In a recent paper commissioned by Intel, Forrester Consulting’s research reveals that, despite the growth of big data, advanced analytics, and artificial intelligence (AI), enterprises still struggle with data management.
Forrester’s study found that, when asked about their biggest challenges relating to data analytics, respondents indicated that two of the top five issues are related to data management.1 A third of respondents indicated that data security is a top challenge, and 23 percent said questionable data quality and integrity is one of their major challenges.
Data management issues are compounded by the fact that many enterprises don’t have the right infrastructure in place to deploy advanced analytics and AI workloads. Legacy infrastructure (compute, storage, and network) often lacks the capabilities to process today’s huge volumes of data.
Hybrid Cloud Can Help with Data Management Woes
With 53 percent of all enterprise applications being deployed in public or private clouds by the end of 20182, it is becoming clear that organizations recognize the benefits of a hybrid cloud environment. According to Forrester, improved security and compliance, and enhanced data management capabilities were among the top reasons for using hybrid cloud.
“To stay ahead of their digital competitors, companies seek hybrid cloud because a hybrid model can offer higher consistency, better security, and more agility than any one public or private cloud alone,” states Forrester.
Modern Hybrid Tools Unlock the Power of Analytics
Legacy infrastructures are not prepared to support the data integration, high performance computing (HPC), data warehousing, and storage requirements of today’s analytics workloads. By moving to next-generation hybrid cloud infrastructure, enterprises can implement better colocation and caching strategies, take advantage of cloud-based workload controllers and premium high-speed connections.
They are also able to access the latest business intelligence (BI) tools and choose providers that are physically close to their data centers to reduce latency.
Recommendations for Supporting Analytics Workloads Securely and Efficiently
Companies looking to launch analytics projects such as those below should evaluate both current and future infrastructure needs:
- Predictive and descriptive modeling
- Data mining
- Text analytics
- Machine learning
- Deep learning
- Simulation and modeling
- Experimental design
In fact, most companies quickly outgrow their existing infrastructure’s ability to provide the necessary power, speed, agility, and storage that these workloads require. This is why many companies are turning to public and private cloud solutions to gain access to the necessary infrastructure.
When considering infrastructure capabilities, it is important to look at hardware that underlies both on-premise and public clouds. Intel® Xeon® Scalable processors have been designed specifically to run compute-intensive analytics applications. They’re also energy- and cost-efficient. Other technologies from Intel, especially Intel® Optane™ Solid State Drives (SSDs) and Intel® Optane™ DC persistent memory can further boost analytics workload performance.
If researching the various options and choosing the right combination sounds daunting, Intel® Select Solutions offer pre-configured, workload-optimized data center solutions with predictable performance. These ready-to-deploy solutions make it easy to follow Forrester’s suggestions for reaping the advantages of hybrid cloud, including:
- Setting realistic requirements for storage and compute
- Modernizing your infrastructure to support next-gen digital transformations
- Reformulating your hybrid cloud strategy to support both continuous and spikey workloads
- Making digital makers more productive