Predictive analytics can prove incredibly valuable for businesses in virtually every sector. These solutions enable superior decision-making and long-term strategizing, delivering a major competitive edge. For these efforts, big data is critical. Companies must discover insight from tremendous quantities of unstructured and semi-structured information.
To this end, high performance computing is critical. As TechTarget contributor Bill Claybrook recently highlighted, HPC tools, when combined with raw data, can yield sophisticated, useful predictions for organizations.
HPC and big data analytics
As Claybrook pointed out, HPC systems are specifically designed to work with large amounts of information, as well as to provide accurate models and simulations.
This makes HPC ideal for predictive analytics efforts, the writer explained. After all, big data analytics is essentially the application of advanced analytics techniques to unstructured data sets. HPC is essentially a more powerful version of this very same process.
Not that these technologies are identical. Writing for Midsize Insider, Jason Hannula noted there are a number of key, fundamental differences between HPC and the type of analytics performed in traditional big data environments. Ultimately, though, the technologies are similar enough to reasonably apply HPC to big data, and therefore glean predictive analytics as a result.
Adapting to HPC
To enjoy the benefits offered by HPC-based predictive analytics, though, firms must take a number of important steps.
For starters, Hannula pointed out that companies must acquire and implement enhanced memory storage. Such resources are needed in order to support HPC's greater pattern-processing capabilities, especially in an environment defined by large data volumes moving at high velocities.
The writer also noted that companies must invest in the appropriate human resources to fully utilize high performance data analytics. There are specific skill sets necessary to successfully implement and manage these computing solutions, and these skills are not frequently found in a company's existing IT department. In most cases, decision-makers will need to seek out and hire IT professionals with robust experience utilizing such technologies if they want to maximize the value of their predictive analytics efforts.
On a related note, Hannula argued that personnel from the business side of operations must also be heavily involved in the HPC solutions. Leaving these responsibilities entirely in the hands of the IT department will inevitably undermine potential value.
"[T]he transition to predictive analytics requires the continuous involvement of business experts to validate data relationships and early-phase results," Hannula wrote. HPDA may seem to be an IT-centric functionality, but the implementation is to serve a business need for predictive results, and the business area must be an active partner."
Finally, organizations interested in pursuing HPC for predictive analytics must ensure they have the right support tools in place. For example, firms need to invest in debuggers specific to HPC to minimize program complexity and ease the way for future technology migrations. Without such dedicated solutions, the HPC system becomes far more difficult to manage, which consequently limits the value of the predictive analytics produced.