Cloud adoption is a must for big data applications. As data volumes grow and workloads increase, on-premise solutions quickly become too expensive, slow and unscalable to justify. Even so, cloud data processing costs can – and often do – get out of hand without the right strategy.
Big data processes will only grow from here, so businesses must consider long-term cloud optimization strategies. Learning to save space, processing power and money today will ensure successful cloud operations tomorrow.
The Need for Cloud Cost Optimization
Many organizations already recognize the value of the cloud. Its cost-saving potential is well established at this point, with some companies saving $2 million annually by transitioning. However, not everyone achieves such impressive results.
While cloud data processing is undoubtedly more cost effective than on-prem alternatives, that does not necessarily mean it is cheap. As more businesses move more of their data and processes to the cloud, their monthly expenditures on these services skyrocket. In the enthusiasm to capitalize on the cloud’s potential, many organizations have overlooked optimizing these workloads.
Public clouds now host more than half of all enterprise workloads and some businesses spend upwards of $12 million annually on that space. Considering 30% of cloud spending does not produce tangible value, that leads to significant waste. If companies want to experience the cost-saving opportunities cloud computing offers, they must optimize these processes.
Cloud Data Processing Best Practices
Thankfully, there are several paths to more efficient cloud data processing. Businesses should start with these five optimization strategies to unlock the cloud’s potential.
1. Sort Data Into Tiers
Data tiering is one of the most essential steps towards cost-effective cloud adoption. This involves sorting data based on how often employees access it and the value it brings each time they do. Businesses can then allot varying resources to different tiers to balance accessibility, performance and costs.
According to the Pareto Principle, 80% of a company’s results come from just 20% of its factors. Consequently, the tiers containing a business’s most valuable 20% of data should receive the bulk of its cloud spend. Data tiering helps organizations identify that high-priority data and give it the appropriate resources accordingly.
Data storage solutions are not one size fits all. By storing lower-urgency tiers in lower-performance, more affordable storage solutions, businesses can spend more on their high-priority data without excessive overall costs. It all starts with recognizing which data sets require what level of access and performance.
2. Deduplicate and Compress Cloud Data
Another important step in optimizing cloud data processing is deduplicating the data in question. As much as 30% of all unstructured data is redundant, obsolete or trivial, leaving companies with much more data than they need. That surplus information leads to excessive storage costs.
Using an automated deduplication program lets organizations find and delete duplicate records. Consolidating similar files with complementary information yields similar results. Despite being a relatively straightforward fix, this step can significantly reduce the storage space a business needs.
After deduplicating data, it is a good idea to compress what is left. Like deduplication, compression is straightforward and easily automated but easy to overlook. While each compressed file may only be a few megabytes smaller, that adds up to substantial storage savings at scale.
3. Consolidate SaaS Programs
Similarly, organizations should review their SaaS apps to determine if there are any opportunities to consolidate them. The average business uses 130 different SaaS tools, but many may be unnecessary.
Using consolidated, multi-function SaaS platforms instead of multiple specialized options will reduce cloud software spending. A customer relationship management solution can likely replace individual email automation, marketing analytics and social media management tools. As the cloud market grows, these all-in-one options are becoming more common, offering more saving opportunities.
Where single tools are not possible, look for those with extensive compatibility with other apps. Platforms like digital whiteboards combine multiple devices to enable more seamless collaboration and higher efficiency. In addition to supporting other apps, digital whiteboards provide a single place to use them all. Some of these services can offer thousands of app options under a single cloud umbrella to eliminate slow changeovers and in-between services. As a result, teams save time and money, leaving more cloud capacity, budget space and processing power.
4. Embrace Data Archiving
Another way to reduce cloud data processing costs is to recognize data has a limited life span. Depending on the information, it may only be useful for a few months before it is outdated. Some files become unnecessary once teams switch to a new platform. Consequently, many companies use significant storage space and costs to store data they no longer need.
Archiving is the solution. The process begins with analyzing how often employees use different records and files. When data usage drops, question whether it is necessary anymore. If teams do not need it now but may need access in the future, archive it by sending it to the lowest-cost tier. If it is no longer of any use, delete it.
Outright deletion is not always possible or ideal. Regulations require organizations to hold scientific research data for at least three years, for example. In these cases, archiving this information in the cheapest possible storage solution helps meet regulations while minimizing storage costs.
5. Review Cloud Data Processing Practices Regularly
As data’s usefulness changes, so does the optimal storage and processing method. Businesses adjust their data collection and analysis workflows, new regulations emerge, and new technologies present novel savings opportunities. These changes require frequent review to ensure ongoing optimization.
At least once a year – ideally more for data-heavy organizations – companies should analyze their cloud data processing practices. Look back through records to see if spending has increased or if any teams have reported difficulty with some cloud systems. Any unwanted changes or factors falling below expectations deserve further analysis.
As teams uncover where their storage and processing do not meet their goals, they should consider how technology and best practices have evolved. Adopting this spirit of ongoing review and innovation will keep organizations at the forefront of cloud adoption.
Optimize Cloud Data Processing Today
With the right approach, cloud computing can offer substantial cost savings, and enable disruptive AI and big data solutions. Achieving those benefits starts with understanding where many companies fall short.
These five optimization techniques will help any business reduce its cloud storage space and costs. It can then make the most of their IT expenditures.
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