A man with one watch always knows what time it is...but a man with two watches is never sure. Contrary to this adage, load forecasters at electric utilities would gladly wear an armful of watches. With only one model to choose from, it is certain that some forecasts will be wrong. But with multiple models, forecasters can have confidence about periods when the forecasts agree and can focus their attention on periods when the predictions diverge. Having a second opinion is preferred, and that's one of the six classic rules for forecasters as per Dr. Tao Hong of the University of North Carolina at Charlotte. Dr. Hong is the premiere thought leader and practitioner in the field of energy forecasting. This presentation discusses Dr. Hong's six rules, how they relate to the increasingly complex problem of forecasting electricity consumption, and the role that predictive analytics plays.

Microsoft Windows 10 is a new operating system that is increasingly being adopted by enterprises around the world. SAS has planned to expand SAS^® Mobile BI, which is currently available on Apple iOS and Google Android, to the Microsoft Windows 10 platform. With this new application, customers can download business reports from SAS^® Visual Analytics to their desktop, laptop, or Microsoft Surface device, and use these reports both online and offline in their day-to-day business life. With Windows 10, users have the option of pinning a report to the desktop for quick access. This paper demonstrates this new SAS mobile application. We also demonstrate the cool new functionality on iOS and Android platforms, and compare them with the Windows 10 application.

Would you agree that the value of SAS^® for your organization comes from transforming data into actionable information, using well-prepared human resources? This paper presents seven areas where this potential SAS value can be lost by inefficient data access, limited reporting and visualization, poor data cleansing, obsolete predictive analytics, incomplete SAS solutions, limited hardware use, and lack of governance. This paper also suggests what to do to overcome these issues.

A major issue in America today is the growing gap between the rich and the poor. Even though the basic concept has entered the public consciousness, the effects of highly concentrated wealth are hotly debated and poorly understood by the general public. The goal of this paper is to get a fair picture of the wealth gap and its ill effects on American society. Before visualizing the financial gap, an exploration and descriptive analysis is carried out. By considering the data (gross annual income, taxable income, and taxes paid), which is available on the website of United States Census Bureau, we try to find out the actual spending capacity of the people in America. We visualize the financial gap on the basis of the spending capacity. With the help of this analysis we try to answer the following questions. Why is it important to have a fair idea of this gap? At what rate is the average wealth of the American population increasing? How does it affect the tax system? Insights generated from answering these questions will be used for further analysis.

Sovereign risk rating and country risk rating are conceptually distinct in that the former captures the risk of a country defaulting on its commercial debt obligations using economic variables while the latter covers the downside of a country's business environment including political and social variables alongside economic variables. Through this paper we would like to understand the differences between these risk approaches in assessing a country's credit worthiness by statistically examining the predictive power of political and social variables in determining country risk. To do this, we wish to build two models, first model with economic variables as regressors (sovereign risk model) and the second model with economic, political and social variables as regressors (country risk model) to compare the predictive power of regressors and model performance metrics between both the models. This will be an OLS regression model with country risk rating obtained from S&P as the target variable. With a general assumption that economic variables are driven by political processes and social factors, we would like to see if the second model has better predictive power. The economic, political and social indicators data that will be used as independent variables in the model will be obtained from world bank open data and target variable (country risk rating) will be obtained from S&P country risk ratings data.

The Consumer Financial Protection Bureau (CFPB) collects tens of thousands of complaints against companies each year, many of which result in the companies in question taking action, including making payouts to the individuals who filed the complaints. Given the volume of the complaints, how can an overseeing organization quantitatively assess the data for various trends, including the areas of greatest concern for consumers? In this presentation, we propose a repeatable model of text analytics techniques to the publicly available CFPB data. Specifically, we use SAS^® Contextual Analysis to explore sentiment, and machine learning techniques to model the natural language available in each free-form complaint against a disposition code for the complaint, primarily focusing on whether a company paid out money. This process generates a taxonomy in an automated manner. We also explore methods to structure and visualize the results, showcasing how areas of concern are made available to analysts using SAS^® Visual Analytics and SAS^® Visual Statistics. Finally, we discuss the applications of this methodology for overseeing government agencies and financial institutions alike.

A lot of time and effort goes into creating presentations or dashboards for the purposes of management business reviews. Data for the presentation is produced from a variety of tools, and the output is cut and pasted into Microsoft PowerPoint or Microsoft Excel. Time is spent not only on the data preparation and reporting, but also on the finishing and touching up of these presentations. In previous years, SAS^® Global Forum authors have described the automation capabilities of SAS^® and Microsoft Office. The default look and feel of SAS output in Microsoft PowerPoint and Microsoft Excel is not always adequate for the more polished requirement of an executive presentation. This paper focuses on how to combine the capabilities of SAS^® Enterprise Guide^®, SAS^® Visual Analytics, and Microsoft PowerPoint into a finished, professional presentation. We will build and automate a beautiful finished end product that can be refreshed by anyone with the click of a mouse.

Historically, the risk and finance functions within a bank have operated within different rule sets and structures. Within its function, risk enjoys the freedom needed to properly estimate various types of risk. Finance, on the other hand, operates within the well-defined and structured rules of accounting, which are required for standardized reporting. However, the International Financial Reporting Standards (IFRS) newest standard, IFRS 9, brings these two worlds together: risk, to estimate credit losses, and finance, to determine their impact on the balance sheet. To help achieve this integration, SAS^® has introduced SAS^® Expected Credit Loss. SAS Expected Credit Loss enables customers to perform risk calculations in a controlled environment, and to use those results for financial reporting within the same managed environment. The result is an integrated and scalable risk and finance platform, providing the end-to-end control, auditability, and flexibility needed to meet the IFRS 9 challenge.

Coming off a recent smart grid implementation, OGE Energy Corp. was collecting more data than at any time in its history. This data held the potential to help the organization uncover new insights and chart new paths. Find out how OGE Energy is building a culture of data analytics by using SAS^® tools, a distributed analytics model, and an analytics center of excellence.

When new technologies, workflows, or processes are implemented, an organization and its employees must embrace changes in order to ensure long-term success. This paper provides guidelines and best practices in change management that the SAS Advanced Analytics Division uses with customers when it implements prescriptive analytics solutions (provided by SAS/OR^® software). Highlights include engaging technical leaders in defining project scope and providing functional design documents. The paper also highlights SAS' approach in engaging business leaders on business scope, garnering executive-level project involvement, establishing steering committees, defining use cases, developing an effective communication strategy, training, and implementing of SAS/OR solutions.

This end-to-end capability demonstration illustrates how SAS^® Viya can aid intelligence, homeland security, and law enforcement agencies in counter radicalization. There are countless examples of agency failure to apportion significance to isolated pieces of information which, in context, are indicative of an escalating threat and require intervention. Recent terrorist acts have been carried out by radicalized individuals who should have been firmly on the organizational radar. Although SAS^® products enable analysis and interpretation of data that enables the law enforcement and homeland security community to recognize and triage threats, intelligence information must be viewed in full context. SAS Viya can rationalize previously disconnected capabilities in a single platform, empowering intelligence, security, and law enforcement agencies. SAS^® Visual Investigator provides a hub for SAS^® Event Stream Processing, SAS^® Visual Scenario Designer, and SAS^® Visual Analytics, combining network analysis, triage, and, by leveraging the mobile capability of SAS, operational case management to drive insights, leads, and investigation. This hub provides the capability to ingest relevant external data sources, and to cross reference both internally held data and, crucially, operational intelligence gained from normal policing activities. This presentation chronicles the exposure and substantiation of a radical network and informs tactical and strategic disruption.

For all business analytics projects big or small, the results are used to support business or managerial decision-making processes, and many of them eventually lead to business actions. However, executives or decision makers are often confused and feel uninformed about contents when presented with complicated analytics steps, especially when multi-processes or environments are involved. After many years of research and experiment, a web reporting framework based on SAS^® Stored Processes was developed to smooth the communication between data analysts, researches, and business decision makers. This web reporting framework uses a storytelling style to present essential analytical steps to audiences, with dynamic HTML5 content and drill-down and drill-through functions in text, graph, table, and dashboard formats. No special skills other than SAS^® programming are needed for implementing a new report. The model-view-controller (MVC) structure in this framework significantly reduced the time needed for developing high-end web reports for audiences not familiar with SAS. Additionally, the report contents can be used to feed to tablet or smartphone users. A business analytical example is demonstrated during this session. By using this web reporting framework based on SAS Stored Processes, many existing SAS results can be delivered more effectively and persuasively on a SAS^® Enterprise BI platform.

As an information security or data professional, you have seen and heard about how advanced analytics has impacted nearly every business domain. You recognize the potential of insights derived from advanced analytics to improve the information security of your organization. You want to realize these benefits, and to understand their pitfalls. To successfully apply advanced analytics to the information security business problem, proper application of data management processes and techniques is of paramount importance. Based on professional services experience in implementing SAS^® Cybersecurity, this session teaches you about the data sources used, the activities involved in properly managing this data, and the means to which these processes address information security business problems. You will come to appreciate how using advanced analytics in the information security domain requires more than just the application of tools or modeling techniques. Using a data management regime for information security concerns can benefit your organization by providing insights into IT infrastructure, enabling successful data science activities, and providing greater resilience by way of improved information security investigations.

This paper describes specific actions to be taken to increase the usability, data consistency, and performance of an advanced SAS^® Customer Intelligence solution for marketing and analytic purposes. In addition, the paper focuses on the establishment of a data governance program to support the processes that take place within this environment. This paper presents our experiences developing a data governance light program for the enterprise data warehouse and its sources as well as for the data marts created downstream to address analytic and campaign management purposes. The challenge was to design a data governance program for this system in 90 days.

The ever growing volume of data challenges us to keep pace in ensuring that we use it to its full advantage. Unfortunately, often our response to new data sources, data types, and applications is somewhat reactionary. There exists a misperception that organizations have precious little time to consider a purposeful strategy without disrupting business continuity. Strategy is a phrase that is often misused and ill-defined. However, it is nothing more than a set of integrated choices that help position an initiative for future success. This presentation covers the key elements defining data strategy. The following key topics are included: What data should we keep or toss? How should we structure data (warehouse versus data lake versus real-time event streaming)? How do we store data (cloud, virtualization, federation, cloud, Hadoop)? What is the approach we use to integrate and cleanse data (ETL versus cognitive/ automated profiling)? How do we protect and share data? These topics ensure that the organization gets the most value from our data. They explore how we prioritize and adapt our strategy to meet unanticipated needs in the future. As with any strategy, we need to make sure that we have a roadmap or plan for execution, so we talk specifically about the tools, technologies, methods, and processes that are useful as we design a data strategy that is both relevant and actionable to your organization.

Because many SAS^® users either work for or own companies that house big data, the threat that malicious software poses becomes even more extreme. Malicious software, often abbreviated as malware, includes many different classifications, ways of infection, and methods of attack. This E-Poster highlights the types of malware, detection strategies, and removal methods. It provides guidelines to secure essential assets and prevent future malware breaches.

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Investors usually trade stocks or exchange-traded funds (ETFs) based on a methodology, such as a theory, a model, or a specific chart pattern. There are more than 10,000 securities listed on the US stock market. Picking the right one based on a methodology from so many candidates is usually a big challenge. This paper presents the methodology based on the CANSLIM1 theorem and momentum trading (MT) theorem. We often hear of the cup and handle shape (C&H), double bottoms and multiple bottoms (MB), support and resistance lines (SRL), market direction (MD), fundamental analyses (FA), and technical analyses (TA). Those are all covered in CANSLIM theorem. MT is a trading theorem based on stock moving direction or momentum. Both theorems are easy to learn but difficult to apply without an appropriate tool. The brokers' application system usually cannot provide such filtering due to its complexity. For example, for C&H, where is the handle located? For the MB, where is the last bottom you should trade at? Now, the challenging task can be fulfilled through SAS^®. This paper presents the methods on how to apply the logic and graphically present them though SAS. All SAS users, especially those who work directly on capital market business, can benefit from reading this document to achieve their investment goals. Much of the programming logic can also be adopted in SAS finance packages for clients.

This paper describes an effective real-time contextual marketing system based on a successful case implemented in a private communication company in Chile. Implementing real-time cases is becoming a major challenge due to stronger competition, which generates an increase of churn and higher operational costs, among other issues. All of these can have an enormous effect on revenue and profit. A set of predictive machine learning models can help to improve response rates of outbound campaigns, but it s not enough to be more proactive in this business. Our real-time system for contextual marketing uses the two SAS^® solutions: SAS^® Event Stream Processing and SAS^® Real-Time Decision Manager, which are connected in cascade. In this configuration, SAS Event Stream Processing can read massive amounts of data from call detail records (CDRs) and antennas, and SAS Real-Time Decision Manager receives the resulting golden events, which trigger the right responses. Time elapsed from the detection of a golden event until a response is processed is approximately 5 seconds. Since implementing seven use cases of this real-time system, the results show an average augmentation in revenue of two million dollars in a testing period of four months, thus returning the investment in a short-term period. The implementation of this system has changed the way Telef nica Chile generates value from big data. Moreover, an outstanding, long-term working relationship between Telef nica Chile and SAS has been started.

In this panel session, professors from three geographically diverse universities explain what makes for an effective partnership with private sector companies. Specific examples are discussed from health care, insurance, financial services, insurance, and retail. The panelists discuss what works, what doesn t, and what both parties need to be prepared to bring to the table for a long-term, mutually beneficial partnership.

As a retailer, have you ever found yourself reviewing your last season's assortment and wondering, What should I have carried in my assortment ? You are constantly faced with the challenge of product selection, placement, and ensuring your assortment will drive profitable sales. With millions of consumers, thousands of products, and hundreds of locations, this question can often times be challenging and overwhelming. With the rise in omnichannel, traditional approaches just won't cut it to gain the insights needed to maximize and manage localized assortments as well as increase customer satisfaction. This presentation explores applications of analytics within marketing and merchandising to drive assortment curation as well as relevancy for customers. The use of analytics can not only increase efficiencies but can also give insights into what you should be buying, how best to create a profitable assortment, and how to engage with customers in-season to drive their path to purchase. Leveraging an analytical infrastructure to infuse analytics into the assortment management process can help retailers achieve customer-centric insights, in a way that is easy to understand, so that retailers can quickly take insights to actions and gain the competitive edge.

SAS^® BI Dashboard is an important business intelligence and data visualization product used by many customers worldwide. They still rely on SAS BI Dashboard for performance monitoring and decision support. SAS^® Visual Analytics is a new-generation product, which empowers customers to explore huge volumes of data very quickly and view visualized results with web browsers and mobile devices. Since SAS Visual Analytics is used by more and more regular customers, some SAS BI Dashboard customers might want to migrate existing dashboards to SAS Visual Analytics to take advantage of new technologies. In addition, some customers might hope to deploy the two products in parallel and keep everyone on the same page. Because the two products use different data models and formats, a special conversion tool is developed to convert SAS BI Dashboard dashboards into SAS Visual Analytics dashboards and reports. This paper comprehensively describes the guidelines, methods, and detailed steps to migrate dashboards from SAS BI Dashboard to SAS Visual Analytics. Then the converted dashboards can be shown in supported viewers of SAS Visual Analytics including mobile devices and modern browsers.

Banks can create a competitive advantage in their business by using business intelligence (BI) and by building models. In the credit domain, the best practice is to build risk-sensitive models (Probability of Default, Exposure at Default, Loss Given Default, Unexpected Loss, Concentration Risk, and so on) and implement them in decision-making, credit granting, and credit risk management. There are models and tools on the next level that are built on these models and that are used to help in achieving business targets, setting risk-sensitive pricing, capital planning, optimizing Return on Equity/Risk Adjusted Return on Capital (ROE/RAROC), managing the credit portfolio, setting the level of provisions, and so on. It works remarkably well as long as the models work. However, over time, models deteriorate, and their predictive power can drop dramatically. As a result, heavy reliance on models in decision-making (some decisions are automated following the model's results-without human intervention) can result in a huge error, which might have dramatic consequences for the bank's performance. In my presentation, I share our experience in reducing model risk and establishing corporate governance of models with the following SAS^® tools: SAS^® Model Monitoring Microservice, SAS^® Model Manager, dashboards, and SAS^® Visual Analytics.

This presentation has the objective to present a methodology for interest rates, life tables, and actuarial calculations using generational mortality tables and the forward structure of interest rates for pension funds, analyzing long-term actuarial projections and their impacts on the actuarial liability. It was developed as a computational algorithm in SAS^® Enterprise Guide^® and Base SAS^® for structuring the actuarial projections and it analyzes the impacts of this new methodology. There is heavy use of the IML and SQL procedures.

A successful conversion to the International Financial Reporting Standards (IFRS) standard known as IFRS 9 can present many challenges for a financial institution. We discuss how leveraging best practices in project management, accounting standards, and platform implementation can overcome these challenges. Effective project management ensures that the scope of the implementation and success criteria are well defined. It captures all major decision points and ensures thorough documentation of the platform and how its unique configuration ties back directly to specific business requirements. Understanding the nuances of the IFRS 9 standard, specifically the impact of bucketing all financial assets according to their cash flow characteristics and business models, is crucial to ensuring the design of an efficient and robust reporting platform. Credit impairment is calculated at the instrument level, and can both improve or deteriorate. Changes in the level of credit impairment of individual financial assets enters the balance sheet as either an amortized cost, other comprehensive income, or fair value through profit and loss. Introducing more volatility to these balances increases the volatility in key financial ratios used by regulators. A robust and highly efficient platform is essential to process these calculations, especially under tight reporting deadlines and the possibility of encountering challenges. Understanding how the system is built through the project documentatio

This paper establishes the conceptualization of the dimension of the shopping cart (or market basket) on apparel retail websites. It analyzes how the cart dimension (describing anonymous shoppers) and the customer dimension (describing non-anonymous shoppers) impact merchandise return behavior. Five data-mining techniques-namely logistic regression, decision tree, neural network, gradient boosting, and support vector machine-are used for predicting the likelihood of merchandise return. The target variable is a dichotomous response variable: return vs not return. The primary input variables are conceptualized as constituents of the cart dimension, derived from engineering merchandise-related variables such as item style, item size, and item color, as well as free-shipping-related thresholds. By further incorporating the constituents of the customer dimension such as tenure, loyalty membership, and purchase histories, the predictive accuracy of the model built using each of the five data-mining techniques was found to improve substantially. This research also highlights the relative importance of the constituents of the cart and customer dimensions governing the likelihood of merchandise return. Recommendations for possible applications and research areas are provided.

Knowing which SAS^® products are being used in your organization, by whom, and how often helps you decide whether you have the right mix and quantity licensed. These questions are not easy to answer. We present an innovative technique using three SAS utilities to answer these questions. This paper includes example code written for Linux that can easily be modified for Windows and other operating systems.

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Data is a valuable corporate asset that, when managed improperly, can detract from a company's ability to achieve strategic goals. At 1-800-Flowers.com, Inc. (18F), we have embarked on a journey toward data governance through embracing Master Data Management (MDM). Along the path, we've recognized that in order to protect and increase the value of our data, we must take data quality into consideration at all aspects of data movement in the organization. This presentation discusses the ways that SAS^® Data Management is being leveraged by the team at 18F to create and enhance our data quality strategy to ensure data quality for MDM.

Oi S.A. (Oi) is a pioneer in providing convergent services in Brazil. It currently has the greatest network capillarity and WiFi availability Brazil. The company offers fixed lines, mobile services, broadband, and cable TV. In order to improve service to over 70 million customers, The Customer Intelligence Department manages the data generated by 40,000 call center operators. The call center produces more than a hundred million records per month, and we use SAS^® Visual Analytics to collect, analyze, and distribute these results to the company. This new system changed the paradigm of data analysis in the company. SAS Visual Analytics is user-friendly and enabled the data analysis team to reduce IT time. Now it is possible to focus on business analysis. Oi started developing its SAS Visual Analytics project in June 2014. The test period lasted only 15 days and involved 10 people. The project became relevant to the company. It led us to the next step, in which 30 employees and 20 executives used the tool. During the last phase, we applied that to a larger scale with 300 users, including local managers, executives, and supervisors. The benefits brought by the fast implementation (two months) are many. We reduced the time it takes to produce reports by 80% and the time to complete business analysis by 40%.