The holiday season is over – and you survived. You’ve made a lot of personal resolutions for 2017 - go to the gym, eat less sugar, save more money, visit Grandma more often. These are all great personal resolutions for 2017, but what about your analytics resolutions? If you are having trouble with your analytics resolutions then let us help you out. The recent release of SAS 9.4 M4 will help you make 2017 your best analytics year yet.
Resolution 1: Build more accurate models faster!
Now you will be able to leverage the power of the two most advanced analytics platforms on the market, SAS 9 and SAS Viya from one interface. Using SAS/Connect, users can call powerful SAS Viya analytics from within a process flow in Enterprise Miner. Would you prefer to use the super-fast, autotuned gradient boosting in SAS Viya? No problem! Call SAS Viya analytics directly from Enterprise Miner using the SAS Viya Code node. Then, from the same process flow you can also call open source models, all from one interface, SAS Enterprise Miner. Do you prefer to use SAS Studio on SAS 9? You will also be able to call SAS Viya analytics from SAS Studio as well. With SAS 9 M4, SAS gives you the ability to use both of SAS’ powerful platforms from one interface.
Resolution 2: Score your unstructured models in Hadoop without moving your data!
Got Hadoop? Got a lot of unstructured data? Now SAS Contextual Analysis allows you to score models in Hadoop using the SAS Code Accelerator add-on. Identify new insights with your unstructured text without ever having to move your data. Score it all in Hadoop. Uncover new trends and topics buried in documents, emails, social media and other unstructured text that is stored in Hadoop. You will be able to do it faster because you won’t have to move that data outside of Hadoop. SAS just keeps getting better in 2017.
Resolution 3: Make better forecasts using the weather!
Through SAS/ETS, econometricians and others wanting to incorporate weather data into their models can now do so directly through two new interface engines. SASERAIN enables SAS users to retrieve weather data from the World Weather Online website. And SASENOAA provides access to severe weather data from the National Oceanic and Atmospheric Administration (NOAA) Severe Weather Data Inventory (SWDI) web service. So now you’ll know why there was that big sales spike for rock salt and snow shovels in July! Who says there is no climate change in 2017?
Resolution 4: Estimate causal effects more efficiently!
The new CAUSALTRT procedure in SAS/STAT estimates the average causal effect of a binary treatment variable T on a continuous or discrete outcome Y. Depending on the application, the variable T can represent an intervention (such as smoking cessation – which is a great 2017 resolution - versus control), an exposure to a condition (such as attending private versus public schools), or an existing characteristic of subjects (such as high versus low socioeconomic status). The CAUSALTRT procedure estimates two types of causal effects: the average treatment effect and the average treatment effect for the treated. And best of all, the causal inference methods that the CAUSALTRT procedure implements are designed primarily for use with data from nonrandomized trials or observational studies, where you observe T and Y without assigning subjects randomly to the treatment conditions.
Resolution 5: Design better factory floors!
A factory floor can be a complicated place, with raw materials coming in one side, and finished products going out the other. Options are virtually unlimited for the placement of materials and equipment – and a poorly designed layout can dramatically reduce production capability. Yet experimenting with different layouts would be extremely costly and time consuming. Thankfully, SAS Simulation Studio (a component of SAS/OR) provides a rich – and animated – environment for testing alternatives and coming up with the most appropriate design. And it can handle any kind of discrete-event simulation, integrating with JMP for experimental design and input analysis, and with JMP and SAS for source data and analysis of simulation results. How will your factory floor simulation impact your productivity in 2017?