The benefits of analyzing vast amounts of data, long-term or in real-time, has captured the attention of businesses of all sizes. Big data analytics has moved beyond the rarified domain of government and university research environments equipped with supercomputers to include businesses of all kinds that are using modern high performance computing (HPC) solutions to get their analytics jobs done. Its big data meets HPC ― otherwise known as high performance data analytics.
Bigger, Faster, More Compute-intensive Data Analytics
Big data analytics has relied on HPC infrastructure for many years to handle data mining processes. Today, parallel processing solutions handle massive amounts of data and run powerful analytics software that uses artificial intelligence (AI) and machine learning (ML) for highly demanding jobs.
A report by Intersect360 Research found that “Traditionally, most HPC applications have been deterministic; given a set of inputs, the computer program performs calculations to determine an answer. Machine learning represents another type of applications that is experiential; the application makes predictions about new or current data based on patterns seen in the past.”
This shift to AI, ML, large data sets, and more compute-intensive analytical calculations has contributed to the growth of the global high performance data analytics market, which was valued at $48.28 billion in 2020 and is projected to grow to $187.57 billion in 2026, according to research by Mordor Intelligence. “Analytics and AI require immensely powerful processes across compute, networking and storage,” the report explained. “As a result, more companies are increasingly using HPC solutions for AI-enabled innovation and productivity.”
Benefits and ROI
Millions of businesses need to deploy advanced analytics at the speed of events. A subset of these organizations will require high performance data analytics solutions. Those HPC solutions and architectures will benefit from the integration of diverse datasets from on-premise to edge to cloud. The use of new sources of data from the Internet of Things to empower customer interactions and other departments will provide a further competitive advantage to many businesses. Simplified analytics platforms that are user-friendly resources open to every employee, customer, and partner will change the responsibilities and roles of countless professions.
How does a business calculate the return on investment (ROI) of high performance data analytics? It varies with different use cases.
For analytics used to help increase operational efficiency, key performance indicators (KPIs) contributing to ROI may include downtime, cost savings, time-to-market, and production volume. For sales and marketing, KPIs may include sales volume, average deal size, revenue by campaign, and churn rate. For analytics used to detect fraud, KPIs may include number of fraud attempts, chargebacks, and order approval rates. In a healthcare environment, analytics used to improve patient outcomes might include key performance indicators that track cost of care, emergency room wait times, hospital readmissions, and billing errors.
Customer Success Stories
Combining data analytics with HPC:
A technology firm applies AI, machine learning, and data analytics to client drug diversion data from acute, specialty, and long-term care facilities and delivers insights within five minutes of receiving new data while maintaining a HPC environment with 99.99% uptime to comply with service level agreements (SLAs).
A research university was able to tap into 2 petabytes of data across two HPC clusters with 13,080 cores to create a mathematical model to predict behavior during the COVID-19 pandemic.
A technology services provider is able to inspect 124 moving railcars ― a 120% reduction in inspection time ― and transmit results in eight minutes, based on processing and analyzing 1.31 terabytes of data per day.
A race car designer is able to process and analyze 100,000 data points per second per car ― one billion in a two-hour race ― that are used by digital twins running hundreds of different race scenarios to inform design modifications and racing strategy.
Scientists at a university research center are able to utilize hundreds of terabytes of data, processed at I/O speeds of 200 Gbps, to conduct cosmological research into the origins of the universe.
Data Scientists are Part of the Equation
High performance data analytics is gaining stature as more and more data is being collected. Beyond the data and HPC systems, it takes expertise to recognize and champion the value of this data. According to Datamation, “The rise of chief data officers and chief analytics officers is the clearest indication that analytics has moved from the backroom to the boardroom, and more and more often it’s data experts that are setting strategy.”
No wonder skilled data analysts continue to be among the most in-demand professionals in the world. The U.S. Bureau of Labor Statistics predicts that the field will be among the fastest-growing occupations for the next decade, with 11.5 million new jobs by 2026.
Data analytics is the key to unlocking the most value you can extract from data across your organization. To create a productive, cost-effective analytics strategy that gets results, you need high performance hardware that’s optimized to work with the software you use.
Modern data analytics spans a range of technologies, from dedicated analytics platforms and databases to deep learning and artificial intelligence (AI). Just starting out with analytics? Ready to evolve your analytics strategy or improve your data quality? There’s always room to grow, and Intel is ready to help. With a deep ecosystem of analytics technologies and partners, Intel accelerates the efforts of data scientists, analysts, and developers in every industry. Find out more about Intel advanced analytics.