Cloud-based analytics functions have become increasingly popular in organizations seeking data-driven insights. However, as demand for these functions grows and data volumes increase, so do cloud costs. According to IDC, the global data sphere is projected to skyrocket to 175 zettabytes by 2025 from 33 zettabytes in 2018. This growth requires high computational resources, processing power and more storage capacities, translating into higher cost of cloud services.
In addition, Gartner predicts that by 2024, cost overruns from public clouds will negatively impact on-premises budgets for up to 60% of infrastructure and operations leaders. These figures show obstacles companies encounter in balancing optimal analytics performance while managing rising cloud costs. With effective cost optimization strategies, they can reduce expenses while maintaining top-notch analytics capabilities.
Cloud Cost Optimization Methods for Businesses
Businesses can achieve substantial cost savings while ensuring the accuracy and speed of their analytics operations by implementing the following proven optimization methods:
Reduce Expenses by Controlling Compute Costs
Organizations need solutions that help them in controlling computing costs and reducing cloud expenses to optimize analytics operations without compromising performance. One impactful approach is optimizing runtime processing, auto-scaling and load-balancing to reduce compute costs.
Accurately aligning computation needs with the appropriate resources for each workload helps avoid over-provisioning and mitigates unnecessary expenses. Leveraging auto-scaling capabilities facilitates the dynamic allocation of resources in line with demand, ensuring efficient resource utilization and preventing high costs during peak periods.
Moreover, load balancing plays an essential role in driving down computing costs. Organizations maximize efficiency and reduce processing time by distributing workloads across multiple nodes or instances. This enables effective management of available resources, which prevents resource bottlenecks and leads to optimal cost savings through reduced processing expenses.
Create a Single Version of the Truth Across the Board
Managing multiple dashboards and measures in data analytics can be overwhelming and prohibitively expensive, since it takes significant time and resources to integrate and maintain various analytics platforms, increasing organization costs and inefficiencies.
However, there’s a solution: establishing a single source of truth for the organization. Creating a consolidated data view across the enterprise can decrease stress and financial burden while maintaining optimal business intelligence (BI) speed and performance. A single source of truth is the authoritative repository for all data an organization uses. Here are some ways it can benefit them:
- Centralized Data Management: Consolidating data from different sources into one repository eliminates data silos and redundancies, streamlines cloud cost management processes, improves quality metrics and minimizes storage costs.
- Consistent Data Definitions: Standardizing definitions allows uniform interpretations and promotes accurate decision-making based on reliable information, ensuring the entire workforce is on the same wavelength.
- Unified Data Access: With this approach, all knowledge workers have access to trustworthy information regardless of where it has come from or who created it. This fosters collaboration among team members while overseeing how redundant extractions are reduced or completely avoided.
Accelerate Time-To-Insights with Smart Aggregation
A common challenge associated with cloud data warehouses is slower query response times. This can lead to exponentially increased consumption, resulting in shockingly high cloud costs. With queries being charged by seconds, the cost implications become even more significant when dealing with billions of data passes. Employing smart aggregation techniques can help organizations improve analytics performance while keeping cloud costs at a minimum.
By precomputing frequently used metrics and aggregating data at the source, resource-intensive ad hoc queries can be minimized. Utilizing cutting-edge technologies such as distributed caching, in-memory processing and data indexing helps accelerate query response time drastically, enabling faster time-to-insights and reduced expenses.
Conclusion
Reducing cloud expenses while maintaining analytics performance is key to successful data-driven operations for any organization. They can achieve significant savings by implementing cloud cost optimization strategies such as auto-scaling, runtime processing and load balancing. Additionally, smart aggregation tactics help accelerate query response times leading to faster insights and reduced cloud costs. By adopting these methods, they can strike a balance between controlling expenses and maintaining optimal analytics performance levels hence maximizing their investment value and achieving business success.
