The rise of cloud computing has prompted a surge in cloud adoption among enterprises. These enterprises are seeking to leverage the immense benefits of the cloud, including lower operating costs, higher performance, and virtually limitless scalability. Trasers research shows, however, that only 7% of enterprises investing in digital transformation will meet business expectations.
This is primarily due to a fragmented approach to cloud adoption. As business departments identify and outline new IT requirements to stakeholders, transformative action is applied — but in isolation from the bigger IT picture. The result is often disparate systems and software between different business modalities, setting a trap that will complicate IT service and operations management (ITSM and ITOM) as the business scales.
With digital transformation enabled through cloud adoption, consistency is vital. Mismatched IT systems can reduce agility, despite the immediate benefits new technologies may offer. Instead, enterprises should look to apply cloud technologies across the business in one fell swoop, driving consistency in software and hardware through a comprehensive cloud adoption strategy.
Let us explore the why and how for successful cloud adoption strategy planning.
A successful cloud adoption strategy involves end-to-end planning. Every business requirement must ensure that the strategy caters to every business need. This will help businesses adapt their infrastructure services to accommodate current and future processes and workflows — rather than relaying the foundation each time business requirements change.
Thus, success in the planning stage requires considerable foresight. Decision makers need to be mindful of how future business growth could cause IT requirements to outgrow IT capabilities.
For enterprises that have yet to adopt cloud technologies, they likely use a number of monolithic applications. Monolithic applications are notoriously hard to scale, regardless of whether they run on-premises or in the cloud. This is because they can only scale in one dimension, mandating either a server hardware upgrade, or a clone of the application on a separate server to increase throughput. Both options will require significant investment.
Alternatively, the cloud is known for supporting modular, microservice compute applications. A microservice can be imagined as numerous small applications targeted to a single workflow or process, working together as an orchestrated group to deliver broader functionality.
When part of a microservice fails, the rest of the system can continue working in its absence, limiting loss of functionality during outages. This is achieved by decoupling the underlying foundational components of an application, reducing resource footprints, and increasing scalability and reliability.
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This poses a question during cloud adoption planning. Does the business retain its monolithic applications? Or does it seek out an alternative based on microservice architecture?
Relating back to foresight, microservices are designed to scale and grow with business requirements, making this a vital planning consideration for long-term cloud success.
A cloud adoption roadmap should detail the technologies you currently use, whether they are viable on the cloud, and consider any alternative technologies you plan to use when replacing non-viable software and systems. It should also detail the desired future-state of your IT capabilities, with analysis to determine long-term candidacy of proposed systems in the end-state of the adoption process.
Trianz applies roadmapping as part of the Digital Enterprise Evolution Model (DEEM™). The model is based on research around the technological capabilities of 5000+ companies across 18 different industries, illustrated through a digital maturity scale. By grading digital maturity with DEEM™, enterprises can identify shortcomings in their IT services and craft a plan to elevate their IT capabilities during cloud adoption.
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This roadmapping phase will assist with migration prioritization. Migrations are non-linear by nature, with co-dependent systems risking downtime if taken offline for migration. For example, migrating a database will put all data-dependent IT services at risk. Alternatively, by moving data-dependent IT services to the cloud and creating a temporary data pipeline to an on-premises database, enterprises can execute parts of their migration roadmap with minimal risk.
A roadmap functions as a prerequisite for prioritization in this scenario. Prioritization itself should entail analysis of the potential productivity benefits or cost-savings with differing orders of migration, weighted against risk to business operations. This entails what can you migrate now with minimal risk and effort for maximum benefit to postpone costly, low-reward migrations until later in your cloud adoption journey.
With the agile methodology, failure is a common occurrence. Agile cloud adoption combats this by failing fast, minimizing wasted time, and providing valuable insight to rectify problems during migration. To put agile methodology into action, the cloud adoption strategy must break down planning, implementation, and optimization into a set of defined processes. These processes will work in parallel, with problems being reported, rectified, and retried in real-time to expedite cloud adoption.
An agile development approach is greatly beneficial to learn and adopt as part of the cloud adoption process. There are two popular agile frameworks. Scrum involves iterative sprints over a fixed timeframe, improving development regularity and adherence to deadlines. Kanban involves real-time communication of team capacity with full transparency of work. It uses visualized storyboards to designate and distribute smaller chunks of work, highlighting potential bottlenecks with uncompleted items to sustain pace of development.
In addition, Kanban enables continuous delivery and deployment (CI/CD). This can increase the velocity of cloud adoption, such as with CI/CD pipelines. When migrating a database, pipelines can separate live data traffic from migratory data traffic. New data is written to the cloud, old data is retrieved in parallel from the on-premises database to facilitate querying during migration—with the end-state being all data in the cloud and decommission of the old database architecture. This process keeps the service live, minimizing disruption while enabling progression through the cloud adoption roadmap.
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