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By Allison Foster
It’s an age-old question: “How can I reduce costs without impacting performance?” Traditionally, data cost optimization has come at a cost itself – either in terms of a drop in performance, or a heavy internal lift (and dedication of time and other resources) to manage and reduce data costs effectively.
The challenge for many organizations is that optimizing data costs keeps being pushed down the priority list, as there is always a new release or feature request that’s due.
That’s why taking a systematic approach to data cost optimization is so critical; it formalizes the process and introduces best practices to ensure that maximum benefit is gained with minimum interruption.
We’ll look at such a process, including real-world examples of organizations optimizing their data costs effectively.
The key to understanding data cost optimization is having visibility into the component parts of what makes up your data costs. Data costs are generally driven by:
With these basics in place, it makes it easier to assess which areas can be made more efficient, and to prioritize your data cost optimization efforts.
A systematic approach to data cost optimization is key. Here is such an approach, informed by best practices:
Perform an audit to identify where and how data is being stored, processed, and shared.
What data needs to be retained? Based on this, along with questions such as for what purpose and for how long, will inform your now-streamlined data retention strategy.
This is one of the areas where the biggest impact can be found. Split data storage into data that’s seldom accessed (e.g. archival storage), versus frequently accessed data that needs to be stored in high-performance tiers.
It’s the transfer of data that often incurs the highest cost as a proportion of total data-related costs. It’s thus a prime target for optimizing data costs.
Optimize queries and data processing tasks to ensure you’re maximizing efficiencies. Consider third-party tools to further streamline query and processing workloads.
Dynamic scaling matches resources with demand, ensuring you always have the perfect amount of resources for your needs, and aren’t over-provisioning.
Many platforms or providers have inbuilt cost monitoring tools that help you keep on top of your data coast. Implement alerts when you’re approaching tier limits.
To implement cost-effective data architectures, you first need to deeply understand your workflows. You’re then ready to ensure that your data architecture supports your needs effectively. On the one hand you want to avoid bottlenecks and make sure you have the capacity to deal with growing data volumes; on the other hand, you don’t want to over-build or over-invest, as this is just wasteful spending.
Most organizations therefore choose a scalable cloud option coupled with data stored in tiers, where data that’s accessed more frequently is kept in higher-performance, higher-cost tires, while data accessed less frequently such as backups is archived in less expensive storage.
A managed service can maximize the cost effectiveness of your data architecture, taking care of everything from compression and data deduplication to retention and scaling.
The best way to gain an understanding of how your organization can optimize data costs is by seeing how others have done it effectively.
For example, many organizations today are implementing a solution like SQream to reduce data costs while actually increasing performance.
SQream’s GPU-based technology enables businesses to process and analyze massive datasets, avoiding the limitations, bottlenecks, and costs presented by traditional solutions. With its advanced data acceleration capabilities, organizations can access the ability to perform complex analytics on large-scale data while simultaneously significantly cutting costs. And enterprises can optimize performance by enabling faster queries and more efficient data preparation.
Recently, a leading electronics manufacturer used SQream’s technology to overcome hardware limitations, enabling them to perform advanced analytics they previously were not able to even contemplate.
SQream’s advanced GPU acceleration helped this organization analyze much larger datasets than ever before, and deliver insights at a fraction of the time and cost.
Similarly, businesses across industries such as telecom and e-commerce are leveraging SQream’s tools to gain deeper insights, streamline operations, and drive down total ownership costs without compromising on data-driven decision-making.
A: Common challenges in data cost optimization include identifying hidden costs, managing data growth, and balancing performance with cost-cutting measures.
A: Access to cloud computing provides increased flexibility, though it requires careful monitoring to avoid unexpected expenses – for example, being moved automatically up a tier.
A: Data governance ensures data is organized, retained, and used efficiently.
A: Data cost optimization strategies should ideally be reviewed continuously, however an in-depth review should be conducted at least quarterly.
SQream redefines data analytics with its powerful GPU-accelerated platform, empowering organizations to process and analyze massive datasets faster and more efficiently than ever before.
Unlike traditional systems, SQream leverages the powerful parallel processing capabilities of GPUs to deliver unmatched speed and scalability for even the most complex queries. Whether deployed in the cloud or on-premises, SQream enables organizations to surface deeper and more impactful insights, all while cutting data analytics costs by up to 90%.
From financial services to healthcare, retail, telecommunications, and beyond, SQream is transforming industries by addressing key challenges such as unsustainable data-related cost increases, long-running queries, data segmentation, and high infrastructure costs.
The platform dramatically shortens time-to-insight, optimizes operations, and drives innovation by integrating seamlessly into existing data stacks. To learn more about how SQream can dramatically optimize your data costs, get in touch with the team.
We looked at how to effectively optimize data costs by using a systematic approach, which is especially valuable given the pressures to maintain performance while concurrently managing data costs.
Organizations embracing this approach will turn data challenges into opportunities, gain access to deeper insights, and stay agile enough to outmaneuver competitors and forge long-term leadership positions in their industries.
