Reports by IDC have pointed out that businesses and customers are fast adopting the cloud for quick and ubiquitous access to data. This has seen a surge in the use of lower storage capacity on endpoint devices, which are also becoming more intelligent and connected. With this craze, many companies have already adopted private, public and hybrid cloud environments to store data and compute. Sadly, they still struggle in areas like server and resource utilization. The use of rack-mounted servers and hardware independently ends up being a waste because only 10% to 20% of the capacity is used. This increases costs while remaining idle. The costs are associated with maintenance and monitoring.
The solution to this challenge is to use a billing approach like that used by Amazon. This pay-per-use model enables economies of scale since VMs can be easily added and removed. When adapted and adopted to budget resources in on-premise environments, this model will bring greater cost and resource efficiencies because hardware and VMs will be optimized.
Using hyperconverged infrastructure (HCI)
Virtualization offers many cost-saving options by eliminating OS-based redundancies when dealing with big data. However, there are limited options for storage virtualization in the market. While virtualization offers cost-saving options through hardware and operating system management-related complexities’ abstraction, there are limited options for managing multiple software storage-related complexities. Virtualization is missing a big data component with system management applications running on embedded SQL databases. Although SQL and Oracle databases have helped on many occasions, Apache Hadoop and NoSQL database systems have emerged as key solutions to cost-cutting. These two are resource-effective tools that help store, organize and manage large data volumes in the least expensive and easy manner. They bring cloud computing closer and take the complexity of managing enterprise cloud systems. Hyperconvergence of infrastructure creates flexible building blocks that replace legacy infrastructure.
Make processes and strategies in an organization data-first
The modern age is characterized by investment in big data to improve analytics for informed decision-making. The key to success in this area is determining your company’s response and executing the needed changes systematically and effectively. This can be achieved by preparing to adapt by being ready to undergo a crucial change in how data is applied. The structure can be changed based on technological or regulatory requirements. Proper recruitment and partnerships will also bring on board talent with the necessary skills and work with vendors who use data efficiently. Furthermore, iterative improvements can help identify data that can be used, allow processes to run their course, enhance data and make changes based on learnings.
Since data is a primary resource, businesses must build data assets to strengthen and improve their competitiveness. This will only succeed if a budget improves data readiness and strategy to ensure efficient data use in decision-making.