customer case study aws

- Shaoyi Li, Cloud Security Architecture Consultant PAX Technology Inc.

Manufacturing

Get Started

Companies of all sizes in all sectors and industries are using Amazon Web Services to transform their daily businesses. Contact our experts and embark on your Amazon Web Services cloud journey immediately.

Join us in-person to explore how generative AI can boost your business May 29 th -30 th | Shanghai Expo Center, Secure your free seat »

Ending Support for Internet Explorer

For enquiries call:

+1-469-442-0620

• Cloud Computing

AWS Case Studies: Services and Benefits in 2024

Home Blog Cloud Computing AWS Case Studies: Services and Benefits in 2024

With its extensive range of cloud services, Amazon Web Services (AWS) has completely changed the way businesses run. Organisations demonstrate how AWS has revolutionized their operations by enabling scalability, cost-efficiency, and innovation through many case studies. AWS's computing power, storage, database management, and artificial intelligence technologies have benefited businesses of all sizes, from startups to multinational corporations. These include improved security, agility, worldwide reach, and lower infrastructure costs. With Amazon AWS educate program it helps businesses in various industries to increase growth, enhance workflow, and maintain their competitiveness in today's ever-changing digital landscape. So, let's discuss the AWS cloud migration case study   and its importance in getting a better understanding of the topic in detail.

What are AWS Case Studies, and Why are They Important?

The   AWS case   studies comprehensively explain how companies or organizations have used Amazon Web Services (AWS) to solve problems, boost productivity, and accomplish objectives. These studies provide real-life scenarios of Amazon Web Services (AWS) in operation, showcasing the wide range of sectors and use cases in which AWS can be successfully implemented. They offer vital lessons and inspiration for anyone considering or already using AWS by providing insights into the tactics, solutions, and best practices businesses use the AWS Cloud Engineer program . The Amazon ec2 case study   is crucial since it provides S's capabilities, assisting prospective clients in comprehending the valuable advantages and showcasing AWS's dependability, scalability, and affordability in fostering corporate innovation and expansion.

What are the Services Provided by AWS, and What are its Use Cases?

The   case study on AWS in Cloud Computing provided and its use cases mentioned:

Elastic Compute Cloud (EC2) Use Cases

Amazon Elastic Compute Cloud (EC2) enables you to quickly spin up virtual computers with no initial expenditure and no need for a significant hardware investment. Use the AWS admin console or automation scripts to provision new servers for testing and production environments promptly and shut them down when not in use.

AWS EC2 use cases consist of:

• With options for load balancing and auto-scaling, create a fault-tolerant architecture.
• Select EC2 accelerated computing instances if you require a lot of processing power and GPU capability for deep learning and machine learning.

Relational Database Service (RDS) Use Cases

Since Amazon Relational Database Service (Amazon RDS) is a managed database service, it alleviates the stress associated with maintaining, administering, and other database-related responsibilities.

AWS RDS uses common cases, including:

• Without additional overhead or staff expenditures, a new database server can be deployed in minutes and significantly elevate dependability and uptime. It is the perfect fit for complex daily database requirements that are OLTP/transactional.
• RDS should be utilized with NoSQL databases like Amazon OpenSearch Service (for text and unstructured data) and DynamoDB (for low-latency/high-traffic use cases).

AWS Workspaces

AWS offers Amazon Workspaces, a fully managed, persistent desktop virtualization service, to help remote workers and give businesses access to virtual desktops within the cloud. With it, users can access the data, apps, and resources they require from any supported device, anywhere, at any time.

AWS workspaces use cases

• IT can set up and manage access fast. With the web filter, you can allow outgoing traffic from a Workspace to reach your chosen internal sites.
• Some companies can work without physical offices and rely solely on SaaS apps. Thus, there is no on-premises infrastructure. They use cloud-based desktops via AWS Workspaces and other services in these situations.

AWS Case Studies

Now, we'll be discussing different case studies of AWS, which are mentioned below: -

Case Study - 1: Modern Web Application Platform with AWS

American Public Media, the programming section of Minnesota Public Radio, is one of the world's biggest producers and distributors of public television. To host their podcast, streaming music, and news websites on AWS, they worked to develop a proof of concept.

After reviewing an outdated active-passive disaster recovery plan, MPR decided to upgrade to a cloud infrastructure to modernize its apps and methodology. This infrastructure would need to be adaptable to changes within the technology powering their apps, scalable to accommodate their audience growth, and resilient to support their disaster recovery strategy.

MPR and AWS determined that MPR News and the public podcast websites should be hosted on the new infrastructure to show off AWS as a feasible choice. Furthermore, AWS must host multiple administrative apps to demonstrate its private cloud capabilities. These applications would be an image manager, a schedule editor, and a configuration manager.

To do this, AWS helped MPR set up an EKS Kubernetes cluster . The apps would be able to grow automatically according to workload and traffic due to the cluster. AWS and MPR developed Elasticsearch at Elastic.co and a MySQL instance in RDS to hold application data.

Business Benefits

Considerable cost savings were made possible by the upgraded infrastructure. Fewer servers would need to be acquired for these vital applications due to the decrease in hardware requirements. Additionally, switching to AWS made switching from Akamai CDN to CloudFront simple. This action reduced MPR's yearly expenses by thousands.

Case Study - 2: Platform Modernisation to Deploy to AWS

Foodsby was able to proceed with its expansion goals after receiving a $6 million investment in 2017, but it still needed to modernize its mobile and web applications. For a faster time to launch to AWS, they improved and enhanced their web, iOS, and Android applications.

Sunsetting technology put this project on a surged timeline. Selecting the mobile application platform required serious analysis and expert advice to establish consensus across internal stakeholders.

Improving the creation of front-end and back-end web apps that separated them into microservices to enable AWS hosting, maximizing scalability. Strengthening recommended full Native for iOS and Android and quickly creating and implementing that solution.

Case Study - 3: Cloud Platform with Kubernetes

SPS Commerce hired AWS to assist them with developing a more secure cloud platform, expanding their cloud deployment choices through Kubernetes, and educating their engineers on these advanced technologies.

SPS serves over 90,000 retail, distribution, grocery, and e-commerce businesses. However, to maintain its growth, SPS needs to remove obstacles to deploying new applications on AWS and other cloud providers in the future. They wanted a partner to teach their internal development team DevOps principles and reveal them to Kubernetes best practices, even though they knew Kubernetes would help them achieve this.

To speed up new project cycle times, decrease ramp-up times, and improve the team's Kubernetes proficiency, it assisted with developing a multi-team, Kubernetes-based platform with a uniform development method. The standards for development and deployment and assisted them in establishing the deployment pipeline.

Most teams can plug, play, and get code up and running quickly due to the streamlined deployment interface. SPS Commerce benefits from Kubernetes' flexibility and can avoid vendor lock-in, which they require to switch cloud providers.

Case Study - 4: Using Unified Payment Solutions to Simplify Government Services

The customer, who had a portfolio of firms within its authority, needed to improve experience to overcome the difficulty of combining many payment methods into a single, unified solution.

Due to the customers' varied acquisitions, the payment system landscape became fragmented, making it more difficult for clients to make payments throughout a range of platforms as well as technologies. Providing a streamlined payment experience could have been improved by this lack of coherence and standardization.

It started developing a single, cloud-based payment system that complies with the customers' microservices-based reference design. CRUD services were created after the user interface for client administration was set at the beginning of the project.

With this, the customer can streamline operations and increase efficiency by providing a smooth payment experience.

The new system demonstrated a tremendous improvement over the old capability, demonstrating the ability to handle thousands of transactions per second.

Maintaining system consistency and facilitating scalability and maintenance were made more accessible by aligning with the reference architecture.

Case Study - 5: Accelerated Data Migration to AWS

They selected improvements to create   an   AWS cloud migration case study cloud platform to safely transfer their data from a managed service provider to AWS during the early phases of a worldwide pandemic.

Early in 2020, COVID-19 was discovered, and telemedicine services were used to lessen the strain on hospital infrastructure. The number of telehealth web queries increased dramatically overnight, from 5,000 to 40,000 per minute. Through improvement, Zipnosis was able to change direction and reduce the duration of its AWS migration plan from six to three months. The AWS architecture case study includes HIPAA, SOC2, and HITRUST certification requirements. They also wanted to move their historic database smoothly across several web-facing applications while adhering to service level agreements (SLAs), which limited downtime.

Using Terraform and Elastic Kubernetes Service, the AWS platform creates a modern, infrastructure-as-code, HIPAA-compliant, and HITRUST-certified environment. With the help of serverless components, tools were developed to roll out an Application Envelope, enabling the creation of a HIPAA-compliant environment that could be activated quickly.

Currently, Zipnosis has internal platform management. Now that there is more flexibility, scaling up and down is more affordable and accessible. Their services are more marketable to potential clients because of their scalable, secure, and efficient infrastructure. Their use of modern technologies, such as Kubernetes on Amazon EKS, simplifies hiring top people. Zipnosis is in an excellent position to move forward.

Case Study - 6: Transforming Healthcare Staffing

The customer's outdated application presented difficulties. It was based on the outdated DBROCKET platform and needed an intuitive user interface, testing tools, and extensibility. Modernizing the application was improving the job and giving the customer an improved, scalable, and maintainable solution.

Although the customer's old application was crucial for predicting hospital staffing needs, maintenance, and improvements were challenging due to its reliance on the obscure DBROCKET platform. Hospitals lost money on inefficient staff scheduling due to the application's lack of responsiveness and a mobile-friendly interface.

Choosing Spring Boot and Groovy for back-end development to offer better maintainability and extensibility throughout the improved migration of the application from DBROCKET to a new technology stack. Unit tests were used to increase the reliability and standard of the code.

Efficiency at Catalis increased dramatically when the advanced document redaction technology was put in place. They were able to process papers at a significantly higher rate because the automated procedure cut down the time and effort needed for manual redaction.

Catalis cut infrastructure costs by utilizing serverless architecture and cloud-based services. They saved a significant amount of money because they were no longer required to upgrade and maintain on-premises servers.

The top-notch Knowledgehut best Cloud Computing courses that meet different demands and skill levels are available at KnowledgeHut. Through comprehensive curriculum, hands-on exercises, and expert-led instruction, attendees may learn about and gain practical experience with cloud platforms, including AWS, Azure, Google Cloud, and more. Professionals who complete these courses will be efficient to succeed in the quickly developing sector of cloud computing.

Finally,   a   case study of   AWS retail case studies offers a range of features and advantages. These studies show how firms in various industries use AWS for innovation and growth, from scalability to cost efficiency. AWS offers a robust infrastructure and a range of technologies to satisfy changing business needs, whether related to improving customer experiences with cloud-based solutions or streamlining processes using AI and machine learning. These case studies provide substantial proof of AWS's influence on digital transformation and the success of organizations.

Frequently Asked Questions (FAQs)

From the case study of Amazon web services, companies can learn how other businesses use AWS services to solve real-world problems, increase productivity, cut expenses, and innovate. For those looking to optimize their cloud strategy and operations, these case studies provide insightful information, optimal methodologies, and purpose. 

You can obtain case studies on AWS through the AWS website, which has a special section with a large selection of case studies from different industries. In addition, AWS releases updated case studies regularly via various marketing platforms and on its blog.

The case study of Amazon web services, which offers specific instances of how AWS services have been successfully applied in various settings, can significantly assist in the decision-making process for IT initiatives. Project planning and strategy can be informed by the insights, best practices, and possible solutions these case studies provide.

Kingson Jebaraj

Kingson Jebaraj is a highly respected technology professional, recognized as both a Microsoft Most Valuable Professional (MVP) and an Alibaba Most Valuable Professional. With a wealth of experience in cloud computing, Kingson has collaborated with renowned companies like Microsoft, Reliance Telco, Novartis, Pacific Controls UAE, Alibaba Cloud, and G42 UAE. He specializes in architecting innovative solutions using emerging technologies, including cloud and edge computing, digital transformation, IoT, and programming languages like C, C++, Python, and NLP. 

Avail your free 1:1 mentorship session.

Something went wrong

Upcoming Cloud Computing Batches & Dates

Scalestack Unlocks GTM Productivity and Growth With AWS and MongoDB

CUSTOMER SINCE

Unlocking sales productivity and growth with ai.

Sales productivity startup Scalestack is driven by a lofty mission. The company — which was founded in 2021 by veteran startup entrepreneurs Elio Narciso and Alex Prioni — builds products that “make computers work for humans, so they can focus on what really matters,” according to its website.

Scalestack seeks to bring order to often chaotic go-to-market (GTM) data. Specifically, Scalestack helps customers manage both structured and unstructured data, from first-party CRM data to third-party data from sources like LinkedIn. The company’s AI-powered, all-in-one GTM data orchestration and activation platform helps a growing list of customers — including MongoDB’s own sales team — spend less time manually researching and organizing data, and more time executing.

To make its mission a reality, Scalestack turned to MongoDB and Amazon Web Services (AWS). With the industry’s most popular developer data platform and the world’s most comprehensive and broadly adopted cloud services provider, respectively, the choice of MongoDB Atlas on AWS was simple.

“Scalestack’s mission is to help organizations unlock sales productivity, and our relationship with MongoDB and AWS has been integral to achieving that,” said Narciso.

Big data, small team

Research by Salesforce backs up Scalestack’s mission . A survey of nearly 8,000 sales professionals showed that they “spend just 28% of their week actually selling, with the majority of their time consumed by other tasks,” like manually researching and prioritizing prospects, and data entry.

The issue, Narciso said, is that there is no tech to easily connect GTM data to customers’ Ideal Customer Profiles (ICP) and sales engines, hence sales professionals waste time reconciling data sets that don't talk to each other. To solve this, Scalestack developed an AI solution that would enable customers to aggregate and understand a variety of data from disparate sources, helping reps execute sales plays (repeatable, scenario-specific strategies to help sales reps succeed) with context.

Indeed companies — particularly enterprises — usually have three big GTM data “buckets,” said Narciso. The first is their customer relationship management (CRM) system, which contains lots of “first party” data about target companies and prospects. The second GTM data bucket is “third party data” from external sources, which is often purchased. Examples include Linkedin data, news data, and information from Crunchbase, Zoominfo, job postings and more. And the last bucket is “zero party” data that comes from marketing motions like lead generation forms, or when people share data as part of the process of registering to use a product. As a result, GTM teams spend a lot of their time organizing all of this data, sometimes even doing so manually.

Scalestack's AI platform brings the GTM data “buckets” together in an easy and intuitive automated “workflow,” suggests prioritized actions, and helps sales representatives execute their sales plays. It unlocks a new level of sales productivity and growth for customers, Narciso said.

AI powered by AWS and MongoDB

To make all of that magic happen, Scalestack needed a solution that would be easy to deploy and that would allow their developers to focus on the core components of their innovation.

Enter AWS and MongoDB Atlas Vector Search . By choosing AWS as its cloud provider, Scalestack has been able to keep its cloud costs low and its data usage elastic. And Scalestack uses MongoDB’s “flexible schema and powerful querying abilities to efficiently manage a diverse range of data types,” said Scalestack’s Alex Prioni.

AWS also provides the infrastructure for Scalestack’s Spotlight AI copilot — which helps sales representatives draft emails and scripts based on GTM data — ensuring scalability and reliability.

Meanwhile, Spotlight uses Atlas Vector Search’s retrieval-augmented generation (RAG) abilities to perform quick searches over large datasets using vector similarity. With Spotlight, users can have “conversational interactions” and ask the copilot complex questions about company data, leading to dynamic, informative responses, Prioni said.

Spotlight draws on a huge amount of disparate types of data—such as customer information, company facts, news, and job openings. By using Atlas Vector Search to find connections and patterns across all of those data types, Spotlight gives its users answers that are both relevant to and useful.

“By leveraging Atlas Vector Search, our AI copilot can understand the context of user queries more effectively, drawing upon a rich database of indexed information,” said Prioni. “This leads to more accurate and contextually relevant responses, a key factor in providing an engaging and helpful user experience.”

Increased productivity and revenue

Though Scalestack is still in its early days, the company has already seen impressive results thanks to its work with AWS and MongoDB.

“Working with MongoDB and AWS has notably improved Scalestack's efficiency, particularly by streamlining data management and enhancing query performance,” said Prioni. “MongoDB's real-time processing capabilities have also been crucial in providing up-to-date information across our services.”

And by using Scalestack’s AI, customers have seen drastically reduced research times and significant increases in productivity. Customers saw a 40% increase in rep productivity, and GTM teams saw a 53X ROI on average on the Scalestack platform, measured as delta revenues influenced by Scalestack.

“Our sellers get hundreds of sales leads coming in weekly from a large variety of sources like events, job postings, and via social networks. Leveraging AI, Scalestack has been key to helping us to easily aggregate, manage, and automate disparate GTM data sets in a matter of minutes and identify true leads,” said Meghan Gill, SVP of Sales Operations at MongoDB.

Going forward, Scalestack looks forward to leveraging the integration of Atlas Vector Search and Amazon Bedrock, which was announced during AWS re:Invent 2023 .

The planned integration between Atlas Vector Search and Amazon Bedrock will make it easier for developers to create applications on AWS that use generative AI to complete complex tasks for a wide range of use cases and deliver up-to-date responses based on proprietary data processed by MongoDB Atlas Vector Search.

Prioni noted that Scalestack will be using Amazon Bedrock starting in Q2 of 2024. The ability to customize Amazon Bedrock models will allow the company to fine-tune AI models to specific use cases, enhancing the Spotlight copilot’s relevance and performance, he said.

“We’re really excited about the integration between MongoDB Atlas Vector Search and Amazon Bedrock — this fully managed system will let our developers focus on innovating on behalf of customers. We look forward to working with both MongoDB and AWS to further the development of Scalestack’s AI-powered GTM orchestration and activation platform,” said Narciso.

If you’d like to learn more about how MongoDB helps startups build faster and scale further, check out MongoDB for Startups. And for more information about how AWS and MongoDB work together to help organizations accelerate the development of modern applications, see the AWS MongoDB Partner page .

Elio Narciso, co-founder and CEO at Scalestack.

Alex Prioni, co-founder at Scalestack.

What will your story be?

Mongodb will help you find the best solution..

Detailed business case

In this stage, we recommend validating and expanding the scope of the business case to provide a greater level of detail to support the transformation program. The quickly assembled initial directional business case is designed to provide enough confidence to invest in the foundational steps and next level of detailed planning.

Developing a detailed business case supports this planning process in the following ways:

Providing financial analyses that inform decisions on what should be migrated and modernized, which options to select and how to phase and prioritize the work

Validating, refining, and developing the original directional financial case by re-examining in detail:

The infrastructure cost-reduction potential

The internal IT productivity and any outsourced operations efficiencies

The estimates for the investments needed for program setup, migration, and modernization

Identifying, estimating the scale of, and setting up the process for tracking the further value drivers that migration brings

In the detailed business case, you establish the following:

The objective basis on which to secure the mandate and investment to implement at least the first phase of migration

The baseline minimum financial performance expectation for the program

Clarity over the financial basis on which various migration design and prioritization decisions are made, so that when circumstances and people change over the course of the program, the new leadership can make informed choices.

Insight into incremental areas of cost optimization to be explored after initial usage data becomes available as workloads are migrated and start operation

Estimates for the value that cloud transformation brings to the business from increased resilience and agility

The associated KPIs, metrics, and assumptions used to estimate the financial return from improved resilience and agility, which then form the baseline for driving the primary benefits realization out of the program

Determine the scenarios needed for the case

When building the detailed business case, it is usually necessary to develop multiple scenarios to support the various purposes that the business case is used for.

Minimum change scenario – To assess the minimum financial performance expectation, prepare a scenario that assumes the minimum expected change to the status quo. This scenario, as a worst-case scenario, is useful support when getting the mandate to invest in the migration. This scenario models the minimum expected degree of capacity growth and minimum changes for other quality-of-service needs, such as availability and resilience. The least change creates the lowest cost and least resource inefficiencies for the current operating model.

Most likely scenario – To inform program strategy and prioritization decisions, prepare the scenario that reflects what the business expects to happen. This scenario should include the likely peak utilization growth or reduction and the upgrade costs to meet demand for high levels of service quality (especially availability and resilience) from the business.

Other specific scenarios – Where it is still necessary to make an assumption that could have a large impact on the business case, develop scenarios for both where the assumption holds true and where it does not. However, we recommend keeping the number of these alternative scenarios to the absolute minimum. Creating any more than three to four scenarios in total slows progress, and becomes expensive, confusing and difficult to maintain. Wherever possible, conduct experiments and work to remove larger assumptions.

Validate and refine the infrastructure and migration cost model

After you have completed the portfolio analysis and prepared the design and sizing of the target AWS services, refine the running cost estimates for the current operating model (COM) and future operating model (FOM) on AWS for each scenario. It is usually necessary to refine the estimates for the following:

COM infrastructure costs of hypervisor host server, bare-metal server, storage, network device, security appliance hardware refreshes, installation, and maintenance. Calculate these with actual pricing and discount levels for the capacity needed for the scenario.

COM data center and collocated facilities costs, including space, cooling, power, racks, uninterruptible power supply (UPS), cabling, physical security systems, sized for the growth and specified to meet the capacity, and high availability and disaster recovery (DR) levels for the scenario.

COM network services costs, including costs for WAN links, content delivery networks, and virtual private networks (VPNs), calculated using contracted pricing for the connectivity, bandwidth, throughput, and latency needs for the scenario.

COM application and infrastructure software costs based on existing contracts to provide the growth or reduction of usage for the scenario.

FOM AWS utility costs, including tech support and managed services as needed, based on the refined service architecture, instance sizes, preferred pricing model, expected usage, and usage volatility.

FOM application licensing based on final application design, the configuration of the infrastructure running the applications, growth over time, and license transferability rules.

FOM migration and modernization cost estimates , refined to reflect the baseline migration wave plan for the scenario, and detailed to provide costs for each workload, especially for those to be replatformed, repurchased, or refactored.

FOM decommissioning costs , including estimates of asset write-off and contract early termination costs, revised to reflect decommissioning timing in the baseline migration wave plan, verification of what assets can be repurposed and what assets can be switched around to minimize write-offs, and the cost of disposal of the physical assets and media.

Migration parallel run costs refined to reflect the timing of each migration cutover and each existing service decommissioning.

Refine the IT productivity and IT operations and support efficiency value model

As with the directional business case, there are two primary approaches to refining and developing the value model around IT operations and support. The approach that you choose depends on whether the COM is managed in-house or with contractors or outsourced services:

Internal team productivity improvement

Where IT operations and support are managed in house, the focus of the business case is on the following:

Identifying and quantifying the productivity gains from migration and any operational automation that is included in scope

Validating that the time freed up for the in-house team can be readily and productively applied to other typically higher-value activities, giving opportunities for progression and greater reward to the team and more value to the organization

Assess how much time each member in each role within the team spends on their various regular activities, and guidance on the expected reduction in workload for different activities.

The following table provides initial guidance for the typical levels of workload reduction by activity for those tasks that consume the bulk of IT operations and support effort across the different roles in the team. The table includes a description of how the productivity is achieved.

Note: The activities listed are typically performed by team members in several different roles, so the productivity saving for each task should be assessed across the full set of roles in the team. For example, in IT operations teams organized by infrastructure tower (such as compute, storage, and networking), capital expenditure planning and budgeting might be common to tower leads for each tower.

The following table shows the expected savings for each level of workload reduction.

These metrics provide a starting point for assessing productivity gains and including them in the detailed business case. Actual productivity gains vary based on the specific situation. It can be useful to calculate the productivity savings at both the midpoint and lower end of the ranges to estimate typical and conservative scenarios.

As the program progresses, it is valuable to capture actual data for time spent on each activity by role. That data builds an improved base for estimating operations and supports costs for new projects and expansions of services.

Outsourced IT operations and support cost reduction

Where IT operations and support are primarily outsourced or managed with contractors, the cost allocation for the future operating model (FOM) can be prepared by requesting quotations from AWS Partners that offer managed service solutions, including AWS Partner-led AWS Managed Services (AMS). You can also contact your AWS account manager and request a price for AMS directly, as described in the subsection on Building in operational cost optimization within the Creating a directional business case section.

For the detailed business case, replace any benchmark figure with a quotation based on the revised AWS services bill of materials and expected service consumption, the AMS package and any options needed, and the service level needed. The cost will have a one-time implementation component and a consumption-based run rate.

Include any remaining IT operations, support that must be retained for any service that will not be migrated to AWS, and a one-time cost if there are any contract penalties (for example, for early termination).

Develop the resilience value model

On AWS, you can construct a wide range of high availability, disaster recovery, and fault-tolerant architectures. Consumption-based pricing means that services are charged for only when used. Together, these two factors provide exceptional cost performance for resilience.

Furthermore, AWS ccustomers have been using this to improve their workloads' resilience. The IDC 2018 survey gives examples of participating customers achieving 73 percent fewer outages per year, a 58 percent reduction in mean time to recover (MTTR) and a 94 percent reduction in lost productivity. The same survey showed that the financial benefits derived through increased resilience were 50 percent greater than the IT infrastructure cost reduction benefit.

In addition, further resilience is achieved through modernizing the software development lifecycle for applications. Where CI/CD pipelines with test automation are introduced to support greater business agility, software defects are caught earlier in the development cycle, greatly reducing software maintenance costs.

To assess and include this value in the business case, first work with application business owners to build a picture of the total benefit opportunity for each workload to be migrated . This might include as the following items:

The number, average duration, and nature of interruptions in service:

Examples of service interruptions include outages, performance slow-downs, planned batch and maintenance windows overrunning, bugs in key functions, and access throttling during peak periods.

Impact on revenue by interruptions of revenue-generating services, such as ecommerce systems:

The likely number of transactions unable to be completed through service interruptions, based on the interruption time and transaction rates

Average value for each transaction impacted

The additional cost of support engineers' time to resolve defects in production systems compared to the cost of discovering them earlier in the development process

Impact on internal users' productivity and the cost of lost time

Then make an assessment of an expected and a more conservative reduction in time lost to service interruptions that the increased resilience should yield. For example, consider including the following items:

Reduced number of outages and MTTR using high availability architectures and improved recovery time objective (RTO) and recovery point objective (RPO)

Reduction in slow-downs, elimination of capacity throttling and avoidance in batch processing overruns, using capabilities such as automatic scaling

Reduced number of application bugs that are discovered only in production, through the implementation of CI/CD pipelines and automated regression testing on infrastructure spun up and spun down to minimize cost

Put these together for the portfolio of applications to be migrated and modernized, and calculate the expected and more conservative business value figures for each year of the case. The benefits should ramp up in line with the migration schedule and then scale in volume in line with the usage growth expectations of the contributing applications.

Develop the business agility value model

Business agility is the prime reason that AWS customers migrate to AWS. The IDC 2018 Survey of AWS customers indicated that for them, business agility benefits accounted for 47 percent of the total benefits measured and over five times the benefits accruing from infrastructure cost reduction.

Accurately predicting all the business agility benefits that will accrue from any transformation is challenging. However, by focusing on applications that support large numbers of users or are sources of business differentiation, you can model and include a material portion of this benefit into the baseline detailed business case.

As the migration proceeds, incrementally refine and expand the business agility value model as more benefits become quantifiable. This keeps the business case relevant, so that it can be used as the primary decision support tool with which to steer the program.

To build the business agility value model, use the following guidance:

Select the workloads that have the opportunity to drive the greatest business performance improvement, such as:

Revenue-generating workloads

Business operations workloads with scope for driving efficiency gains and removing costs from the business

Business productivity tools supporting large user bases

For revenue and efficiency generating workloads, do the following:

Make a realistic and a more conservative assessment of the revenue growth or operational efficiencies that major and minor application upgrades could be expected to drive.

Estimate the increased number of major and minor releases per year that AWS increased application development speed and reduced infrastructure deployment time enables. Some baseline metrics for this are provided in the IDC report.

Calculate the realistic and more conservative benefit expectations. Map them over the period of the business case, making allowances for ramping up to full efficiency some time after the respective workloads are migrated.

For business productivity tools, do the following:

Make a realistic and a more conservative assessment of the time savings that major and a minor application upgrades could be expected to drive.

Estimate the average cost of people's time and effort across the impacted user base.

Use the figures for increased major and minor release frequency, and calculate the benefits over the term of the business case.

Because the increased developer productivity and reduced time to launch require no additional resources, add the net benefit lines for each workload into the business case cash flow model for inclusion in the discounted cashflow, NPV, ROI, MIRR, and payback calculations.

To use the Amazon Web Services Documentation, Javascript must be enabled. Please refer to your browser's Help pages for instructions.

Thanks for letting us know we're doing a good job!

If you've got a moment, please tell us what we did right so we can do more of it.

Thanks for letting us know this page needs work. We're sorry we let you down.

If you've got a moment, please tell us how we can make the documentation better.

Real-world hybrid cloud use cases with Amazon Web Services (AWS)

Thank you for your interest. The {{resourceType}} is now [[ready for download]]

Many of today’s hybrid and cloud-based applications require secure, low latency private connections to cloud infrastructure. Equinix and Amazon Web Services (AWS), partners in world class private cloud connectivity, can help you quickly and efficiently connect hybrid workloads directly to the AWS Cloud with Equinix Fabric capabilities and AWS Direct Connect.

To find out more about the interconnected approach, download the eBook to see how industries are utilizing AWS Direct Connect and Equinix Fabric to harness the power of the hybrid cloud.

Explore case studies from: 

• Healthcare – Global healthcare provider improves patient health via AWS outposts on Platform Equinix.
• Online Gaming – Online gaming company reaches global customers via AWS Outposts on Platform Equinix.
• Financial Services – Financial services firm maintains data protection with AWS on Platform Equinix.
• Insurance Management – Global insurance management department scales high performance computing via AWS on Platform Equinix.
• Real Estate - Online real estate leader simplifies database management with AWS on Platform Equinix.
• Cloud Service Providers
• Financial services
• Digital Transformation
• AWS Direct Connect
• Colocation at Equinix
• Networking solutions