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By: SAS     Published Date: Apr 20, 2017
Hype and hope — Big Data has generated a lot of both. Thanks to an abundance of enterprise information systems, networks, applications and devices that churn out huge volumes of information, government agencies are awash in Big Data. Add to this data growth the emerging trend of the Internet of Things (IoT) — the network of people, data, things and processes that is increasingly linked through automated connections and sensors — and the future of Big Data can seem quite daunting.
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By: SAS     Published Date: Apr 25, 2017
But if you can’t explain how you got the answer, or what it means, it’s no good. Most self-service BI solutions can only display what has already happened, through reports or dashboards. And most have a predefined path of analysis that gives users very little creative freedom to explore new lines of thought. To maintain competitive advantage, your BI solution should allow business users to quickly and easily investigate and interrogate the data to find out why something happened – to uncover the root cause behind the “what.”
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By: SAS     Published Date: Apr 25, 2017
Physicians and their patients, medical policy makers and licensing boards, pharmaceutical companies and pharmacies all must work together to stem the opioid epidemic and achieve the fundamental objectives of reducing addiction and deaths. With so many players and data sources, today’s information is partial, fragmented, and often not actionable. We don’t have the data to understand what’s happening, to adjust policy, and to motivate physicians and patients to change their behaviors. Better data and analytics can help develop better treatment protocols, both for pain in the first place and for remediation when patients are becoming dependent on the drugs.
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By: SAS     Published Date: Apr 25, 2017
Your company’s culture is indispensable in informing the right way to govern data, a point we hope to drive home in this white paper. When you review the mistakes that otherwise earnest companies have made in their quests to apply rigor to their data, you’ll see why our mantra is: There is no template for data governance. And hopefully, you’ll realize what you need to do now.
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By: SAS     Published Date: Jun 05, 2017
From cars to factories to cities, many governments are already collecting information from citizens and connected devices that send and receive data over the internet of things (IoT). While analysts expect the IoT to soar to tens of billions of devices by 2020, no one knows how many or what new types of intelligent devices will emerge. But we do know that traditional approaches to data management and analytics may not be sufficient for sustaining value in this new, connected world
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By: SAS     Published Date: Oct 18, 2017
Want to get even more value from your Hadoop implementation? Hadoop is an open-source software framework for running applications on large clusters of commodity hardware. As a result, it delivers fast processing and the ability to handle virtually limitless concurrent tasks and jobs, making it a remarkably low-cost complement to a traditional enterprise data infrastructure. This white paper presents the SAS portfolio of solutions that enable you to bring the full power of business analytics to Hadoop. These solutions span the entire analytic life cycle – from data management to data exploration, model development and deployment.
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By: SAS     Published Date: Oct 18, 2017
Machine learning uses algorithms to build analytical models, helping computers “learn” from data. It can now be applied to huge quantities of data to create exciting new applications such as driverless cars. This paper, based on presentations by SAS Data Scientist Wayne Thompson, introduces key machine learning concepts and describes SAS solutions that enable data scientists and other analytical professionals to perform machine learning at scale. It tells how a SAS customer is using digital images and machine learning techniques to reduce defects in the semiconductor manufacturing process.
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By: SAS     Published Date: Oct 18, 2017
Health insurers have long been plagued by issues of fraud, waste, abuse, error and corruption. Taking an enterprise approach to payment integrity – one that combines advanced data management and sophisticated analytics – can help payers detect and prevent fraud; effect positive change in how providers, employees and patients behave; and substantially reduce health care costs. Payers can achieve better outcomes when software support for the core disciplines of payment integrity run on a single platform.
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By: SAS     Published Date: Jan 17, 2018
We have conditioned patients not only to expect opioids for pain relief, but to utilize more and more of them, and the addiction is both psychological and physical. To remedy the situation, a lot of policies and practices and behaviors must change around how the health care system approaches pain. But we do not yet have the data and analytics we need to determine what specifically to do at the patient level or the policy level. Download this whitepaper to learn more about the resources available and how we can fix this issue.
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By: SAS     Published Date: Jan 17, 2018
What can you see and discover when you’re able to explore trends and make predictions with your organization’s data? If you’re a midsize home delivery business, you can discover new ways to make customers happy. If you’re a local government agency, you can predict where your resources are needed most. And if you’re a growing hospital, you can bring life-changing patient data directly to doctors and nurses. In this e-book, we’ve profiled six organizations that are using self-service visual exploration to make big improvements in the way they work. From college administrators to professional sports teams, everyone makes better decisions with easy access to powerful, interactive analytics.
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By: SAS     Published Date: Jan 17, 2018
Competitive advantage from analytics is changing, and for the better. For the first time in four years, MIT Sloan Management Review found an increasing ability to strategically innovate with analytics based on interviews with more than 2,600 practitioners and scholars globally. Learn more about key findings, including: Wider use of analytics, better knowledge of its benefits and greater focus on applications have reversed a trend on the benefits of analytics. Return on investment for analytics stems from the governing and sharing of data throughout the organization. Machine learning enables organizations to discover more insight from their data, allowing employees to focus on other critical responsibilities.
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By: SAS     Published Date: Mar 06, 2018
Imagine getting into your car and saying, “Take me to work,” and then enjoying an automated drive as you read the morning news. We are getting very close to that kind of scenario, and companies like Ford expect to have production vehicles in the latter part of 2020. Driverless cars are just one popular example of machine learning. It’s also used in countless applications such as predicting fraud, identifying terrorists, recommending the right products to customers at the right time, and correctly identifying medical symptoms to prescribe appropriate treatments. The concept of machine learning has been around for decades. What’s new is that it can now be applied to huge quantities of data. Cheaper data storage, distributed processing, more powerful computers and new analytical opportunities have dramatically increased interest in machine learning systems. Other reasons for the increased momentum include: maturing capabilities with methods and algorithms refactored to run in memory; the
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By: SAS     Published Date: Mar 06, 2018
Machines learn by studying data to detect patterns or by applying known rules to: • Categorize or catalog like people or things • Predict likely outcomes or actions based on identified patterns • Identify hitherto unknown patterns and relationships • Detect anomalous or unexpected behaviors The processes machines use to learn are known as algorithms. Different algorithms learn in different ways. As new data regarding observed responses or changes to the environment are provided to the “machine” the algorithm’s performance improves. Thereby resulting in increasing “intelligence” over time.
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By: SAS     Published Date: Mar 06, 2018
With decisions riding on the timeliness and quality of analytics, business stakeholders are less patient with delays in the development of new applications that provide reports, analysis, and access to diverse data itself. Executives, managers, and frontline personnel fear that decisions based on old and incomplete data or formulated using slow, outmoded, and limited reporting functionality will be bad decisions. A deficient information supply chain hinders quick responses to shifting situations and increases exposure to financial and regulatory risk—putting a business at a competitive disadvantage. Stakeholders are demanding better access to data, faster development of business intelligence (BI) and analytics applications, and agile solutions in sync with requirements.
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By: SAS     Published Date: Mar 06, 2018
Despite heavy, long-term investments in data management, data problems at many organizations continue to grow. One reason is that data has traditionally been perceived as just one aspect of a technology project; it has not been treated as a corporate asset. Consequently, the belief was that traditional application and database planning efforts were sufficient to address ongoing data issues.
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By: SAS     Published Date: Mar 06, 2018
The 2016 ACFE Report to the Nations on Occupational Fraud and Abuse analyzed 2,410 occupational fraud cases that caused a total loss of more than $6.3 billion.8 Victim organizations that lacked anti-fraud controls suffered double the amount of median losses. SAS’ unique, hybrid approach to insider threat deterrence – which combines traditional detection methods and investigative methodologies with behavioral analysis – enables complete, continuous monitoring. As a result, government agencies and companies can take pre-emptive action before damaging incidents occur. Equally important, SAS solutions are powerful yet simple to use, reducing the need to hire a cadre of high-end data modelers and analytics specialists. Automation of data integration and analytics processing makes it easy to deploy into daily operations.
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By: SAS     Published Date: Mar 06, 2018
The Internet of Things enables retailers to do three basics better and faster: 1) Sensing who customers are and what they’re doing, 2) Understanding customer behavior and preferences, and 3)Acting on that insight to create a more engaging customer experience. - There are high-potential IoT applications in supply chain, in “smart store” operations, and especially in providing an engaging experience to the “connected customer.” IoT data can anticipate where the customer is headed and how to meet her there. - Much of the IoT ground, in both data management and analytics, may be unfamiliar. Retailers and their IT organizations have to be realistic about the technological challenges, their own capabilities, and where they need assistance. - To differentiate through IoT, focus on the analytics. Devices and their data — and even their platforms — are commodities. Advantage goes to the retailer who does the most with the data to engage the connected customer.
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By: SAS     Published Date: May 24, 2018
This paper provides an introduction to deep learning, its applications and how SAS supports the creation of deep learning models. It is geared toward a data scientist and includes a step-by-step overview of how to build a deep learning model using deep learning methods developed by SAS. You’ll then be ready to experiment with these methods in SAS Visual Data Mining and Machine Learning. See page 12 for more information on how to access a free software trial. Deep learning is a type of machine learning that trains a computer to perform humanlike tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. Deep learning is used strategically in many industries.
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By: SAS     Published Date: Jun 06, 2018
Competitive advantage from analytics is changing, and for the better. For the first time in four years, MIT Sloan Management Review found an increasing ability to strategically innovate with analytics based on interviews with more than 2,600 practitioners and scholars globally. Learn more about key findings, including: Wider use of analytics, better knowledge of its benefits and greater focus on applications have reversed a trend on the benefits of analytics. Return on investment for analytics stems from the governing and sharing of data throughout the organization. Machine learning enables organizations to discover more insight from their data, allowing employees to focus on other critical responsibilities.
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By: SAS     Published Date: Jun 06, 2018
A multitude of “things” generate floods of big data – cars, wearables, machines and appliances. Wouldn’t you like to sift through that noise and become an organization that relies on data to make fact-based decisions? Learn about the three foundations of becoming data-driven – data management, analytics and visualization – and how they can increase profitability, boost performance, raise market share and improve operations. Read about hurdles to becoming a data-driven organization and learn best practices from others. Then get a glimpse of what the future holds with the Internet of Things (IoT), edge analytics, artificial intelligence (AI) and other technology innovations.
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By: SAS     Published Date: Aug 28, 2018
Despite heavy, long-term investments in data management, data problems at many organizations continue to grow. One reason is that data has traditionally been perceived as just one aspect of a technology project; it has not been treated as a corporate asset. Consequently, the belief was that traditional application and database planning efforts were sufficient to address ongoing data issues. As our corporate data stores have grown in both size and subject area diversity, it has become clear that a strategy to address data is necessary. Yet some still struggle with the idea that corporate data needs a comprehensive strategy. There’s no shortage of blue-sky thinking when it comes to organizations’ strategic plans and road maps. To many, such efforts are just a novelty. Indeed, organizations’ strategic plans often generate very few tangible results for organizations – only lots of meetings and documentation. A successful plan, on the other hand, will identify realistic goals along with a r
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By: SAS     Published Date: Aug 28, 2018
Machine learning systems don’t just extract insights from the data they are fed, as traditional analytics do. They actually change the underlying algorithm based on what they learn from the data. So the “garbage in, garbage out” truism that applies to all analytic pursuits is truer than ever. Few companies are already using AI, but 72 percent of business leaders responding to a PWC survey say it will be fundamental in the future. Now is the time for executives, particularly the chief data officer, to decide on data management strategy, technology and best practices that will be essential for continued success.
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By: SAS     Published Date: Aug 28, 2018
Starting data governance initiatives can seem a bit daunting. You’re establishing strategies and policies for data assets. And, you’re committing the organization to treat data as a corporate asset, on par with its buildings, its supply chain, its employees or its intellectual property. However, as Jill Dyché and Evan Levy have noted, data governance is a combination of strategy and execution. It’s an approach that requires one to be both holistic and pragmatic: • Holistic. All aspects of data usage and maintenance are taken into account in establishing the vision. • Pragmatic. Political challenges and cross-departmental struggles are part of the equation. So, the tactical deployment must be delivered in phases to provide quick “wins” and avert organizational fatigue from a larger, more monolithic exercise. To accomplish this, data governance must touch all internal and external IT systems and establish decision-making mechanisms that transcend organizational silos. And, it must provi
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By: SAS     Published Date: Oct 03, 2018
Risks have intensified as retailers and financial organizations embrace new technologies to meet customer demands for convenience. The rise of mobile and online transactions introduces new risks – and with that, new requirements for fraud mitigation. This paper discusses key steps for fighting back against fraud risk by establishing appropriate and accurate data, analytics and alert management.
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By: SAS     Published Date: Oct 03, 2018
Because terrorists and other criminals are already using technology to carry out their missions, intelligence professionals need to access all available, appropriate information, to extract important elements and process, analyze and disseminate it quickly to keep ahead of potential threats. The scale, complexity and changing nature of intelligence data can make it impossible to stay in front without the aid of technology to collect, process and analyze big data. This paper describes a solution for how this information can be quickly and safely shared with access based on a user's organizational responsibilities and need to know.
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