The Clinical Data Interchange Standards Consortium (CDISC) encompasses a variety of standards for medical research. Amongst the several standards developed by the CDISC organization are standards for data collection (Clinical Data Acquisition Standards Harmonization CDASH), data submission (Study Data Tabulation Model SDTM), and data analysis (Analysis Data Model ADaM). These standards were originally developed with drug development in mind. Therapeutic Area User Guides (TAUGs) have been a recent focus to provide advice, examples, and explanations for collecting and submitting data for a specific disease. Non-subjects even have a way to collect data using the Associated Persons Implementation Guide (APIG). SDTM domains for medical devices were published in 2012. Interestingly, the use of device domains in the TAUGs occurs in 14 out of 18 TAUGs, providing examples of the use of various device domains. Drug-device studies also provide a contrast on adoption of CDISC standards for drug submissions versus device submissions. Adoption of SDTM in general and the seven device SDTM domains by the medical device industry has been slow. Reasons for the slow adoption are discussed in this paper.

Often the burden of productionisation of analytical models falls upon the analyst, so every Monday morning the analyst comes in and presses the Run button. Now this is obviously fraught with danger (for example, the source data isn't available, the analyst goes on holidays, or the analyst resigns), and might lead to invalid results being consumed by downstream systems. There are many reasons that this might occur, but the most common one is that it takes IT too long to put a model into full production (especially if that model contains new data sources). In this presentation, I show a tested architecture that allows for the typical rapid development of models (and in fact it actually significantly speeds up the discovery phase), as well as allows for an orderly handover to IT for them to productionise without disrupting the regular run of the models. This allows for notification of downstream users if there is a delay in the arrival of data, as well as rapid IT Operations response if there is a problem during the loading and creation.

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.

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.

Organizations that create and store personally identifiable information (PII) are often required to de-identify sensitive data to protect an individual s privacy. There are multiple methods in SAS^® that can be used to de-identify PII depending on data types and encryption needs. The first method is to apply crosswalk mapping by linking a data set with PII to a secured data set that contains the PII and its corresponding surrogate. Then, the surrogate replaces the PII in the original data set. A second method is SAS encryption, which involves translating PII into an encrypted string using SAS functions. This could be a one-byte-to-one-byte swap or a one-byte-to-two-byte swap. The third method is in-database encryption, which encrypts the PII in a data warehouse, such as Oracle and Teradata, using SAS tools before any information is imported into SAS for users to see. This paper discusses the advantages and disadvantages of these three methods, provides sample SAS code, and describes the corresponding methods to decrypt the encrypted data.

Read the paper (PDF) | View the e-poster or slides (PDF)