Published By: McKesson
Published Date: May 27, 2015
The shift to value-based care creates a sharp increase in healthcare organizations and networks’ need for data collection, aggregation and analysis. This white paper outlines the challenges involved with performing population-level analyses, developing cost accounting and profitability analyses across care settings, evaluating care episodes and integrating quality data. It explores the limitations of targeted software solutions to provide cross-enterprise insights. Finally, it provides advice for healthcare executives regarding how to approach gathering quality and cost-related data and how to leverage technology and analytical expertise to drive risk-based contract success.
"Extracting value from data is central to the digital
transformation required for businesses to succeed
in the decades to come. Buried in data are insights
that reveals what your customers need and how
they want to receive it, how sales, manufacturing,
distribution, and other aspects of business operations
are functioning, what risks are arising to threaten
the business, and more. That insight empowers your
businesses to reach new customers, develop and
deliver new products, to operate more efficiently
and more effectively, and even to develop new
business models. "
The bar for success is rising in higher education. University leaders and IT administrators are aware of the compelling benefits of digital transformation overall—and artificial intelligence (AI) in particular. AI can amplify human capabilities by using machine learning, or deep learning, to convert the fast-growing and plentiful sources of data about all aspects of a university into actionable insights that drive better decisions. But when planning a transformational strategy, these leaders must prioritize operational continuity. It’s critical to protect the everyday activities of learning, research, and administration that rely on the IT infrastructure to consistently deliver data to its applications.
Published By: Uberflip
Published Date: Dec 20, 2018
In today’s world, marketers know that producing content isn’t enough. If they’re going to continue to make an investment in creating content, they need to do more to ensure it performs. We’ve long since known that combining content with a remarkable experience will allow it to reach its full potential, and allow marketers to see results. But as with any emerging category, content experience was not without its detractors. After all, what kind of results could you expect from an investment in the experience around that content? If you’ve ever wondered why you should care about content experience, and wanted something a little more concrete than a few anecdotes from marketers, or third-party stats, then look no further.
Enterprises use data virtualization software such as TIBCO® Data Virtualization to reduce data bottlenecks so more insights can be delivered for better business outcomes. For developers, data virtualization allows applications to access and use data without needing to know its technical details, such as how it is formatted or where it is physically located. For developers, data virtualization helps rapidly create reusable data services that access and transform data and deliver data analytics with even heavylifting reads completed quickly, securely, and with high performance. These data services can then be coalesced into a common data layer that can support a wide range of analytic and applications use cases. Data engineers and analytics development teams are big data virtualization users, with Gartner predicting over 50% of these teams adopting the technology by 202
In this case study, large health systems implement IBM Watson Health to surface improvement opportunities. Using this tool, they were able to cut costs, reduce patients’ length of stay, acquire actionable data, address number of readmissions and improve management of COPD and sepsis.
Self-insured employers are mining their health and benefits data to save costs and provide quality care for employees. Data is driving business decisions, but how do you get from millions of rows of data to a consumable graph to taking action? In this white paper, we’ll delve into data analytics best practices that help self-insured employers find actionable insights in their benefits data.
• Which data sources will help you ensure you’re measuring the right thing at the right time
• How to ensure data variety and choose key metrics
• An example of a successful predictive analysis using benefits data
Published By: FICO EMEA
Published Date: Jan 25, 2019
Communications service providers (CSPs) have long recognized the potential of data analytics. Yet their early efforts to pull actionable intelligence from the oceans of data they have access to were largely unsuccessful. Many tried a 'big bang' approach to building a central repository without knowing what they wanted to do with the data in it. The arrival of artificial intelligence (AI) – its machine learning subset in particular – has changed their thinking and approach.
For this Quick Insights report, we surveyed 64 professionals from CSPs around the world who are applying, leveraging and/ or planning to deploy advanced analytics in some capacity at various points across the customer lifecycle.
Published By: Verisign
Published Date: May 31, 2017
Verisign has a unique view into distributed denial of service (DDos) attack trends, including attack statistics, behavioral trends and future outlook. The below data contains observations and insights about attack frequency and size derived from mitigations enacted on behalf of customers of Verisign DDoS Protection Services from January through March 2017.
Published By: Cisco EMEA
Published Date: Nov 13, 2017
Big data and analytics is a rapidly expanding field of information technology. Big data incorporates technologies and practices designed to support the collection, storage, and management of a wide variety of data types that are produced at ever increasing rates. Analytics combine statistics, machine learning, and data preprocessing in order to extract valuable information and insights from big data.
From its conception, this special edition has had a simple goal: to help SAP customers better understand SAP HANA and determine how they can best leverage this transformative technology in their organization. Accordingly, we reached out to a variety of experts and authorities across the SAP ecosystem to provide a true 360-degree perspective on SAP HANA.
This TDWI Checklist Report presents requirements for analytic DBMSs with a focus on their use with big data. Along the way, the report also defines the many techniques and tool types involved. The requirements checklist and definitions can assist users who are currently evaluating analytic databases and/or developing strategies for big data analytics.
For years, experienced data warehousing (DW) consultants and analysts have advocated the need for a well thought-out architecture for designing and implementing large-scale DW environments. Since the creation of these DW architectures, there have been many technological advances making implementation faster, more scalable and better performing. This whitepaper explores these new advances and discusses how they have affected the development of DW environments.
New data sources are fueling innovation while stretching the limitations of traditional data management strategies and structures. Data warehouses are giving way to purpose built platforms more capable of meeting the real-time needs of a more demanding end user and the opportunities presented by Big Data. Significant strategy shifts are under way to transform traditional data ecosystems by creating a unified view of the data terrain necessary to support Big Data and real-time needs of innovative enterprises companies.
Big data and personal data are converging to shape the internet’s most surprising consumer products. they’ll predict your needs and store your memories—if you let them. Download this report to learn more.
This white paper discusses the issues involved in the traditional practice of deploying transactional and analytic applications on separate platforms using separate databases. It analyzes the results from a user survey, conducted on SAP's behalf by IDC, that explores these issues.
The technology market is giving significant attention to Big Data and analytics as a way to provide insight for decision making support; but how far along is the adoption of these technologies across manufacturing organizations? During a February 2013 survey of over 100 manufacturers we examined behaviors of organizations that measure effective decision making as part of their enterprise performance management efforts. This Analyst Insight paper reveals the results of this survey.
This paper explores the results of a survey, fielded in April 2013, of 304 data managers and professionals, conducted by Unisphere Research, a division of Information Today Inc. It revealed a range of practical approaches that organizations of all types and sizes are adopting to manage and capitalize on the big data flowing through their enterprises.
In-memory technology—in which entire datasets are pre-loaded into a computer’s random access memory, alleviating the need for shuttling data between memory and disk storage every time a query is initiated—has actually been around for a number of years. However, with the onset of big data, as well as an insatiable thirst for analytics, the industry is taking a second look at this promising approach to speeding up data processing.
Over the course of several months in 2011, IDC conducted a research study to identify the opportunities and challenges to adoption of a new technology that changes the way in which traditional business solutions are implemented and used. The results of the study are presented in this white paper.
Forrester conducted in-depth surveys with 330 global BI decision-makers and found strong correlations between overall company success and adoption of innovative BI, analytics, and big data tools. In this paper, you will learn what separates the leading companies from the rest when it comes to exploiting innovative technologies in BI and analytics, and what steps you can take to either stay a leader or join their ranks.
This white paper, produced in collaboration with SAP, provides insight into executive perception of real-time business operations in North America. It is a companion paper to Real-time Business: Playing to win in the new global marketplace, published in May 2011, and to a series of papers on real-time business in Europe, Asia-Pacific and Latin America.
Leading companies and technology providers are rethinking the fundamental model of analytics, and the contours of a new paradigm are emerging. The new generation of analytics goes beyond Big Data (information that is too large and complex to manipulate without robust software), and the traditional narrow approach of analytics which was restricted to analysing customer and financial data collected from their interactions on social media. Today companies are embracing the social revolution, using real-time technologies to unlock deep insights about customers and others and enable better-informed decisions and richer collaboration in real-time.
If you are trying to process, understand, and benefit from "big data," you need SAP® HANA®.
Process data at extreme speeds
Real-time analytics and insights
If you want to make sure you have access to your data for insights, whenever and wherever you need them, then SAP HANA on Lenovo's future-defined infrastructure—powered by the Intel® Xeon® Platinum processor—delivers what you need.
Get the details on everything you need to know about the value of SAP HANA, why SAP chose Lenovo for their own HANA installation, and how Lenovo can help your organization today.