What do you think both wildebeest and modern businesses have in common?
If your answer is migration, that’s correct.
The migration of modern businesses to the cloud is somewhat similar to the migration of herds of wildebeest, million-strong, to the Serengeti. On both occasions, the migration occurs for one specific reason: survival.
Modern businesses rely on big data to operate competitively. Migrating data analytics to cloud computing platforms has become a key enabler for that. According to one study, by 2022, we can expect cloud computing to be home to 90% of innovations when it comes to data analytics.
Businesses that don’t get in line are at risk of incurring unnecessary costs and operational inefficiencies compared to companies that have migrated to a virtual computing environment. Are you one of them?
While moving your data analytics to the cloud is a journey, the benefits outweigh the challenges—significantly. Here’s why.
The resource-heavy nature of your operations and sudden spikes in short-term resource needs are potential shortfalls you may be grappling with when it comes to on-site data analytics.
To tackle this, most organisations today are building powerful and expensive IT infrastructure. This, however, is not foolproof; it comes with heavy operational inefficiencies because resources become idle in normal use cases.
Additionally, you may not be able to process any data beyond the capabilities of your infrastructure, even if this is something that’s critical to your operations.
Cloud computing, on the other hand, excels when it comes to scalability with its pay-per-use model. You can scale your operations up, when and how you need it, without having to pay for idle infrastructure.
Traditional data analytics models require organisations to store their data on data racks full of expensive, enterprise-grade storage drives.
In fact, did you know that legacy implementations can account for up to 75% of operational and maintenance budgets?
Cloud computing eliminates the need for on-site hardware given that all the data is migrated to virtual servers on the cloud, dramatically reducing your costs. One study even found that adopting cloud platforms reduced business operational costs by 13%.
When it comes to physical infrastructure, there is always the risk of data loss due to disasters, theft or other acts of God. Because data is stored on onsite servers, data recovery after any kind of loss is also time-consuming and expensive.
Migrating all your data to the cloud reduces the risk of loss because data is stored outside your organisation. All your data is backed up, so even in a disaster or crisis situation, you can recover your data easily and get back online on a much faster timeline.
Conventional data analytics operations are bottlenecked by their hardware. Because it is slow and can’t handle large data sets quickly, it slows you and your team down.
Beyond losing out on quick-win efficiency gains, this also represents a more serious problem: your organisation won’t be able to identify trends on time and make the most of them.
On-site IT implementation also requires users to be connected to the network to access any data.
Cloud computing, on the other hand, makes it easier and quicker to access data from anywhere and on any device that is connected to the internet. No matter where you are or what you’re doing, you can access your data to make the right decisions for your business.
Moving advanced analytics platforms to the cloud will require you to modify how you do certain things, but today, this is the future of business.
If deployed correctly, cloud services will empower better, data-driven decision-making in your company and help you identify trends on time. You can adapt your operations with confidence, maximise your revenue and operate more competitively.
Embrace cloud data analytics if you’re interested in operating ahead of the market, instead of behind it.
It might seem counterintuitive to talk about optimising cloud storage. After all, the cloud was built to host large amounts of data, why spend time and effort optimising storage on the cloud? But as the capacity of big data expands, servers will be pushed to their limits, and this will compromise the efficiency of data collection and analysis. When cloud storage is not optimised, it hinders efficiency in data analytics.
Several clients have come to us, seeking advice on how to optimise their environment for SAS analytics. One particular firm in fintech struggled with its data collection and analysis because cloud storage was not properly optimised. According to their CIO, working on their data analytics pipeline was like “Trying to swim up a sludge-filled river,” because completing basic functions was much harder than it should have been.
Given the connection between data analysis and cloud storage, organisations need to find ways to optimise their data storage to get the best results. Optimised cloud storage allows for responsive and efficient transfer of data, making data analysis more efficient. This maximises the value of SAS analytics and reduces operating costs.
However, despite the obvious benefits, optimising cloud storage will not be easy, especially with SAS analytics. This is because SAS analytics works with several cloud databases, like AWS and Azure. To optimise storage, you need to be familiar with these different platforms. However, there are still some things that can be done to optimise storage, in general.
One of the most common methods for optimising cloud storage is minimising data duplication and replication. Data duplication has several steps in the process, like chunking and securing hash algorithms. But the advantages are significant because it essentially eliminates all duplicates from the dataset, making it easier to work with and providing high-quality data for SAS analytics platforms to process.
Autoscaling is one of the best practices for optimising cloud storage. When auto-scaling solutions are implemented, the cloud platform scales automatically to match the volume of the workload.
Autoscaling makes cloud storage more efficient because cloud resources can expand and contract dynamically to match demand, reducing the workload for SAS experts and technical users.
You can configure auto-scaling solutions on AWS and Azure, although it should be noted that theprocess for implementing auto-scaling will be different for both platforms.
At its core, optimising the cloud is about maintaining a balancing act between workload performance, costs and compliance. The goal is to balance workload against infrastructure in real-time to attain efficiency. The challenge for optimising the cloud for SAS analytics is that no single strategy is the same. However, there are some things you can do to optimise cloud storage.
A significant chunk of cloud optimisation is analysing patterns in the workload, including past use and operational costs, in a process called workload modelling. Current use is then compared against the recommended configurations that would deliver the ideal workload for the platform.
When cloud platforms grow, they become more complex, compromising transparency. When transparency is compromised, it becomes much harder to maintain an efficient cloud platform. So, it’s important to improve oversight and transparency across the board to make the sharing of data much easier. Furthermore, it improves efficiency in cloud storage methods.
It’s important to understand that optimising cloud storage is an ongoing process, so its best to invest in tools to make the process easier. Workflow automation can help significantly with the process because it helps SAS professionals identify any unused or partially idle resources and can either use these resources or shut them down.
As many of you know, optimising cloud storage comes with several benefits that could help your clients reduce costs and improve operational efficiency. However, this is easier said than done because SAS Analytics uses different cloud databases, like Azure and AWS, to get the job done. However, by using the right tools, optimising cloud storage becomes a more efficient process, which is crucial for organisations working with large volumes of data.