data organization

When it comes to making decisions that positively impact care delivery and business outcomes, great leaders will tell you it’s better to rely on data than on myth. Through healthcare analytics, the clinical and financial leadership at Regions Hospital in Saint Paul, Minnesota used data to do just that—and set a strong course for reliable, trusted decision-making that helps address their most pressing issues. Using strong IT systems, accompanied by a cooperative and inquisitive organizational culture that brings together clinical and financial decision makers together to address pressing issues, put Regions on the path to create powerful healthcare analytics that fuel organizational change.

The need for analytic tools to make sense of disparate data sources will certainly be expanding in the upcoming years. This report highlights what analytical data healthcare leaders are currently focusing on, as well as the challenges they expect to face when using analytics to support their organizations in the future.

Even as the move to electronic health records (EHR) progresses in earnest, there are a myriad of challenges involving legacy data systems. Chief among these challenges is the cost of maintaining obsolete systems solely for the patient information they contain. When up to 70% of a typical IT budget is spent on maintaining the current IT infrastructure and application portfolio, organizations have little left to invest in much-needed innovation. According to a recent HealthLeaders Media Survey, many organizations are still adjusting after their migration to a new EHR system. Hospitals need to get a better grasp on all forms and sources of data that they have—and the data they don’t yet have—so that the right information can be delivered to the right individual, and in the right context, at the point of care.

Creating a state-of-the-art clinical documentation improvement (CDI) program isn’t just about boosting coding accuracy. It’s a key strategy in managing the transition from volume-based to value-based care, say healthcare leaders. That transition is a risky endeavor that is putting hospital and physician financial performance to the test. As hospitals participate in new care and business models aimed at improving value, leaders must ensure that their organizations are able to maintain reimbursement levels, effectively treat the chronically ill—especially in outpatient settings—and gather accurate data that will allow them to assess performance and segment their varying populations. While some organizations often believe they are leaving revenue on the table because of documentation and coding issues, CDI offers numerous opportunities for improving financial performance, finds a recent HealthLeaders Media survey of 149 healthcare executives at provider organizations.

Electronic health record (EHR) system implementation is one of the largest IT investments most healthcare systems have ever made but it’s success is largely dependent upon the data which feeds it. One the main data sources for the EHR is the item master, which drives not only supply chain processes but also a broad range of clinical and financial functions. Only with a clean, accurate and complete item master can a healthcare organization trust the outputs generated from its EHRs – from evaluating the clinical effectiveness of products to securing reimbursements. Learn how to execute a master data management strategy to derive the greatest value from your EHR investment.

HealthLeaders' survey on workforce management queried leaders from a cross-section of U.S. healthcare organizations, including hospitals, health systems, physician organizations, and long-term care/skilled nursing facilities. The 150 respondents represent executives across all disciplines — administration, clinical, operations, finance, marketing, and information. In the next three to five years, hospitals, health systems, and other patient service providers expect to augment their time-and-attendance and payroll systems with integrated applications that enable more sophisticated data crunching around labor analytics, acuity management, and staffing assignments. The goal? To convert the workforce from overhead to asset — a flexible, agile asset that will help organizations succeed in an increasingly demanding regulatory and competitive environment.

Data is one of your enterprise's most valuable assets and the key to accelerating growth and improvement. Yet most organizations keep data in silos, stunting its value. Are you giving it the enterprise-level attention it needs?

Building a data-driven organization takes more than technology. And, healthcare leaders can all agree that issues facing today’s industry are daunting; determining how to react in a way that matches your organization’s strengths and vulnerabilities may be even more challenging. With the right resources in place, you can take pragmatic steps to reduce data silos and deliver efficient, coordinated care across all your settings.

Learn about the HPE Intelligent Data Platform and the new IT realities it addresses. With digital transformation underway in many organizations, more dynamic business models are becoming the key to success. This means infrastructure modernization and the introduction of technologies such as solid state storage, artificial intelligence and machine learning, software-defined infrastructure, and the cloud. At the same time, it means IT infrastructure management becomes much more complex. Enter HPE’s Intelligent Data Platform. With comprehensive coverage and AI/ML-driven real-time optimization that enables intelligent management of the entire data life cycle, the HPE Intelligent Data Platform enables an organization to get the most out of its IT resources while also meeting its evolving needs over time.

In years past, device functionality was enough to sell most embedded products without much concern for cybersecurity. Of course there were exceptions, such as in critical infrastructure, aviation, and military, for which security was always of importance. But today’s environment has evolved on several fronts. First, organizations across nearly all markets are demanding Internet connectivity to monitor and control devices as well as to aggregate and analyze data. Second, the magnitude of security threats has exploded, driven by highly sophisticated hackers including organized criminal gangs seeking financial returns, creating a constantly evolving threat landscape. Third, the increasingly complex nature of connected systems makes them ever more challenging to protect. The more complex a system, the more potential vulnerabilities it may contain. And fourth, the data generated by connected devices represent an asset that is becoming increasingly valuable for organizations to derive insigh

The IT pendulum is swinging to distributed computing environments, network perimeters are dissolving, and compute is being distributed across various parts of organizations’ infrastructure—including, at times, their extended ecosystem. As a result, organizations need to ensure the appropriate levels of visibility and security at these remote locations, without dramatically increasing staff or tools. They need to invest in solutions that can scale to provide increased coverage and visibility, but that also ensure efficient use of resources. By implementing a common distributed data services layer as part of a comprehensive security operations and analytics platform architecture (SOAPA) and network operations architecture, organizations can reduce costs, mitigate risks, and improve operational efficiency.

Regardless of whether your data resides on-premises, in the cloud, or a combination of both, you are vulnerable to security threats, data breaches, data loss, and more. Security is often cited as a concern for organizations who are migrating to the public cloud, but the belief that the public cloud is not secure is a myth. In fact, the leading public cloud service providers have built rigorous security capabilities to ensure that your applications, assets, and services are protected. Security in the public cloud is now becoming a driver for many organizations, but in a rapidly evolving multicloud environment, you must keep up with changes that might impact your security posture. This eBook outlines the three core recommendations for cloud security across Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform.

Tips and best practices for data analytics executives Organizations today understand the value to be derived from arguably their greatest asset—data. When successfully aggregated and analyzed, data can unlock valuable insights, solve problems, improve products and services, and help companies gain a competitive edge. However, analytics executives face significant challenges in collecting, validating and analyzing data to deliver the right analytic insight to the right person at the right time. This e-book is designed to help. First, we'll explore the growing expectations for data analytics and the rise of the analytics executive. Then we'll explore a range of specific challenges those executives face, including those around data blending, analytics, and the organization itself, and offer best practices and strategies for meeting them.

Tips and best practices for data analytics executives Organizations today understand the value to be derived from arguably their greatest asset—data. When successfully aggregated and analyzed, data can unlock valuable insights, solve problems, improve products and services, and help companies gain a competitive edge. However, analytics executives face significant challenges in collecting, validating and analyzing data to deliver the right analytic insight to the right person at the right time. This e-book is designed to help. First, we'll explore the growing expectations for data analytics and the rise of the analytics executive. Then we'll explore a range of specific challenges those executives face, including those around data blending, analytics, and the organization itself, and offer best practices and strategies for meeting them. We'll also provide a short overview of TIBCO Statistica, an easy-to-use predictive analytics software solution designed to turn big data into your bigg

The days of data being narrowly defined as highly structured information from a few specific sources is long gone. Replacing that notion is the reality of a wide variety of data types coming from multiple sources, internal and external, to an organization. All of it is in service of providing everyone from IT, to line-of-business (LOB) employees, to C-level executives with insights that can have an immediate and transformative impact.

In the last few years we have seen a rapid evolution of data. The need to embrace the growing volume, velocity and variety of data from new technologies such as Artificial Intelligence (AI) and Internet of Things (IoT) has been accelerated. The ability to explore, store, and manage your data and therefore drive new levels of analytics and decision-making can make the difference between being an industry leader and being left behind by the competition. The solution you choose must be able to: • Harness exponential data growth as well as semistructured and unstructured data • Aggregate disparate data across your organization, whether on-premises or in the cloud • Support the analytics needs of your data scientists, line of business owners and developers • Minimize difficulties in developing and deploying even the most advanced analytics workloads • Provide the flexibility and elasticity of a cloud option but be housed in your data center for optimal security and compliance

Infinidat provides the difference you seek in a data storage solution for media and entertainment organizations. New demands have everyone in the industry feeling the impact of high latency storage and its impact on efficiently delivering speed to market on new and dense workload management. The Infinidat unified storage architecture enables the scale, performance and best overall TCO required to deliver an extreme competitive advantage to media and entertainment companies.

Big Data and analytics workloads represent a new frontier for organizations. Data is being collected from sources that did not exist 10 years ago. Mobile phone data, machine-generated data, and website interaction data are all being collected and analyzed. In addition, as IT budgets are already under pressure, Big Data footprints are getting larger and posing a huge storage challenge. This paper provides information on the issues that Big Data applications pose for storage systems and how choosing the correct storage infrastructure can streamline and consolidate Big Data and analytics applications without breaking the bank.

Continuous data availability is a key business continuity requirement for storage systems. It ensures protection against downtime in case of serious incidents or disasters and enables recovery to an operational state within a reasonably short period. To ensure continuous availability, storage solutions need to meet resiliency, recovery, and contingency requirements outlined by the organization.

Databases represent the backbone of most organizations. And Oracle databases in particular have become the mainstream data repository for most mission-critical environments. Some of the largest companies and organizations in the world rely on Oracle databases to store their most important data. The biggest challenge organizations face relative to an Oracle database is to maintain these databases at optimum performance and reliability without breaking the bank. This paper discusses the storage capabilities customers should consider when choosing storage to support an Oracle database environment.

Disaster recovery and long-term retention of data can be very challenging for organizations of any size, especially small to mid-sized companies. Cloud can provide efficiencies such as scale, agility, and initial lower storage costs, but organizations face significant challenges when expanding their data protection environments to the cloud. Dell EMC recognizes the use of cloud as a backup destination that is only going to increase in the future and as such they have architected modern IT solutions for small and mid-sized organizations that require powerful, easy to manage and to deploy data protection solutions with the introduction of the Dell EMC DP4400. To learn more, download this report from Dell and Intel®

Organizations are charging ahead with investments in cloud and analytics to deliver agility, scalability and cost savings. With computing power advancements and continuous growth of data, cloud provides the elastic workloads and flexibility required for modern business. However, the environment of flexibility and choice that cloud provides also creates complexity and challenges. In this white paper, learn how organizations are applying expertise and using the latest methods to move analytics to the cloud, including: • Why are organizations moving analytic work to the cloud? • What are the key challenges and misconceptions? • How do IT leaders provide choice while maintaining control?

Over the last decade, the enterprise analytics landscape has dramatically transformed. Vendors have come and gone, and platforms have continually expanded their offerings to include new functionality and keep pace with the demands of the businesses they serve. Originally envisioned as an IT-centric tool for enterprise reporting, analytics today has evolved into a business solution—empowering a range of users across every line of business, including front-line employees, field personnel, and executives. The rise of self-service analytics over the past decade has played a key role in promoting a data-driven mindset within every business function. However, this practice is limited to a skilled few. The vast majority of business professionals lack the time, analytical skills, or inclination to conduct their own analyses, and fail to effectively use analytics on a day-to-day basis. The result? Despite decades of investments, BI adoption at most organizations remains at 30%. The failure of e

With trends such as big data, artificial intelligence and IoT dominating today’s business technology headlines, the buzz around enterprise mobility (now considered known and familiar) may have lessened, but its importance has not. Consider its key role in today’s top tech trends, and then also consider this. According to an Oxford Economics survey of 500 senior IT executives, CEOs, and other senior managers, 80% say workers cannot do their jobs effectively without a mobile device. The same survey shows: 82% say mobile devices are critical to employee productivity 82% say mobile devices are critical to agility and the speed of decision making 76% say mobile devices are critical to customer service and satisfaction 75% say mobile devices are critical to innovation and collaboration 70% say mobile devices are critical to employee satisfaction and 70% say mobile devices are critical to revenue growth. The known and familiar now demands a fresh look and focus from enterprise organizations a

Why HyperIntelligence? Today, despite massive investments in data, IT infrastructure, and analytics software, the adoption of analytics continues to lag behind. In fact, according to Gartner, most organizations fail to hit the 30% mark. That means that more than 70% of people at most organizations are going without access to the critical information they need to perform to the best of their abilities. What’s stopping organizations from breaking through the 30% barrier and driving the pervasive adoption of intelligence? Simple. The majority of existing tools only cater to users who are naturally analytically inclined—the analysts, data scientists, and architects of the world. The other 70%—the people making the operational decisions daily within a business—simply lack the time, skill, or desire to seek out data and intelligence on their own. HyperIntelligence helps organizations operationalize their existing investments and arm everyone across the organization with intelligence. Whether