SAS^® has been an early leader in big data technology architecture that more easily integrates unstructured files across multi-tier data system platforms. By using SAS^® Data Integration Studio and SAS^® Enterprise Business Intelligence software, you can easily automate big data using SAS^® system accommodations for Hadoop open-source standards. At the same time, another seminal technology has emerged, which involves real-time multi-sensor data integration using Arduino microprocessors. This break-out session demonstrates the use of SAS^® 9.4 coding to define Hadoop clusters and to automate Arduino data acquisition to convert custom unstructured log files into structured tables, which can be analyzed by SAS in near real time. Examples include the use of SAS Data Integration Studio to create and automate stored processes, as well as tips for C language object coding to integrate to SAS data management, with a simple temperature monitoring application for Hadoop to Arduino using SAS.

In-database processing refers to the integration of advanced analytics into the data warehouse. With this capability, analytic processing is optimized to run where the data reside, in parallel, without having to copy or move the data for analysis. From a data governance perspective there are many good reasons to embrace in-database processing. Many analytical computing solutions and large databases use this technology because it provides significant performance improvements over more traditional methods. Come learn how Blue Cross Blue Shield of Tennessee (BCBST) uses in-database processing from SAS and Teradata.

Well, Hadoop community, now that you have your data in Hadoop, how are you staging your analytical base tables? In my discussions with clients about this, we all agree on one thing: Data sizes stored in Hadoop prevent us from moving that data to a different platform in order to generate the analytical base tables. To address this dilemma, I want to introduce to you the SAS^® In-Database Code Accelerator for Hadoop.

Researchers, patients, clinicians, and other health-care industry participants are forging new models for data-sharing in hopes that the quantity, diversity, and analytic potential of health-related data for research and practice will yield new opportunities for innovation in basic and translational science. Whether we are talking about medical records (for example, EHR, lab, notes), administrative data (claims and billing), social (on-line activity), behavioral (fitness trackers, purchasing patterns), contextual (geographic, environmental), or demographic data (genomics, proteomics), it is clear that as health-care data proliferates, threats to security grow. Beginning with a review of the major health-care data breeches in our recent history, we highlight some of the lessons that can be gleaned from these incidents. In this paper, we talk about the practical implications of data sharing and how to ensure that only the right people have the right access to the right level of data. To that end, we explore not only the definitions of concepts like data privacy, but we discuss, in detail, methods that can be used to protect data--whether inside our organization or beyond its walls. In this discussion, we cover the fundamental differences between encrypted data, 'de-identified', 'anonymous', and 'coded' data, and methods to implement each. We summarize the landscape of maturity models that can be used to benchmark your organization's data privacy and protection of sensitive data.

Read the paper (PDF). | Watch the recording.

Streaming data is becoming more and more prevalent. Everything is generating data now--social media, machine sensors, the 'Internet of Things'. And you need to decide what to do with that data right now. And 'right now' could mean 10,000 times or more per second. SAS^® Event Stream Processing provides an infrastructure for capturing streaming data and processing it on the fly--including applying analytics and deciding what to do with that data. All in milliseconds. There are some basic tenets on how SAS^® provides this extremely high-throughput, low-latency technology to meet whatever streaming analytics your company might want to pursue.

Read the paper (PDF). | Watch the recording.

Is your company using or considering using SAP Business Warehouse (BW) powered by SAP HANA? SAS^® provides various levels of integration with SAP BW in an SAP HANA environment. This integration enables you to not only access SAP BW components from SAS, but to also push portions of SAS analysis directly into SAP HANA, accelerating predictive modeling and data mining operations. This paper explains the SAS toolset for different integration scenarios, highlights the newest technologies contributing to integration, and walks you through examples of using SAS with SAP BW on SAP HANA. The paper is targeted at SAS and SAP developers and architects interested in building a productive analytical environment with the help of the latest SAS and SAP collaborative advancements.

The SAS^® LASR™ Analytic Server acts as a back-end, in-memory analytics engine for solutions such as SAS^® Visual Analytics and SAS^® Visual Statistics. It is designed to exist in a massively scalable, distributed environment, often alongside Hadoop. This paper guides you through the impacts of the architecture decisions shared by both software applications and what they specifically mean for SAS^®. We then present positive actions you can take to rebound from unexpected outages and resume efficient operations.

A maximum harvest in farming analytics is achieved only if analytics can also be operationalized at the level of core business applications. Mapped to the use of SAS^® Analytics, the fruits of SAS be shared with Enterprise Business Applications by SAP. Learn how your SAS environment, including the latest of SAS^® In-Memory Analytics, can be integrated with SAP applications based on the SAP In-Memory Platform SAP HANA. We'll explore how a SAS^® Predictive Modeling environment can be embedded inside SAP HANA and how native SAP HANA data management capabilities such as SAP HANA Views, Smart Data Access, and more can be leveraged by SAS applications and contribute to an end-to-end in-memory data management and analytics platform. Come and see how you can extend the reach of your SAS^® Analytics efforts with the SAP HANA integration!

In 2012, the Obama campaign used advanced analytics to target voters, especially in social media channels. Millions of voters were scored on models each night to predict their voting patterns. These models were used as the driver for all campaign decisions, including TV ads, budgeting, canvassing, and digital strategies. This presentation covers how the Obama campaign strategies worked, what's in store for analytics in future elections, and how these strategies can be applied in the business world.

Read the paper (PDF). | Watch the recording.

Managing and organizing external files and directories play an important part in our data analysis and business analytics work. A good file management system can streamline project management and file organizations and significantly improve work efficiency . Therefore, under many circumstances, it is necessary to automate and standardize the file management processes through SAS^® programming. Compared with managing SAS files via PROC DATASETS, managing external files is a much more challenging task, which requires advanced programming skills. This paper presents and discusses various methods and approaches to managing external files with SAS programming. The illustrated methods and skills can have important applications in a wide variety of analytic work fields.