Cloud and
Analytics
CLOUD
Industry Strategic Challenges
Migrating to the cloud entirely or in part helps businesses distribute their resources effectively, respond to user and customer demand quickly, and become more agile overall. It also helps cut costs and reduce total cost of ownership (TCO). In fact, 95 percent of the IT professionals who responded to the 2017 RightScale annual State of the Cloud Survey are now using cloud.
No single cloud model or set of services is perfect for every organization. It’s important to position workloads to take advantage of the varying benefits of public, hybrid, and private cloud solutions. Therefore, selecting the best cloud solution involves careful consideration of how each cloud implementation option would meet the needs of the overall organization and its specific workloads.
Some companies have chosen public clouds. These cloud services are maintained by a service provider off-premises and are readily scalable. Paying only for the services used allows an organization to use OPEX dollars and clearly track the costs to specific projects. But public cloud offerings, with multiple tenants using the same server resources, don’t meet the workload performance needs or security requirements for some industries.
Other companies find that private clouds are better suited to their needs. These are on-premises with more controlled access to data, but not resource-flexible enough for some companies. They also require CAPEX infrastructure investments, which can end up being underutilized. It’s challenging to wade through the complexities and find the right solution with the fewest tradeoffs.
Businesses have a third option that offers advantages from both public and private clouds, along with unique benefits that come from combining them. This solution is the hybrid cloud, which can deliver the best of both worlds. A hybrid cloud implementation lets the company decide where deploying workloads onto quick-scaling, fast-to-market systems makes sense versus accessing traditional and core business systems. This flexibility helps increase competitiveness and TCO.
Hybrid cloud is not merely a scenario where an organization uses a public cloud for some things and a private cloud for others, working separately in parallel, with data in silos. For maximum result, the two kinds of clouds must be integrated, with interoperable hardware, software, networking, storage, and services working seamlessly together. This architecture benefits from a layer of specialized software to orchestrate operations according to organizational IT policies and changing operational priorities.
Intel and its broad ecosystem of solution providers can help organizations get started by evaluating whether hybrid cloud is the best cloud strategy for their workloads (see “Steps to Setting an Optimal Cloud Strategy” section below for step-by-step guide) – and then implementing successful Intel® technology-based hybrid clouds, with security built in to every infrastructure component.
Intel Is dynamIc
The Move to Hybrid Cloud
• 70% of enterprise-based IT professionals are considering a hybrid cloud strategy in the next 24 months
• 85% of enterprises now adopt a multi-cloud strategy, while only 9% use a single public cloud and another 5% use a single private cloud
• 60% of IT decision makers have moved or are considering moving to a hybrid cloud platform
• 83% of IT leaders said they currently use a hybrid cloud or plan to do so in the future
Source: "Digital Industrial Transformation with the Internet of Things”, a 2017 European Study by PAC – © : CXP Group 2017
ANALYTICS
Intel® Xeon® Scalable processors empower advanced analytics for business transformation
In today’s data-dependent world, businesses must work smarter and faster to stand out from the competition, deliver new products, and achieve results. Regardless of industry, geography or size, now is the time to consider what you need from your data in order to succeed in this new business landscape, and what you must do to ensure these needs are met.
Businesses Demand Better Analytics
A 2017 Gartner survey of more than 2,500 CIOs found that spending on “business intelligence and analytics” was the top investment priority in all types of organizations. The analyst house has indicated that it expects advanced analytics will be a USD 76 billion business by 2021.
It’s clear that every business is becoming a data business. Yet the challenge is no longer about acquiring data or building infrastructure to contain it. Instead, it is about extracting insight fast enough to matter and provide return on investment (ROI).
As the notion of “time-to-insight” has become a critical metric for business leaders, data scientists and technology leaders are demanding far more from their analytics infrastructure. They need answers faster, with greater efficiency and more flexibility, all while ensuring the enterprise-wide security of mission-critical data. Being unable to meet these needs will quickly reduce a company’s ability to compete, leaving it open to disruption.
Source: "Future-Ready Analytics" - Intel Business Brief
Intel’s Continued Commitment to Enterprise Analytics
Enterprises who wish to deploy analytics or further their current analytics investment, demand increased performance and agility in the data center to support diverse analytic workloads. Technology must be scalable and versatile enough to keep up with demanding changes in these workloads, and with evolving business strategy.
The new Intel® Xeon® Scalable processors build upon the exceptional performance, efficiency and value of previous Intel® Xeon® processors which have been the workhorse of the enterprise for nearly two decades. These technology gains, coupled with the innovation of system and solution vendors, have enabled incredible economic advantage for enterprises pursuing analytics. For example, IT can replace four four-to-five year old servers with only one Intel Xeon Scalable processor based server, lowering four-year total cost of ownership (TCO) by up to 65 percent. This ultimately allows the business to invest more in its analytics strategy and support more advanced analytics use cases. Intel’s dedication to delivering unmatched enterprise-ready platforms represents our commitment to underpinning our customers’ business success.
Accelerate Business
Insight from Data
Enhance Security and Reliability
One of the major concerns in building analytics infrastructures is securing the data both in transit and stored across the enterprise. With micro-architecture improvements, Intel Xeon Scalable processors vastly increase the speed at which enterprises can encrypt their data with negligible impact on overall performance, allowing IT to maintain fast analytics while protecting information. Furthermore, Intel has taken all of the reliability, availability and serviceability (RAS) features of the Intel Xeon processor E7 family and enabled them on the new processor, including the expanded Intel® Run Sure Technology. This provides IT with additional peace of mind for the analytics infrastructure they rely upon.
MANUFACTURING
Digital Industrial Transformation with the Internet of Things
At the heart of the digital transformation of asset-intensive industries, such as manufacturing, is the leveraging of emerging technologies. These promise to streamline decade-old processes and operations, improve existing products and launch new ones, create new channels to the customers and develop new business models. As a result of this transformation, industrial companies should be able to increase their benefit to the value chain by becoming organizations that are more agile, lean and know more about their customers’ needs tailoring their products accordingly.
This study sets out to understand the existing appetite of European industrial companies for IoT solutions going beyond Industry 4.0 concepts that focus on the internal "production & logistics" silos to a more holistic and externally oriented IoT applications within an enterprise, These include applications enabling the development of connected products and new services. This paper also evaluates the major concerns standing in the way of faster IoT adoption.
Top drivers and
challenges
of IoT adoption
Industrial companies' major priority is improving operational efficiency however they are aware of other benefits that IoT can bring.
Source: "Digital Industrial Transformation with the Internet of Things”, a 2017 European Study by PAC – © : CXP Group 2017
IoT adoption in the European
industrial sector
IoT adoption is considerable and most of the companies are beyond the planning and evaluation stage, but there is a lack of large-scale initiatives.
Source: "Digital Industrial Transformation with the Internet of Things”, a 2017 European Study by PAC – © : CXP Group 2017
Industrial companies need a hand with the IoT
The fact that only about half of the companies are strongly involved with service providers on collaboration, reflects some underdeveloped capabilities of companies, such as analytics. To bear the fruit of the IoT on a large-scale enterprise level, more work and collaboration with third parties is needed.
Source: "Digital Industrial Transformation with the Internet of Things”, a 2017 European Study by PAC – © : CXP Group 2017
The key of the IoT is in the data, but capabilities are still underdeveloped
Although 60% of the companies have live IoT initiatives, they don't use the data as much as they should.
FINANCE
Stay Ahead of Fraud with Big Data Analytics
Fraud is on the rise - and no industry is immune. From healthcare to insurance to firms with highly valuable intellectual property, targeted companies are losing billions. In fact, it is estimated that the typical organization loses five percent of its annual revenue to fraud. Hit particularly hard are financial services, communications, technology, and entertainment.
In an effort to stem the losses, eCommerce merchants and financial institutions alone are expected to spend USD 9.2 billion on preventing fraud by 2020 - a 30 percent rise over current levels. But they are hampered by increasingly sophisticated fraud schemes and legacy detection systems that can’t keep up. A key challenge is that fraud perpetrators bury repetitive but small fraudulent transactions in seemingly benign business processes, making them hard to detect.
Companies need smart, reliable ways to monitor patterns of suspicious behavior that come with the transformation of online business, connectivity, cloud-based services, and mobile applications. Most legacy fraud detection tools are rule-based and are limited to finding known issues. The challenge is to find emerging, unknown fraud patterns that a company doesn’t know exist. By tapping metadata from numerous sources and data types, crucial contextual relationship patterns can predict and prevent illegal, fraudulent activity. Though most organizations have huge volumes of data at their disposal, few of them have effective methods to extract, transform, and load it to create actionable information to act quickly enough to mitigate risk. Until now.
By using machine learning and artificial intelligence, powered by high-performance computing, disparate data sources can be correlated into powerful real-time fraud detection insight. These advanced platforms can wrap around and complement existing fraud control systems to enhance theft detection and eliminate fraud rings faster and more effectively than legacy solutions alone.
A Rising Tide Across
All Industries
Business Drivers and Desired Outcomes
• Reduce fraud prevention costs by freeing expensive resources to investigate legitimate attacks rather than false positive dead ends.
• Mitigate risk exposure surfaces now and in the future by deploying a real-time fraud detection solution that will:
- Provide actionable metrics to gauge where losses are occurring
- Easily scale the analysis of big data to efficiently detect known, unknown, and emerging risks
- Eliminate false positives by drilling down into event information to quickly determine if a given anomaly is truly a risk
- Employ automation with discovery and alert monitoring to streamline remediation efforts
- Generate prioritized reports of new and emerging risks quickly and efficiently, even as requirements change
- Perform ad hoc analyses in real time to address new questions and issues
- Integrate real-time fraud findings into any incident management system or SIEM platform for advanced forensic automation capabilities
Digital Transformation and Business Innovation
New real-time fraud detection tools go beyond ineffective legacy systems and rule-based, predictive detection. Instead, they use a non-deterministic approach that monitors suspicious behaviors a company doesn’t even know exist. Using advanced artificial intelligence technologies, they quickly analyze massive amounts of heterogeneous data. Baselines of normal activity are created to identify anomalies and isolate, audit, and/or stop vulnerable business processes.
Enabling Transformation
With powerful software from industry-leading solution providers optimized for scalable, efficient hardware from Intel, businesses can use advanced techniques to find suspicious patterns and gain actionable insight into fraud sources and sophisticated attack methodologies. Companies can now perform complex analytics quickly, without overspending on large-scale systems and specialized programmers.
Solution Summary
Using big data with analytics software powered by Intel® technology, data from heterogeneous sources is processed, correlated, and transformed into actionable fraud prevention intelligence. Instead of relying on knowledge of known threats, the solution can process years of stored data to uncover hidden indicators of fraud, enhance existing processes, and speed investigations.
Source: "Digital Industrial Transformation with the Internet of Things”, a 2017 European Study by PAC – :copyright: CXP Group 2017
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HEALTHCARE
DATA ANALYTICS: Unlocking the Future of Healthcare
Evidence-driven healthcare and achieving quality metrics require robust data and an understanding of what that data means. As healthcare progresses into an era that emphasizes prevention and value, data science is becoming an indispensable part of any health system, bringing increased attention to EHRs and improving quality of care. Effective use of data analytics can also help save money and improve efficiency. With the adoption of analytics, organizations have reported improved cost containment, more appropriate levels of staffing, and greater productivity. The impact of analytics on cost, quality, and efficiency improves the patient experience. "It's not about what we can do, or what we need. It's about generating value for our patients," Marna Borgstrom, president and CEO of Yale New Haven Health System, told Health Leaders' finance editor Christopher Cheney regarding her organization's greatest motivator in adopting a strong analytics program. In this Insider Collection, we examine the world of healthcare analytics, looking at developing trends and asking what they mean for the future of healthcare. In seven original articles, a Q&A, and two case studies, healthcare leaders share their healthcare analytics–related experiences and concerns.
The future never
takes a sick day
At most hospitals, standalone devices capture such vital signs as pulse oximetry, weight, temperature, and blood pressure, but values from these devices must be reentered into the EHR manually, creating extra work or nurses and introducing the possibility of data entry errors.
Enter Cedars-Sinai, a nonprofit academic medical center in Los Angeles with 886 licensed beds, 2,100 physicians, 2,800 nurses, and thousands of other healthcare professionals and staff. At Cedars, device integration is already eliminating much manual work.
In a talk at the 2016 HIMSS conference, Jennifer Jackson, Cedars-Sinai director of clinical engineering and device integration, described the origins of the initiative, the outcomes of which she is preparing for publication in a peer-reviewed journal. She is also preparing a paper about previous work integrating IV pump data into the health system's EHR.
In the case of pump integration at Cedars-Sinai, the electronic medication administration record (EMAR), which lives inside the EHR, pushes the medication order information to the pump. The pump sends data back to the patient's EMAR. However, the EHR does not start and stop the IV pump itself; that is still done by a nurse, who reviews the settings prepopulated by "On both ends it saves us a lot of time, and it also now removes a high degree of potential error when it comes to data entry and the timing of that data entry," Jackson says. "We're now in the process of reviewing the data and seeing how significant the results are." the EHR before starting the pump.
"On both ends it saves us a lot of time, and it also now removes a high degree of potential error when it comes to data entry and the timing of that data entry," Jackson says. "We're now in the process of reviewing the data and seeing how significant the results are."
Cedars-Sinai first connected infusion pump systems to EHRs in June 2014, adding pulse oximetry integration in August 2016. Although she cannot yet disclose the outcomes of the infusion pump integration, Jackson says it involves significant avoidance of medication errors.
"Our approach to device integration is somewhat holistic," Jackson says. "We look very carefully at not just a movement of data points from one system to another system. In the case of pulse oximetry, a system that we just implemented, it wasn't enough for us just to export the SpO2 and pulse rate into the EHR. We also wanted to capture and be able to distribute the alarm settings. For us, that was a single project."
Another aspect of the integration involves patient-controlled analgesia (PCA) pumps. "We measured their pulse oximetry, just to make sure, to monitor the patient's respiration, so that we can intervene should that patient start to have an adverse reaction to the opioid in their PCA pump," she says.
A drawback and annoyance of standalone devices' alarm systems is that, often, the only person hearing that alarm initially may be the patient, if nurses are busy elsewhere. Alarms can be routed to a nurse's workstation or portable communication device to speed intervention, Jackson says. "The expectation that the nurse or someone will always be available to intervene the moment that device starts to alarm is a large expectation," she says. "Actually, we're asking too much of our care workers to always be waiting for the next alarm. And when we say device integration, it's not just looking at how to get the data points into the EHR; it's also looking at what kind of alarms are more actionable information. Do we need to get to those caregivers in a meaningful time?"
Therefore, at Cedars, data such as pulse oximetry integrates not only with the EHR, but also with Cedars' alarm management system. "We're often shocked at how few medical device manufacturers have actually thought through this kind of integration," Jackson says. Once these integrations were in place, "we know that we've been able to intervene and possibly save lives.” Jackson describes the device integration initiative as "my career. I've been very passionate about it." At Cedars, the initiative's executive champion has been Linda Burnes Bolton, RN, vice president of nursing and chief nursing officer. "She did a lot of the precursor work to identify device integration and some technologies that we actually leverage for the alarm notification process," Jackson says. "She and some of her fellow CNOs got together and said nursing care should be at the highest level that it should be in terms of quality of care in the era of EHRs and newer technologies that are impacting the workflow."
Over time, vendors themselves are filling in some of the device integration gaps. For instance, San Antonio, Texas–based AirStrip Technologies offers an enterprisewide interoperability solution, both vendor- and data source–agnostic, that incorporates devices such as fetal monitors into dashboards viewable on an iPad or similar device. AirStrip investors include Dignity Health, Hospital Corporation of America, St. Joseph Health, and the Gary and Mary West Health Investment Fund.
There is also an effort underway at the national policy level to help with device interoperability issues, including draft guidance from the Food and Drug Administration in January 2016, "Design Considerations and Premarket Submission Recommendations for Interoperable Medical Devices," intended to be a nonbinding set of guidelines regarding interoperability of medical devices with each other and with other health information systems. "There's a lot of different agencies that are looking at interoperability, and each agency has its own hammer, and the FDA's hammer is safety," says Matt Patterson, MD, president of AirStrip. The draft FDA guidance has broad industry support and may help extend the work of trailblazers such as Cedars-Sinai to all of healthcare.