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.
"IoT adoption is expected to generate a 21% increase in corporate profits by 2022. This business value comes from the ability to automate processes and collect and analyze massive amounts of data—so organizations can make better informed decisions and deliver powerful customer experiences.
But how are organizations really putting IoT to work for their business? And how can IoT risks be mitigated so these rewards can truly be achieved?
This e-book explores the potential of IoT in the enterprise, which industries are leading the way and how to secure your connected things. It also provides:
• 7 best practices for data privacy and security policies
• 7 items to consider for device security
• 6 key considerations for network connection security"
There’s strong evidence organizations are challenged by the opportunities presented by external information sources such as social media, government trend data, and sensor data from the Internet of Things (IoT). No longer content to use internal databases alone, they see big data resources augmented with external information resources as what they need in order to bring about meaningful change. According to a September 2015 global survey of 251 respondents conducted by Harvard Business Review Analytic Services, 78 percent of organizations agree or strongly agree that within two years the use of externally generated big data will be “transformational.” But there’s work to be done, since only 21 percent of respondents strongly agree that external data has already had a transformational effect on their firms.
In the financial services industry (FSI), high-performance compute infrastructure is not optional; it’s a prerequisite for survival. No other industry generates more data, and few face the combination of challenges that financial services does: a rapidly changing competitive landscape, a complex regulatory environment, tightening margin pressure, exponential data growth, and demanding performance service-level agreements (SLAs).
Consider the many ways that a customer encounters your brand – organic results on a search engine, display media campaigns, social media links, re-targeting on external sites, etc. One thing is certain – consumer journeys are far from linear. They can occur across multiple platforms, devices and browsers. The problem is that organizations are often constrained to channel-limiting decisions regarding their media investment allocations.
Marketing attribution helps you analyze the impact and business value of company-generated marketing interactions to help make the best marketing investment decisions. The challenge is to interpret the massive volumes of customer data that continues to expand day by day.
Healthcare and Life Sciences organizations are using data to generate knowledge that helps them provide better patient care, enhances biopharma research and development, and streamlines operations across the product innovation and care delivery continuum. Next-Gen business intelligence (BI) solutions can help organizations reduce time-to-insight by aggregating and analyzing structured and unstructured data sets in real or near-real time.
AWS and AWS Partner Network (APN) Partners offer technology solutions to help you gain data-driven insights to improve care, fuel innovation, and enhance business performance.
In this webinar, you’ll hear from APN Partners Deloitte and hc1.com about their solutions, built on AWS, that enable Next-Gen BI in Healthcare and Life Sciences.
Join this webinar to learn:
How Healthcare and Life Sciences organizations are using cloud-based analytics to fuel innovation in patient care and biopharmaceutical product development.
How AWS supports BI solutions f
Published By: Nextgen
Published Date: Aug 08, 2017
Meditouch helps integrate patient engagement across your practice and the care continuum.
Foster patient satisfaction and achieve front office efficiency.
YourHealthFile patient portal for convenience, transparency, and communication.
Patient-generated data alleviates staff burnout via manual duties; prevents bottlenecks.
Easily refill Rx, reduce no-shows, quickly fill cancellations.
iPad functionality supports education and doctor patient relationship.
Garner better reviews, gain more online visibility, market your practice to new patients.
Every day, companies generate mountains of data that are critical to their business. With that data comes
a clear challenge: How do you protect exabytes of data that's strewn across global data centers,
computer rooms, remote offices, laptops, desktops, and mobile devices, as well as hosted by many
different cloud providers, without choking business agility, employee productivity, and customer
experience? The solution lies not in throwing more technology at the network, but in taking specific steps
to identify malicious actions and respond to them in order to fix the issue, a process known as
Productivity has become synonymous with revenue for many organizations. The theory is simple: The more your employees can get done, the more income they can generate. And yet data from the Bureau of Labor Statistics shows that the productivity level in the United States is actually fairly low, topping out at 3.1% in one quarter and going into negative digits in others over the past few years.1 This means labor productivity, measured in output per hour of labor, is low or—in many quarters—shrinking. Download this white paper from Dell and Intel® to learn more.
Intel Inside®. Powerful Productivity Outside.
Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and/or other countries.
Published By: IBM APAC
Published Date: Jul 09, 2017
Organizations today collect a tremendous amount of data and are bolstering their analytics capabilities to generate new, data-driven insights from this expanding resource. To make the most of growing data volumes, they need to provide rapid access to data across the enterprise. At the same time, they need efficient and workable ways to store and manage data over the long term.
A governed data lake approach offers an opportunity to manage these challenges. Download this white paper to find out more.
As social media use has grown, an urgent need has emerged to correlate the information generated through social data with existing consumer information, and integrate it with sophisticated data management systems. This white paper describes how organizations can blend social insights with more-traditional data in an integrated customer data hub to optimize social strategies and create outreach efforts, new products, and campaigns grounded in real-time, repeatable, automated, and scalable analysis.
Published By: FusionOps
Published Date: Jun 15, 2016
The supply chain generates huge volumes of data captured in ERP, CRM, demand planning and other systems. Download this whitepaper to learn how FusionOps Machine Learning can provide companies with a more accurate, granular understanding of their business by harmonizing these disparate data sources in the cloud, and applying machine learning algorithms.
There are five ways to provision test data. You can copy or take a snapshot of your production database or databases. You can provision data manually or via a spreadsheet. You can derive virtual copies of your production database(s).
You can generate subsets of your production database(s). And you can generate synthetic data that is representative of your production data but is not actually real. Of course, the first four examples assume that the data you need for testing purposes is available to you from your production databases.
If this is not the case, then only manual or synthetic data provision is a viable option.
Download this whitepaper to find out more about how CA Technologies can help your business and its Test Data problems.
"There's new legislation in place, that's expanded the definition of personal data and puts IT and testing departments on high alert to safeguard personal data, across testing and development environments. It's the General Data Protection Regulation (GDPR). Are you ready for it?
In this session, we’ll demonstrate how CA Test Data Manager helps to both mask your production data and to generate synthetic test data; a powerful combination to help you meet compliance needs and deliver quality applications. There will be a short section on the future of the tester self-service model that will enable testers to efficiently get access to the right test data."
Relentless digital developments have created the chance for the finance team to become true strategic leaders across the business—but they have also raised expectations and generated new challenges. In this digibook, we explore the growth of cloud, social, mobile and big data, and how smart CFOs are taking advantage. We show how to use your big data analytics and smooth procurement systems as a launchpad to holding your own in the Digital Age.
Each chapter is illustrated by examples from successful businesses that have embraced the new ways of working as well as guidance on how to plan for and implement the changes that are right for you.
Kim Komando, “America’s Digital Goddess,” is a leading radio host and web entrepreneur. Every month her video-taped three-hour weekly radio show adds 2.8 TB of video content to its extensive playback library. Switching from their current storage solution, a combination of NAS Arrays and Tape Drives, to Wasabi not only generated huge savings it was simpler and faster. Plus, since Cloudberry works seamlessly with Wasabi, the show also enjoys efficient and cost-effective remote data protection. In this Case Study read in detail how the ‘Kim Komando Show’ made the transition to next generation cloud storage.
he digital transformation era has left organizations uncertain about how to best use all their newly generated data to foster growth and edge out the competition. This has led to the development of a new set of smart technologies to enhance an organizations’ ability to parse through extensive troves of data to find new insights.
Download your complimentary copy of “AI and Machine Learning in Your Organization” to avoid missing out on valuable business insights.
You’ll learn about:
*The critical role AI and machine learning play in finding answers
*How data is the fuel for your machine learning and AI-powered initiatives
*The ways in which AI and machine learning are being applied today to bolster IT operations and security
Business leaders are eager to harness
the power of big data. However, as the
opportunity increases, ensuring that source
information is trustworthy and protected
becomes exponentially more difficult. If not
addressed directly, end users may lose
confidence in the insights generated from
their data—which can result in a failure to
act on opportunities or against threats.
Information integration and governance
must be implemented within big data
applications, providing appropriate
governance and rapid integration from
the start. By automating information
integration and governance and employing
it at the point of data creation, organizations
can boost confidence in big data.
A solid information integration and
governance program must become a
natural part of big data projects, supporting
automated discovery, profiling and
understanding of diverse data sets to
provide context and enable employees
to make informed decisions. It must be
agile to accommodate a wide variety of
data and seamle
To keep up with sweeping global economic and societal changes, public services organizations are undergoing significant technology-driven transformation. Aging populations, rapid urbanization, political instability, concerns about sustainability and resiliency, and changing worker and resident expectations are driving public services organizations to radically improve operations and service delivery. At the core of this transformation is the ability to collect and process vast amounts of data to help to improve outcomes and services. One way to generate this data is through the Internet of Things (IoT) — which IDC defines as a network of networks of uniquely identifiable endpoints or “things” that communicate without human interaction using IP connectivity. The IoT is a transformational technology that can reshape the public sector, enabling improved outcomes and new services such as remote patient monitoring, advanced traffic solutions and predictive policing.
Generate rich virtual data that covers the full range of possible scenarios and provide the unconstrained access to environments needed to deliver rigorously tested applications on time and within budget. Model complex live system data and apply automated rule-learning algorithms to pay off technical debt and uncover in depth understanding of composite applications, while exposing virtual data to distributed teams on demand and avoiding testing bottlenecks.
With the proliferation of health and fitness data due to personal fitness trackers, medical devices and other sensors that collect real-time information, cognitive computing is becoming more and more important. Cognitive computing systems, with the ability to understand, reason and learn while interacting with human-generated data, enable providers to find meaningful patterns in vast seas of information. IBM Watson Health is leveraging the power of cognitive computing to help providers make data-driven decisions to improve and save lives worldwide, while controlling healthcare costs. Read our whitepaper and learn about the new era of cognitive computing and how it can improve health outcomes, optimize care and engage individuals in making healthy choices.
Big Data has generated much interest and attention in the media of late. Indeed, several authors have recently raised the question of whether Big Data approaches, such as Hadoop, will pronounce the death sentence on the conventional data warehouse.
In this survey we investigate the current state of the data warehouse and examine its recent challenger in the form of Big Data solutions as an alternative. Is the new technology really complementary or is the reign of the data warehouse nearing an end?