (CI/CD) pipelines, and American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 102, No 1, pp 371-381 375 automation within cloud environments has markedly accelerated software deployment cycles. Organizations adopting these methodologies have reported significant improvements in deployment frequency, system reliability, and recovery times. For instance, companies implementing DevOps have achieved up to 200 times more frequent deployments compared to traditional software development approaches. Additionally, these organizations experience 24 times faster recovery from failures and a threefold decrease in change failure rates. These metrics underscore the efficiency and resilience gained through DevOps and CI/CD automation [9]. A notable example is United Airlines, which enhanced its deployment processes by adopting CI/CD pipelines. By utilizing automated pipelines, the airline accelerated its deployment frequency by 75%, leading to more timely updates and improved operational efficiency [10]. Similarly, a FinTech company partnered with Aokumo to implement cloud-native CI/CD and DevOps practices. This collaboration resulted in a threefold increase in deployment speed and a fourfold boost in developer productivity, highlighting the transformative impact of automation and cloud integration on development workflows [11]. These cases illustrate that the adoption of DevOps methodologies, coupled with CI/CD pipelines and automation in cloud settings, substantially accelerates deployment cycles. The resulting improvements in deployment frequency, system reliability, and developer productivity provide organizations with a competitive edge in the rapidly evolving software industry. Case Study 1: A Startup’s Rapid Deployment Leveraging a Cloud Platform A global leader in public safety and enterprise security solutions faced a significant surge in demand for its body camera product line in 2020. To meet this increased demand, the company partnered with NTT DATA to accelerate the development of a cloud-based platform capable of securely processing and storing large volumes of video and transcript data. This collaboration enabled the firm to rapidly deploy the necessary infrastructure, ensuring feature parity with its existing on-premises offerings and effectively managing the spike in market demand [12]. Case Study 2: An Enterprise’s Transition to Cloud Infrastructure and Its Impact on Digital Transformation Carlsberg, a renowned global brewing company, embarked on a digital transformation journey by transitioning to a scalable and flexible cloud infrastructure. With the assistance of Accenture, Carlsberg successfully shifted 100% of its systems and applications from a legacy environment to the cloud. This migration led to a significant reduction in major system incidents, decreasing from an average of 13 per month to just five. The enhanced reliability, security, and disaster recovery capabilities provided by the cloud infrastructure have been pivotal in supporting Carlsberg's transition to a digital business [13]. Case Study 3: A Multinational’s Global Deployment Using the Cloud American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 102, No 1, pp 371-381 376 Thomson Reuters, a multinational media conglomerate, initiated a cloud-first strategy to enhance its global operations. Partnering with Amazon Web Services (AWS), the company developed a cloud-based platform that significantly improved operational efficiency and cost optimization. This transformation enabled Thomson Reuters to rapidly incorporate generative AI technologies across its products, particularly benefiting their legal and accounting divisions. The cloud infrastructure facilitated swift global deployment, allowing the company to adapt quickly to market changes and technological advancements [14]. These case studies illustrate the diverse ways in which organizations—from startups to multinational enterprises—have leveraged cloud platforms to accelerate deployment, enhance digital transformation, and achieve global scalability. 4. Discussion The findings clearly demonstrate that cloud computing significantly enhances the efficiency of software deployment, resulting in a marked reduction in time-to-market. Case studies and empirical data show that traditional IT infrastructure, with its lengthy hardware procurement and configuration processes, is no longer competitive in the face of cloud-enabled deployment strategies. By leveraging cloud technologies, organizations can rapidly provision resources, automate workflows, and ensure seamless scalability, enabling them to deploy applications and updates at an unprecedented pace. The success stories outlined in this study demonstrate how startups, enterprises, and multinational corporations have achieved significant improvements in deployment efficiency. For instance, the rapid provisioning capabilities of Microsoft Azure and Google Cloud Platform (GCP) allow organizations to deploy thousands of containers within seconds, drastically reducing infrastructure setup time [15]. Similarly, companies like HSBC and Twitter have leveraged cloud-based solutions to enhance operational reliability, minimize downtime, and improve overall system performance. The effectiveness of cloud computing in accelerating deployment cycles is underpinned by several enabling factors: ⎯ Cloud platforms integrate tools for automating deployment processes, such as Continuous Integration/Continuous Deployment (CI/CD) pipelines. Automation minimizes human intervention, reduces errors, and enables frequent and consistent updates, as seen in the case of United Airlines, which achieved a 75% acceleration in deployment frequency using automated pipelines. ⎯ The ability to dynamically scale resources up or down based on demand eliminates the need for overprovisioning or underutilization of resources. This ensures that applications have optimal performance during peak periods without delays caused by manual adjustments. ⎯ Cloud providers offer a distributed network of data centers, enabling organizations to deploy applications across multiple regions simultaneously. This feature reduces latency, ensures better performance for end-users, and facilitates global rollouts, as demonstrated by Thomson Reuters’ use of American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 102, No 1, pp 371-381 377 AWS for global scalability [16]. These factors collectively create an environment where software deployment becomes faster, more reliable, and scalable. Organizations that leverage these capabilities not only reduce time-to-market but also gain a significant competitive advantage in responding to market demands and technological trends. Elasticity is one of the most critical advantages of cloud computing. Unlike traditional IT infrastructures, which operate at a fixed capacity and often result in resource underutilization or bottlenecks during demand spikes, cloud platforms provide dynamic resource scaling. This allows organizations to adjust resource allocation in real-time based on fluctuating demand, ensuring optimal performance and cost-efficiency. As depicted in Figure 2, traditional infrastructure maintains a constant resource utilization level (red dashed line), which can lead to inefficiencies during low-demand periods and insufficient capacity during peaks. In contrast, cloud infrastructure (green line) dynamically scales resources up or down, closely matching demand patterns. This flexibility minimizes waste and ensures uninterrupted performance during critical periods, such as product launches or high-traffic events. Figure 2: elasticity and scalability in cloud environments The findings underscore the stark contrast between cloud-enabled deployment processes and traditional IT environments. Traditional methods of software deployment are characterized by significant delays due to hardware procurement, setup, and configuration. For example, acquiring and configuring physical servers in an on-premises data center can take weeks or months, hindering a company’s ability to respond quickly to market demands. These fixed infrastructures often lack the flexibility to scale dynamically, resulting in resource American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 102, No 1, pp 371-381 378 underutilization during low-demand periods and bottlenecks during peak usage. In contrast, cloud computing removes these limitations through on-demand scalability and flexibility. Services like AWS Lambda enable rapid provisioning, with Netflix leveraging AWS to scale thousands of function executions within seconds. This efficiency allows companies to prioritize innovation and development rather than dealing with complex infrastructure management [7]. Furthermore, global availability through cloud providers facilitates simultaneous deployments across multiple regions, a capability not feasible with traditional setups. Despite its advantages, cloud computing is not without challenges. Key issues include: ⎯ Organizations that rely heavily on a single cloud provider may face difficulties migrating to other platforms due to proprietary technologies or data incompatibility. This can limit flexibility and increase long-term dependency [17]. ⎯ Adhering to data privacy and security regulations across different jurisdictions can be complex in a cloud environment, particularly for global deployments. Mismanagement of compliance requirements may lead to legal and financial repercussions. ⎯ The rapid pace of cloud adoption and deployment can result in accumulated technical debt, such as poorly documented code or configurations, which may hinder future scalability and maintenance. Addressing these challenges requires a strategic approach to cloud adoption. Businesses must implement robust governance frameworks, prioritize platform-agnostic architectures, and regularly audit compliance measures. To leverage cloud computing for enhanced deployment efficiency, businesses should consider the following actionable insights: ⎯ Integrate automation tools and CI/CD pipelines to streamline workflows and reduce manual intervention. These practices not only accelerate deployment but also improve system reliability [18]. ⎯ Distribute workloads across cloud providers to mitigate vendor lock-in risks and enhance operational flexibility. ⎯ Develop a clear compliance strategy tailored to the jurisdictions where the business operates. Leverage cloud provider tools that support data security and governance. ⎯ Equip teams with the knowledge and skills needed to manage cloud environments effectively, ensuring smooth transitions and minimizing technical debt. ⎯ Design applications with scalability in mind to fully utilize the dynamic resource allocation capabilities of the cloud. By adopting these strategies, businesses can maximize the benefits of cloud computing, reduce deployment American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 102, No 1, pp 371-381 379 times, and achieve a competitive advantage in the software market. 5. Conclusions This study highlights the transformative role of cloud computing in accelerating software deployment and reducing time-to-market. By enabling rapid provisioning, automating workflows, and offering scalability and global availability, cloud platforms address many limitations inherent in traditional IT infrastructures. The findings illustrate that organizations adopting cloud technologies experience significant improvements in deployment cycles, operational efficiency, and overall competitiveness. Key takeaways from the analysis include: ⎯ Cloud computing dramatically reduces the time required to provision resources, shifting from weeks or months in traditional environments to mere seconds or minutes in cloud settings. This agility allows businesses to respond swiftly to market demands and accelerate their go-to-market strategies. ⎯ The integration of DevOps practices, CI/CD pipelines, and automation in cloud environments facilitates frequent and reliable deployments. These tools empower development teams to deliver updates and innovations faster while maintaining high standards of quality. ⎯ The elasticity of cloud resources ensures that organizations can dynamically scale workloads to meet demand, minimizing both underutilization and overprovisioning. This capability supports growth and enhances customer satisfaction, particularly during global deployments. Despite its many advantages, cloud computing is not without challenges. Issues such as vendor lock-in, compliance complexities, and technical debt require strategic planning and governance. While some businesses adopt a multi-cloud approach for flexibility, many successfully operate within a single cloud ecosystem. The study underscores the importance of leveraging cloud computing to remain competitive in the fast-evolving software industry. By adopting cloud-driven strategies, organizations can achieve faster time-to-market, reduce operational costs, and foster innovation. Future research should explore: ⎯ The long-term economic impact of cloud adoption on businesses. ⎯ The role of emerging technologies, such as artificial intelligence and edge computing, in further optimizing deployment cycles. In conclusion, cloud computing represents a paradigm shift in software development, enabling organizations to accelerate deployment cycles, enhance agility, and maintain a competitive edge in the digital era. By addressing existing challenges and strategically implementing cloud technologies, businesses can unlock new opportunities for growth and innovation. American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 102, No 1, pp 371-381 380 References [1] N. Carroll, K. Conboy, X. Wang. “From transformation to normalisation: An exploratory study of a large-scale agile transformation. ” The Journal of Information Technology, vol.38, no.3, pp.267-303, 2023. https://doi.org/10.1177/02683962231164428 [2] M. Armbrust, A. Fox, R. Griffith, A.D. Joseph, R. Katz, A. Konwinski, ... M. Zaharia. “A view of cloud computing.” The Communications of the ACM, vol. 53, no. 4, pp.50-58, 2010. Vol. 53, no. 4. P. 50–58. https://doi.org/10.1145/1721654.1721672. [3] R. Buyya, C.S. Yeo, S. Venugopal, J. Broberg, I. Brandic. “Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility.” The Future Generation Computer Systems, vol. 25, no. 6, pp. 599-616, 2013. https://doi.org/10.1016/j.future.2008.12.001. [4] S. Marston, Z. Li, S. Bandyopadhyay, J. Zhang, A. Ghalsasi. “Cloud computing—The business perspective.” The Decision Support Systems, vol. 51, no. 1, pp. 176-189, 2011. https://doi.org/10.1016/j.dss.2010.12.006. [5] R. Buyya, C.S. Yeo, S. Venugopal, J. Broberg, I. Brandic. “Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility.” The Future Generation Computer Systems, vol. 25, no. 6, pp. 599-616, 2009. https://doi.org/10.1016/j.future.2008.12.001. [6] N.R. Herbst, S. Kounev, R. Reussner. “Elasticity in Cloud Computing: What It Is, and What It Is Not.” The Proceedings of the 10th International Conference on Autonomic Computing, pp. 23-27, 2013. https://doi.org/10.1145/2462326.2462337. [7] AWS. “Netflix and AWS Lambda. Amazon Web Services.”. Internet: https://aws.amazon.com/solutions/case-studies/netflix-and-aws-lambda/ (accessed Feb. 25, 2025). [8] J.M. Stauffer, A. Megahed, C. Sriskandarajah. “Elasticity management for capacity planning in software as a service cloud computing.” The Iise Transactions, vol. 53, no. 4, pp. 407-424, 2021. https://doi.org/10.1080/24725854.2020.1810368 [9] Nucamp. “DevOps Case Studies: Lessons from the Industry.”. Internet: https://www.nucamp.co/blog/coding-bootcamp-back-end-with-python-and-sql-devops-case-studies- lessons-from-the-industry (accessed Feb. 12, 2025). [10] Harness. “United Airlines Accelerates Deployments by 75% With Harness.”. Internet: https://www.harness.io/case-studies/united-airlines-accelerates-deployments-harness (accessed Feb. 12, 2025). [11] Aokumo. “4X Developer Productivity (DevEx) with Cloud-native CI/CD.”. Internet: https://www.aokumo.io/case-studies/cloud-native-ci-cd-deployment (accessed Feb. 12, 2025). [12] “Rapid Development of Cloud-Based Product Line Meets Spike in Market Demand.”. Internet: https://us.nttdata.com/en/case-studies/rapid-development-of-cloud-based-product-line-meets-spike-in- market-demand (accessed Feb. 12, 2025). [13] “Accenture. Carlsberg brews a scalable cloud infrastructure.”. Internet: https://www.accenture.com/us- en/case-studies/cloud/carlsberg-cloud-innovation (accessed Feb. 12, 2025). [14] The Australian. “The Aussie Who Got 125-Year-Old Thomson Reuters Match Fit for the Artificial https://aws.amazon.com/solutions/case-studies/netflix-and-aws-lambda/ https://doi.org/10.1080/24725854.2020.1810368 https://www.nucamp.co/blog/coding-bootcamp-back-end-with-python-and-sql-devops-case-studies-lessons-from-the-industry https://www.nucamp.co/blog/coding-bootcamp-back-end-with-python-and-sql-devops-case-studies-lessons-from-the-industry https://www.accenture.com/us-en/case-studies/cloud/carlsberg-cloud-innovation https://www.accenture.com/us-en/case-studies/cloud/carlsberg-cloud-innovation American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 102, No 1, pp 371-381 381 Intelligence Boom.”. Internet: https://www.theaustralian.com.au/business/the-aussie-who-got- 125yearold-thomson-reuters-match-fit-for-the-artificial-intelligence-boom/news- story/603c59501ea507edb3d7c3f1405206d8 (accessed Feb. 12, 2025). [15] B. Gupta, P. Mittal, T. Mufti. “A review on Amazon Web Service (AWS), Microsoft Azure & Google Cloud Platform (GCP) services.”. In Proceedings of the 2nd International Conference on ICT for Digital, Smart, and Sustainable Development, pp 9, 2020. [16] The Australian. “Thomson Reuters global deployment using cloud.”. Internet: https://www.theaustralian.com.au (accessed Feb. 12, 2025). [17] S. Marston, Z. Li, S. Bandyopadhyay, J. Zhang, A. Ghalsasi. “Cloud computing—The business perspective.” The Decision Support Systems, vol. 51, no.1, pp. 176-189, 2011. https://doi.org/10.1016/j.dss.2010.12.006. [18] C. Ebert, M. Paasivaara. “Scaling agile.” The IEEE Software, vol. 34, no. 6, pp. 98-103, 2017. https://doi.org/10.1109/MS.2017.4121226. https://www.theaustralian.com.au/business/the-aussie-who-got-125yearold-thomson-reuters-match-fit-for-the-artificial-intelligence-boom/news-story/603c59501ea507edb3d7c3f1405206d8 https://www.theaustralian.com.au/business/the-aussie-who-got-125yearold-thomson-reuters-match-fit-for-the-artificial-intelligence-boom/news-story/603c59501ea507edb3d7c3f1405206d8 https://www.theaustralian.com.au/business/the-aussie-who-got-125yearold-thomson-reuters-match-fit-for-the-artificial-intelligence-boom/news-story/603c59501ea507edb3d7c3f1405206d8 https://www.theaustralian.com.au/