Moving to the Cloud is a strategic choice many organisations make to reduce data costs, accelerate their time to market, mitigate risk, foster innovation and achieve scalability. Whilst its benefits are tenfold, some businesses are grappling with the increased complexity associated with migrating to the Cloud, a result of the rapid uptake in Cloud adoption. NetApp reports that 98% of businesses have been impacted by the increasing complexity of data across the Cloud or on-premise solutions.
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Put simply, Cloud architecture refers to the design and organization of the components and subcomponents needed for cloud computing, including front-end platforms, back-end platforms, cloud-based delivery, and a network. It serves as a blueprint for managing and deploying cloud services and resources efficiently, ensuring scalability, reliability, and security.
Define your data strategy and ensure personnel competencies
Working together as a team to develop an overall data and Cloud strategy is the most important element to achieving business objectives. Doing so ensures that there is a predetermined, specified level of competency set when employing staff members as a prerequisite, needed to assist or manage complex Cloud-based tasks and architectures.
Overall data management within complex Cloud architectures is of increased importance. By ensuring there is a strong yet adaptable strategy in place, data managers can work together easily to solve any number of issues in short periods of time – especially when these issues become critical to system access.
Furthermore, having a clearly defined data strategy helps an organization’s IT team understand the relevance of the data they’re processing. Data can become a mountain of information in a short period, but where it becomes gold is in its analysis, so, by having a disorganized system an organization misses out on important insights. A lack of understanding of the impact and usefulness of data signals a malnourished and underdeveloped strategy making it more difficult for team members to develop consistent approaches and reduce external threats.
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There are too, without a strong strategy, issues in matching data to modern data stacks. Using legacy data presets for data sequences requires a high level of oversight, conversion and often human intervention to ensure there is no data loss in the migration process. This takes time, and expert knowledge – something many organizations struggle to find, and can be costly.
Cloud-based services require constant and significant oversight, management, and organization of cybersecurity to run and operate safely and smoothly. While most of this is done via a third-party host, an enterprise must consider what part its IT team will play in this process, if any. Dedicated members of the cybersecurity and IT team can be employed to undertake primary Cloud management and act as a troubleshooter for remote employees.
Build toward best practice and automated governance
Rapid Cloud migration can cause Cloud complexity, and as a direct result, further issues fester as the business begins to expand. By understanding, and developing failure-management solutions early, IT teams can safeguard issues and account for any additional challenges down the line.
By doing so, organizations can ensure their costs are kept to a minimum when failure inevitably occurs.
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Migrating data exclusively to Cloud solutions opens an organization up to more sophisticated, frequent, and costly cyberattacks. Developing a best practice model for employees operating Cloud architectures reduces the number of risks incurred, generates process consistency, and helps to trace systemic or human errors. Many organizations might feel it is important to introduce a zero-trust model when undertaking Cloud management.
When systems don’t have adequate resilience and/or disaster recovery/business continuity solutions in place, a level of chaos can ensue. Google, one of the largest third-party Cloud providers, proved integral to many institutions during the COVID-19 pandemic and to this day, abrupt disruptions in system operations can leave many organizations unable to operate.
When Gmail, Google Drive, Docs, Meet, and Voice had a global outage for 6 hours in August 2020, organizations were left unable to operate for the remainder of the day. Outages and failures are rare, but focusing on backup plans, best practices, and automated governance can ensure a business provides consistency for employees and customers alike.
Continuous learning is critical to building, improving, and automating the future
Data is adaptive and can be in frequent states of change, whether that is by initiating new data sets, adding data categories, or accounting for automation, the team managing complex Cloud architectures should consider the importance of simplistic and future-proof strategies.
Cloud architectures become complex partly for the length of time they are employed. As an organization grows and develops across industries and managed services, business operations change, as does user base size, therefore, new services or higher storage plans must be employed for organizations to keep pace with this access demand increase. Legacy data sets and architectures must be archived correctly or adapted to fit into the predeveloped data strategy going forward.
Data, across the architecture can be scattered, of varying degrees of quality and security, meaning it can be costly to maintain. With the help of AI, automation can play a large role in addressing system complexities. By examining vulnerabilities of data, services, workloads, and platforms, businesses can use AI to employ better management and process tasks quickly and efficiently.
IT managers can also reduce the scope of manual effort needed for data input and repetitive tasks.
Scaling, storage, sizing, and configuration are tasks that can all be automated for IT managers, meaning risk is mitigated significantly. By doing so, IT teams can streamline workflows, and more accurately spot, diagnose, and treat any issues within critical Cloud architectures in-line with pre-set governance frameworks.
When manually undertaking Cloud architecture-based tasks, an IT manager will encounter errors. Due to their repetitive nature, monotonous tasks like managing performance, VM clusters, load balancing, deployment, and monitoring virtual networks, can all lead to increased human error, therefore automation-led strategies reduce overall human error and further relieve pressure on personnel workloads.
Unlocking potential
Responsible for hosting data, a third-party provider of the Cloud maintains the physicality of servers and ensures minimal data loss. With their sole purpose being data management, using Cloud services outsource the complexities of on-site server management to a dedicated and equipped team for enterprises.
Cloud usage among all companies is now high; 92% of medium-sized businesses, in one form or another, use Cloud architectures and 85% of larger corporations do as well – a number that directly correlates to the rise in remote working during and after the COVID-19 pandemic.
Businesses are faced with the challenge of legacy servers, databases and other platforms that continue to exist when workloads are moved to the Cloud. Whilst they are used less frequently, these endpoints must still be managed and decommissioned, which means that companies’ architectures will inevitably become more complex. It’s important to consider how to leverage technology to reduce the impact of this complexity to truly unlock the Cloud’s potential.
