Data Analytics

Data analytics are crucial to faster, better decision-making for organizations in any vertical industry. Success depends on employees having fast, secure access to vast quantities of data from wherever they may be.

That’s exactly what OSS hot cloud storage provides— inexpensive, high-performance, exabyte scale cloud storage that drives effective data analytics applications.


Data Analytics

Data analytics are crucial to faster, better decision-making for organizations in any vertical industry. Success depends on employees having fast, secure access to vast quantities of data from wherever they may be.

That’s exactly what OSS hot cloud storage provides— inexpensive, high-performance, exabyte scale cloud storage that drives effective data analytics applications.

While we’re producing lots of data today, it’s nothing compared to what’s coming, because some of the greatest contributors to the data of the next 50 years will be from industries that are in their infancy today. For example, it’s projected that by 2035, vehicles in the U.S. alone will produce over 100 zettabytes of data yearly – 15 times as much data as exists globally today. By 2020 it’s estimated the world will produce 44 zettabytes (that’s a 44 followed by twenty-one zeros) of data yearly, and by 2025 the figure will be 180 zettabytes per year. That means the amount of data we produce is doubling every two years – and accelerating.

An organization’s ability to compete will increasingly depend on its ability to mine ever larger datasets, most of it unstructured, to gain valuable insights. Use cases are all around us: digital forensics in law enforcement; Big Data for academic research, such as by Internet2 members and CERN; medical researchers gaining new insights for drug development and patient treatments; insurance and financial companies conducting fraud detection. The list goes on and on.

Cloud object storage, with its extreme capacity and elasticity, is ideal for addressing Big Data storage requirements. The problem, however, is that cloud storage tiers can get expensive fast from providers such as AWS, while the glacial speeds of lower-cost cold storage are hardly conducive to agile decision-making.

OSS provides the solution. With pricing 80% less than players such as AWS and performance that’s faster than the competition, OSS hot cloud storage is the perfect fit for Big Data storage requirements. OSS is secure, infinitely scalable and highly reliable, delivering eleven nines (99.999999999%) of object durability.

Sample Industry Use Cases for Data Analytics

Healthcare and Life Sciences
Healthcare and life sciences organizations employ data analytics in many ways. A hospital may employ the technology to analyze data aimed at improving operations and patient outcomes, including reducing re-admittance, as well as to analyze images and find issues a human might miss. Life sciences organizations use analytics to mine mountains of data related to drug research, looking for indicators that lead to breakthroughs.

Scientific Research
Scientific teams around the world use tools that produce vast quantities of data. There’s the Large Synoptic Survey Telescope, which produces 15 Terabytes of data every night, super-sized particle accelerators, and even electron microscopy. CERN’s Large Hadron Collider has been generating data for nearly 10 years. Indeed, scientific research is a steady, prolonged process that requires poring through years’ worth of stored data to extract value – which is why keeping data for the long-term, at a price that doesn’t penalize your overall research budget, is so important.

Law Enforcement
The increasing use of bodycams along with video from surveillance cameras and even the general public is only adding to the vast amounts of data law enforcement agents have to analyze. But it’s working, as digital forensics and data analytics continue to shrink the window between crime and capture. It was video facial recognition, for example, that helped solve the Boston Marathon bombings.

Data Analytics

Fraud Detection
Effectively mining Big Data can help insurance companies detect cases of fraud – and save big money. The U.S. sees some $30 billion in fraudulent claims per year, with a significant portion from workers’ compensation and medical claims, according to the National Insurance Crime Bureau. Some health care facilities are simply fronts for perpetrators of medical fraud, including “medical mills” that perform fraudulent health care billing on behalf of organized criminal enterprises.

Data analytics help to combat the ongoing problem of fraudulent transactions. Insurance companies can use Big Data technologies to quickly analyze billions of billing and claim records to detect patterns that indicate potential fraud. Some, such as medical records, are subject to strict privacy laws, including HIPAA (Health Insurance Portability and Accountability Act). So, companies need ways to securely and cost-effectively store those billions of records.

With OSS hot cloud storage, all data is encrypted both at rest and in transit, providing the kind of protection insurers need. At a cost of one-fifth that of cloud competitors like AWS, OSS is also much more affordable alternative to premises-based storage. With speeds faster than the competition, OSS also helps make short work of finding fraudulent activity.

Medical Image Analysis
As the number of patients they treat grows, healthcare organizations are dealing with an increasing number of medical images. What’s more, as imaging tools improve in quality, the images they capture have higher resolution, which enables better diagnoses.

At the same time, use of medical imaging analysis software is on the upswing, with market forecasters predicting it will grow at a compound annual growth rate of 8% through 2022, to become a $3.88 billion market. Increasingly, the analysis software is integrated with the medical imaging tool, enabling faster processing.

While this is great news in terms of patient care, all these high-resolution images can put a strain on storage budgets. That’s especially true in healthcare, where providers may be required to keep the images indefinitely, while also meeting privacy demands of regulations including HIPAA.

With its single-tier, hot cloud storage approach, OSS provides a budget-friendly cloud storage solution that addresses these sorts of use cases. All customer data is always readily available, at prices 80% less than competing cloud storage solutions like AWS S3. What’s more, it’s highly secure, as all OSS data is encrypted at rest and in motion, enabling providers to comply with federal HIPAA and HITECH (Health Information Technology for Economic and Clinical Health Act) regulations. And it’s infinitely scalable, able to handle images at any resolution from as many patients as needed.

Academic Research: Internet2
The Internet2 community relies on big data and analytics to conduct fundamental research. Anchored by 319 U.S. institutions of higher education, it was formed in 1996 “to provide a collaborative environment where U.S. research and education organizations can solve common technology challenges and develop innovative solutions in support of their educational, research and community service missions.” The organization operates the nation’s largest and fastest coast-to-coast research and education network.

By analyzing vast datasets, researchers and scientists can improve our understanding of the universe, accelerate cures for diseases, and advance weather forecasting and climate modeling, to name a few areas of interest.

Unfortunately, many academic research organizations simply can’t afford to store and share massive data volumes using traditional on-site storage platforms or costly first-generation cloud storage services.

OSS empowers research organizations to overcome Big Data storage cost and scalability barriers. Specifically conceived to make storage a commoditized utility like electricity, OSS costs 80% less than competitors such as AWS, with no extra fees to retrieve data from storage or for API calls. With OSS, researchers no longer have to make difficult decisions about which data to collect and share, where to store it and how long to retain it. And with performance faster than the competition, it’s a great fit for the high-speed Internet2 network, as this white paper details.

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