it analytics

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Published By: SAS     Published Date: Oct 18, 2017
How much are fraud, error, waste and abuse costing your organization? Costs to insurers are huge – as much as 25 percent of payments made. But data management and analytics can save the day. This paper discusses how data can be used to predict and detect loss in all its forms.
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SAS
Published By: SAS     Published Date: Jan 17, 2018
The Industrial Internet of Things (IIoT) is flooding today’s industrial sector with data. Information is streaming in from many sources — equipment on production lines, sensors at customer facilities, sales data, and much more. Harvesting insights means filtering out the noise to arrive at actionable intelligence. This report shows how to craft a strategy to gain a competitive edge. It explains how to evaluate IIoT solutions, including what to look for in end-to-end analytics solutions. Finally, it shows how SAS has combined its analytics expertise with Intel’s leadership in IIoT information architecture to create solutions that turn raw data into valuable insights.
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SAS
Published By: SAS     Published Date: Jan 17, 2018
For many of us, the term “smart city” conjures up images of sensors collecting data about everything from traffic patterns to energy use. It’s common for government leaders to think, “That’s not for us. We’re not there yet.” But if your organization is collecting data of any kind, you are in a position to use that data to create a smarter city for your citizens. Download this whitepaper for 10 examples of analytics being used to solve problems or simplify tasks for government organizations.
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SAS
Published By: SAS     Published Date: Jan 17, 2018
The Connected Customer is an individual who is intimately connected to the data, outcomes, decisions, and staff associated with any relationship to an organization. This intensely personal connection is not just a matter of the most recent transaction, but represents a combination of connected data, connected analytics, and collaborative decisions associated with improving the customer’s relationship with the organization over time. In this report, Blue Hill explores the key traits associated with supporting the Connected Customer through the Internet of Things, and provides guidance on why the Internet of Things will be essential across the general business landscape.
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SAS
Published 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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SAS
Published By: SAS     Published Date: Jan 17, 2018
Insurers have long been plagued by fraud, error, waste, and abuse in health care payments. The costs are huge – amounting to as much as 25 percent of payments made. Today’s data management and analytics platforms promise breakthroughs by incorporating comparative and behavioral data to predict as well as detect loss in all its forms. To explore the opportunities and how insurers can capitalize on them, IIA spoke with Ben Wright, Sr. Solutions Architect in SAS’s Security Intelligence Global Practice.
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SAS
Published 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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SAS
Published By: SAS     Published Date: Jan 17, 2018
A picture is worth a thousand words – especially when you are trying to find relationships and understand your data – which could include thousands or even millions of variables. To create meaningful visuals of your data, there are some basic tips and techniques you should consider. Data size and composition play an important role when selecting graphs to represent your data. This paper, filled with graphics and explanations, discusses some of the basic issues concerning data visualization and provides suggestions for addressing those issues. From there, it moves on to the topic of big data and discusses those challenges and potential solutions as well. It also includes a section on SAS® Visual Analytics, software that was created especially for quickly visualizing very large amounts of data. Autocharting and "what does it mean" balloons can help even novice users create and interact with graphics that can help them understand and derive the most value from their data.
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SAS
Published By: SAS     Published Date: Jan 17, 2018
This RSR custom research report explores the impact of omnichannel methods on merchandising, marketing and the supply chain; specifically, what analytical capabilities address the challenges that omnichannel selling and fulfillment pose for retailers. Consumers today routinely begin their shopping journeys online, but complete their purchases in nearby stores, in their “home” stores or delivered directly to their doors. Retail analytics enables organizations to capture data from their customers' journeys. Retailers that successfully deliver relevant omnichannel experiences while gaining a more sophisticated understanding of demand (where and how it is initiated) will enhance their brands’ value and create compelling and profitable customer relationships.
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SAS
Published 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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SAS
Published By: SAS     Published Date: Jan 17, 2018
This TDWI Best Practices Report focuses on how organizations can and are operationalizing analytics to derive business value. It provides in-depth survey analysis of current strategies and future trends for embedded analytics across both organizational and technical dimensions, including organizational culture, infrastructure, data and processes. It looks at challenges and how organizations are overcoming them, and offers recommendations and best practices for successfully operationalizing analytics in the organization.
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SAS
Published By: SAS     Published Date: Jan 17, 2018
It’s a social world and the majority of organizations today use Microsoft Office as their chosen office productivity suite. So it’s important to deliver insights where people are working most. This paper shows how you can use results produced by SAS Visual Analytics with Microsoft Office applications – Outlook, Excel, Word, PowerPoint and SharePoint. With plenty of how-to specifics, you’ll see how easy it is to combine analytic visualizations and reports with Microsoft products to share insights, improve collaboration and drive further adoption of analytics across your organization.
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SAS
Published By: SAS     Published Date: Mar 06, 2018
Today's artifical intelligence (AI) solutions are not sentient in the manner popularized in science fiction by scores of self-aware and typically nefarious androids. Even so, the ability to arm such systems with the ability to directly sense and respond to their in situ environment is critical. Why? In the future, our experiences will be smart, intuitive and informed by analytics that are not seen but felt via new business, personal and operational engagement models. Enabling this interaction requires AI applications that can sense, analyze and respond to their environment in an intelligent and interactive manner. Without requiring the end user to write, understand or interpret code. “Sensitive” artificial intelligence enables: • More productive use of expanded (big, often unstructured) information sources • Intuitive man-machine interactions (no code-speak here!) • Adaptive, immersive experiences and environments As frequently touted on the nightly news, AI’s popularity is clear. Ho
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SAS
Published By: SAS     Published Date: Mar 06, 2018
There is a lot of excitement in the market about artificial intelligence (AI), machine learning (ML), and natural language processing (NLP). Although many of these technologies have been available for decades, new advancements in compute power along with new algorithmic developments are making these technologies more attractive to early adopter companies. These organizations are embracing advanced analytics technologies for a number of reasons including improving operational efficiencies, better understanding behaviors, and gaining competitive advantage.
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SAS
Published 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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SAS
Published By: SAS     Published Date: Mar 06, 2018
Business intelligence has come a long way ? from assistance with report generation to self-service platforms for discovery and analytical insight. As technological capabilities and business aptitude with information continue to advance, the next generation of BI will be even more capable and valuable to the enterprise. To discuss today’s success factors and tomorrow’s opportunities, IIA spoke with Rick Styll, Senior Manager, Visual Analytics Product Management at SAS, and Tapan Patel, Principal Product Marketing Manager at SAS.
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SAS
Published By: SAS     Published Date: Mar 06, 2018
Better health care at lower costs, for everyone – how do health care providers get there? Understanding the gaps in patient care, patient needs, and the geographic distribution of the patient population are important elements to consider when making decisions about improving the quality of care and reducing its costs. To effectively analyze gaps in patient care, the data needs to be in a single place or system. However, in many organizations, data is spread across a myriad of spreadsheets and database systems. Data not organized for visual exploration and coherent analysis isn’t useful for decision making. Hence the need for visually appealing and scalable analytical tools to help organizations be more efficient, effective and economically successful.
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SAS
Published By: SAS     Published Date: Mar 06, 2018
Location data is found everywhere – with an item or a device, in a conversation or behavior, in machines or sensors, tied to a customer or competitor, attached to a database record or recorded from vehicles or other moving objects. Organizations want to take advantage of location data to improve decisions, create better customer engagement and experiences, reduce risks and automate business processes.
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SAS
Published By: SAS     Published Date: Mar 06, 2018
Known for its industry-leading analytics, data management and business intelligence solutions, SAS is focused on helping organizations use data and analytics to make better decisions, faster. The combination of self-service BI and analytics positions you for improved productivity and smarter business decisions. So you can become more competitive as you use all your data to take better actions. Instead of depending on hunch-based choices, you can make decisions that are truly rooted in discovery and analytics. And you can do it through an interface that anyone can use. At last, your business users can get close enough to the data to manipulate it and draw their own reliable, fact-based conclusions. And they can do it in seconds or minutes, not hours or days. Equally important, IT remains in control of data access and security by providing trusted data sets and defined processes that promote the valuable, user-generated content for reuse and consistency. But, they are no longer forced
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SAS
Published By: SAS     Published Date: Mar 06, 2018
Digital transformation is a reality for marketers that is wrapped in both opportunities and headaches. Marketers understand the choices and expectations that their customers now have, and they are up for the challenge. But marketers also have many obstacles to overcome to deliver the consistently good, timely and engaging customer experience, across devices, that customers demand. The good news is that the marketing technology industry is rapidly evolving to address these challenges. And in the same way that consumers have an abundance of choices, marketers also have many options when it comes to choosing partners to help. But where to start, and how to choose the right partners?
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SAS
Published By: SAS     Published Date: Mar 06, 2018
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics, and operations. Even so, traditional, latent data practices are possible, too. Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. With the right end-user tools, a data lake can enable the self-service data practices that both technical and business users need. These practices wring business value from big data, other new data sources, and burgeoning enterprise da
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SAS
Published By: SAS     Published Date: Mar 06, 2018
For data scientists and business analysts who prepare data for analytics, data management technology from SAS acts like a data filter – providing a single platform that lets them access, cleanse, transform and structure data for any analytical purpose. As it removes the drudgery of routine data preparation, it reveals sparkling clean data and adds value along the way. And that can lead to higher productivity, better decisions and greater agility. SAS adheres to five data management best practices that support advanced analytics and deeper insights: • Simplify access to traditional and emerging data. • Strengthen the data scientist’s arsenal with advanced analytics techniques. • Scrub data to build quality into existing processes. • Shape data using flexible manipulation techniques. • Share metadata across data management and analytics domains.
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SAS
Published By: SAS     Published Date: Mar 06, 2018
Industry leaders from the banking and vendor landscape are working to streamline the customer experience while closing the opportunities for fraud and exposure. Balancing security and convenience will require an approach that combines consumer-facing authentication (such as passwords, PINs and biometrics) with background security measures (such as transaction and session-behavior analytics).
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SAS
Published By: SAS     Published Date: Mar 06, 2018
These emerging technologies and solutions certainly are not unique to financial services. But Stewart, a business director of security intelligence solutions within the SAS Security Intelligence Practice, sees particular interest and application in AML circles. "There remain a good number of manual processes within financial crimes departments in financial institutions, and AI can help automate some of those rote tasks such as document review or alert triage," he says. "Due to investments in technology, there is a lower barrier of entry for midsized institutions. "And finally, there's this anxiety over the unknown - those risks they are not able to detect, that may be hidden using traditional techniques - so they're hoping that more advanced, unsupervised learning techniques can be used to identify those edge cases or behaviors that are out of norm." In an interview about analytics and the AML paradigm shift, Stewart discusses: • The new industry intrigue with artificial intelligence a
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SAS
Published 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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SAS
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