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.

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.

Learn how SAS^® works with analytics, big data, and the cloud all in one product: SAS^® Analytics for Containers. This session describes the architecture of containers running in the public, private, or hybrid cloud. The reference architecture also shows how SAS leverages the distributed compute of Hadoop. Topics include how SAS products such as Base SAS^®, SAS/STAT^® software, and SAS/GRAPH^® software can all run in a container in the cloud. This paper discusses how to work with a SAS container running in a variety of Infrastructure as a Service (IaaS) models, including Amazon Web Services and OpenStack cloud. Additional topics include provisioning web-browser-based clients via Jupyter Notebooks and SAS^® Studio to provide data scientists with the tool of their choice. A customer use case is discussed that describes how SAS Analytics for Containers enables an IT department to meet the ad hoc, compute-intensive, and scaling demands of the organization. An exciting differentiator for the data scientist is the ability to send some or all of the analytic workload to run inside their Hadoop cluster by using the SAS accelerators for Hadoop. Doing so enables data scientists to dive inside the data lake and harness the power of all the data.

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.

SAS^® 9.4 provides three ways to upgrade: upgrade in place, automated migration with the SAS^® Migration Utility, and partial promotion. This session focuses primarily on the different techniques and best practices for each. We also discuss the pros and cons of using the SAS Migration Utility and what is required for migrating users' content like projects, data, and code.

SAS^® migrations are the number one reason why SAS architects and administrators are fired. Even though this bold statement is not universally true, it has been at the epicenter of many management and technical discussions at UnitedHealth Group. The competing business forces between the desire to innovate and to provide platform stability drive difficult discussions between business leaders and IT partners that tend to result in a frustrated user-base, flustered IT professionals, and a stale SAS environment. Migrations are the antagonist of any IT professional because of the disruption, long hours, and stress that typically ensues. This paper addresses the lessons learned from a SAS migration from the first maintenance release of SAS^® 9.4 to the third maintenance release of SAS^® 9.4 on a technically sophisticated enterprise SAS platform including clustered metadata servers, clustered middle-tier, SSL, an IBM Platform Load Sharing Facility (LSF) grid, and SAS^® Visual Analytics.

Moving a workforce in a new direction takes a lot of energy. Your planning should include four pillars: culture, technology, process, and people. These pillars assist small and large SAS^® rollouts with a successful implementation and an eye toward future proofing. Boston Scientific is a large multi-national corporation that recently grew SAS from a couple of desktops to a global implementation. Boston Scientific's real world experiences reflect on each pillar, both in what worked and in lessons learned.

Installation and configuration of a SAS^® Enterprise BI platform in the requirements of the today's world requires knowledge on a wide variety of subjects. Security requirements are growing, the number of involved components is growing, time to delivery should be shorter, and the quality must be increased. The expectations of the customers are based on a cloud experience where automated deployments with ready-to-use applications are state of the art. This paper describes an approach to address the challenges to deploy SAS^® 9.4 on Linux to meet today's customer expectations.