Unlock Data’s Full Potential with a Mature Analytics Strategy

Over the past decade, businesses have harnessed the power of “big data” to unlock new possibilities and enhance their analytical capabilities. Today, those businesses must accelerate those capabilities by moving beyond experimentation with analytics toward mature investments and capabilities, or risk losing a competitive edge.

A mature data analytics strategy is critical to deriving the most value from data, but many organizations struggle to get it right. Despite the exponential growth in data collection, about 73% of enterprise data remains unused for analytics, according to Forrester. This means that just one-fourth of the data generated is effectively leveraged to gain valuable insights. Embracing modern technology, such as containerized storage capabilities, can help leaders obtain a strong handle on their data and derive actionable insights from it to truly drive business growth.

Legacy Analytics Architectures Are Obstructing Innovation

Today’s software applications need to handle millions of users across the globe on demand while running on multiple platforms and environments. They also need to provide high availability to enable businesses to innovate and respond to changing market conditions. Legacy platforms were designed prior to ubiquitous fast storage and network fabric, presenting more challenges than solutions for organizations looking to get ahead of the competition.

When I spoke to IT leaders who use legacy deployment models, the number-one complaint I heard is that it requires too much effort to support data at the indexer layer, which leads to reduced operational efficiencies. Hours, days and even weeks can be spent on software updates, patches and scaling hardware to support growth. This, in turn, affects optimization as at-scale teams are challenged to meet the needs of their growing organization.

Additionally, legacy architectures require multiple copies of data, which significantly increases compute and storage requirements. When you add storage in a distributed architecture, you add compute regardless of organizational needs, affecting overall utilization and the ability to control costs.

Lastly, with varying performance capabilities across different storage tiers, there is a risk of slower query response times or inconsistent search results. This can hinder the speed and accuracy of data analysis. A mature analytics strategy faces these challenges head-on to provide operational efficiency, accelerated innovation and reduced cost of doing business.

The Case for Containerizing Modern Analytics Loads

Managing modern data involves more than relying on cloud architecture capabilities alone. Containerization can seamlessly integrate into cloud infrastructure to support modern analytics workloads. Imagine the convenience of running an application in a virtual environment without the hefty resource requirements of a hypervisor. By encapsulating software into virtual self-contained units, that’s exactly what a container can do.

Containerized applications provide greater performance and can run reliably from one computing environment to another. More application instances allow for greater performance overall, and the portability of the storage method enables centralized image management, rapid deployment and elasticity for organizations to scale storage capacity based on demand.

Interestingly, containerized applications can help with CPU utilization as well. In testing, we found that containerized applications enabled up to 60% utilization, compared to only 17% from a bare metal application model. Pair containerization with a high-performance storage solution, and organizations can achieve more flexibility and quicker response as data volumes increase.

Kubernetes’ Role in Unlocking Agile Data Management

Container orchestration platforms like Kubernetes provide robust tools for managing and orchestrating containerized applications at scale. With Kubernetes, platform and DevOps teams can easily deploy and run thousands of applications in a containerized or VM format, on any infrastructure, and can operate with much lower operational costs.

But to fully derive the benefits of a powerful application platform like Kubernetes, users need an equally powerful data platform to complete the solution. The Portworx Data Platform offers advancements such as automated and declarative storage provisioning, volume management, high availability and data replication, data protection and backup, business continuity and disaster recovery, security and robust cost optimization and management. These capabilities enable organizations to efficiently manage and control their data storage across distributed cloud environments, ensuring data availability and agility.

When using Kubernetes for containerized storage, there are considerations to keep in mind to ensure an organization’s mature analytics strategy is optimized and agile. First, using Kubernetes operators can further enhance storage capabilities by automating and simplifying complex tasks.

It’s also crucial to set up high availability at both the data service layer and the storage layer because relying on a single instance in a Kubernetes environment can be risky. Lastly, understanding whether an organization’s data service can be scaled up or scaled out will allow IT teams to choose the best solution to add more capacity or compute power as needed.

Organizations with mature analytics investments are achieving bigger impacts on business outcomes across the board, from customer experience and strategy to product innovation. Through modern data management like container applications and Kubernetes, organizations can make greater use of their data for innovation and growth and, more to the point, increase sales.