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Cut Down the Costs of Your Big Data Efforts Featured

Cut Down the Costs of Your Big Data Efforts ThisisEngineering RAEng

Big data has a massive potential to revolutionize businesses by offering business intelligence. However, big data is often incomplete, incorrect, unsegmented, or poorly categorized. This prevents companies from taking advantage of data from different sources to deliver good customer service and implement strategies. Amid the competitive clutter in the business world, having the right insight can propel your company to the top and give it a competitive advantage. Taking advantage of big data to improve decision-making is easier said than done. While big data analytics is a good thing, it sometimes becomes costly. This calls for approaches to eliminate costs associated with gathering, accessing, sorting and analyzing data. Finding the right ways to collect and process information enables streamlining production, sales and customer service. To ensure big data analytics does what is intended, the cost needs to be minimized as much as possible; here are ways to cut down the costs of your big data efforts.

Virtualize data

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.

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

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