SAS Viya

1月 122017
 

analytics resolutionsThe 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?

tags: analytics, SAS 9.4, SAS Viya

Five great analytics resolutions for 2017 was published on SAS Users.

1月 062017
 

Much of my recent work has been along the theme of modernization. Analytics is not new for many of our customers, but standing still in this market is akin to falling behind. In order to continue to innovative and remain competitive, organizations need to be prepared to embrace new technologies […]

The seven traits of a modern analytical platform was published on SAS Voices.

10月 202016
 

The study of social networks has gained importance over the years within social and behavioral research on HIV and AIDS. Social network research can show routes of potential viral transfer, and be used to understand the influence of peer norms and practices on the risk behaviors of individuals. This example analyzes the […]

Analyzing social networks using Python and SAS Viya was published on SAS Voices.

9月 152016
 

“What we are experiencing from analytics today is nothing short of a revolution,” said CEO Jim Goodnight, who spoke at Analytics Experience 2016 and set the stage for the conference’s executive panel. “Right now, my primary mission is to ensure people understand the limitless possibilities that lie before us, given […]

An analytics revolution underway: Insights from Analytics Experience 2016 was published on SAS Voices.

7月 302016
 

I’m an avid open water swimmer. In order to succeed in open water races I must do two things: I must sight properly in order to swim the straightest line possible in the right direction. The straighter the line, the less I have to swim. I must use a powerful, […]

Navigating the open waters of machine learning was published on SAS Voices.

5月 112016
 

SASViyaI’ve been giving presentations about SAS® ViyaTM for a couple of months now, and the reactions have been positive. I’m part of a much bigger cast of speakers at SAS who talk about the new analytics platform with key customers and analysts. While some presenters focus on the overarching benefits or the integration with previous versions of SAS, I get to focus on hands-on demonstrations. My style is to show SAS Viya technology in use, which helps attendees understand the different user experiences.

The demonstrations I show use anywhere from 90,000 to 2.5 million observations. Sometimes I’m looking for fraud or sometimes I’m looking for opportunities to reduce attrition. No matter what business problem I’m trying to solve, I like to show four different ways to tackle the problem, depending on the user persona and skillsets.

First, I show SAS® Visual Statistics. This is a suitable interface for business analysts and citizen data scientists. I can point and click to do a logistic regression and find an answer. Or, I can start to explore my data with SAS® Visual Analytics before I do any modeling.

Running a logistic regression in SAS Visual Statistics

Running a logistic regression in SAS Visual Analytics (click to enlarge).

Next, I show SAS® Studio, where you can also point and click, or you can program SAS code. You can do both here, or toggle back and forth, which provides a lot of benefits when reviewing how your code works. Here, I run a logistic regression, write the procedure and use PROC LOGSELECT, which is the logistic procedure in SAS Viya. I also point out all of the exploratory and descriptive tasks and procedures you can use before fitting your model.

StudioLogistic (002)

SAS Studio shows the selected model and the code side by side (click to enlarge).

Then, I tab over to Jupyter Notebook and show the CAS Python API. (CAS is short for cloud analytic server.) I can write Python code that calls specific CAS actions, like calling the logistic action set or performing model assessment. The CAS Python API is not yet released but will be available later this year.

SAS code in Python

Performing a model assessment from inside Jupyter Notebook.

Finally, I like to show these prepackaged predictive models that run SAS through APIs. Using analytics as a service by SAS, these APIs can be used to embed logistic regression or other modeling techniques into any other application.

A3s (002)

APIs are portable pieces of code that can be easily combined and stacked together to enhance other applications or websites (click to enlarge).

I use the same data for all of the examples and because the same CAS action set is used for analysis you get the same results. For each one, SAS Viya goes right to the data and lifts it up into memory. I can perform my analyses quickly and nimbly, and when I’m done, the data dribbles back down to its initial location instead of proliferating copies of the data on hard disks.

Not only is it quick and easy to get complex answers – but you can get them in whatever way feels most comfortable to you.

Which of the four do you prefer? And what ideas do the different options inspire in you?

tags: Jupyter Notebook, Python, SAS Viya

Choose your own adventure with SAS Viya was published on SAS Users.

4月 222016
 

SAS Global ForumImpressive innovations and exciting announcements took center stage (literally) at Opening Session of SAS Global Forum 2016. Near the end of the session, SAS CEO Jim Goodnight shared news about SAS’ new architecture that had everyone abuzz.

SAS® Viya™ - There’s a new headliner in Vegas

“We are unveiling a quantum leap forward in making analytics easier to use and accessible to everyone,” Goodnight said. “It’s a major breakthrough and it’s called SAS Viya.”

Goodnight was also quick to point out that SAS Viya will work with customers’ existing SAS 9 software.

Goodnight invited Vice President of Analytic Server Research and Development Oliver Schabenberger, who led the development work for SAS Viya, to join him on stage to discuss the new cloud-based analytic and data management architecture.

Jim Goodnight makes some exciting announcements at SAS Global Forum 2016 Opening Session

Jim Goodnight shares exciting announcements at SAS Global Forum 2016 Opening Session

“We see great diversity in the ways our customers approach and consume analytics,” Schabenberger explained. “From small data to big data. From simple analytics to the toughest machine learning problems. Data in motion and data at rest. Structured and unstructured data. Single users and hundreds of concurrent users. In the cloud and on premises. Data scientists and business users.”

SAS has developed a truly unified and integrated modern environment that everyone can use, whether you are a data scientist or a business analyst. “The beauty of SAS Viya is that it’s unified, open, simple and powerful, and built for the cloud,” said Schabenberger. “Today we are moving to a multi-cloud architecture.”

Goodnight encouraged customers to be “sure to try it out. I think you will enjoy the new SAS Viya.”

The SAS Viya procedural interface will be available to early adopters in 30 days, with visual interfaces scheduled for a September release. Customers can apply to be part of the SAS Viya early preview program.

SAS Customer Intelligence 360 and SAS Analytics for IoT announced

SAS Viya wasn’t the only “star” of the evening.

Goodnight lauded the company’s continuing efforts to globalize and expand ways to make our software faster and easier to use. On the development side, he highlighted SAS Customer Intelligence 360, SAS® Forecast Studio, SAS® Event Stream Processing, SAS® Cybersecurity and the next generation of high performance analytics.

Executive Vice President and SAS Chief Revenue Officer Carl Farrell took the stage to share examples of the many diverse uses of SAS. “Today, our customers are so much more educated on big data and analytics,” Farrell said. “CEOs are realizing that analytics can help them draw more value for their business around that data.”

Farrell singled out several customers including Idea Cellular Ltd. in India, which is processing a billion transactions a day -- something that was impossible before high performance analytics – and Macy’s customer intelligence project that is focused on making real-time offers to customers as they walk through a store, creating a personal and immediate experience.

Farrell also said he was so proud of the SAS work being done outside of business, in the data for good realm, specifically mentioning work in Chile combatting the Zika virus and the work of the Black Dog Institute, which conducts research to improve the lives of people with mental illness.

“Our customers are doing amazing things with SAS that we couldn’t have imagined 40 years ago, and this is just the tip of the iceberg and there’s so much more to come,” Farrell said.

Jeromey Farmer accepts the 2016 User Feedback Award from Annette Harris.

Jeromey Farmer accepts the 2016 User Feedback Award from Annette Harris,

Speaking of stars, Senior Vice President of Technical Support Annette Harris applauded the SAS Super Users for their work in support communities. “SAS users have a rich tradition of helping each other in peer-to-peer forums,” said Harris.

Harris also recognized the 2016 SAS User Feedback Award winner, Jeromey Farmer, a Treasury Officer from the Federal Reserve Bank of St. Louis, noting that SAS gained strong insights from Farmer into how SAS can more seamlessly integrate in a complex and secure environment.

SAS Executive Vice President and Chief Marketing Officer Randy Guard took the stage to announce SAS® Analytics for IoT and to talk about some macro trends he is seeing, including the digital transformation taking place in business and technology. He cited an IDC report that stated by the end of 2017, two-thirds of all CEOs will have digital transformation – across their company – at the top of their agenda.

Customers want help in managing their data, including streaming data, and want analytics embedded in their applications, he added. He calls the latter “analytics any way you want it.”

Customers also want software as a service, including self-service, and want to know how to monetize the connectivity and continuous load of data. “That hits our sweet spot in analytics at SAS,” he said. “The transformation is under way and we are investing money to make this transition smoother for our customers.”

40 and Forward

Woven throughout Opening Session were references to SAS’ 40 years in business.

Asked about what has changed over the years, Goodnight recalled that when SAS started, there was one product on a single machine. Now we have more than 200 products on dozens of machines. Back then, a computer could process about 500 instructions a second. Now it’s up to 2 to 3 billion instructions a second. The very first disk drives were two feet across, with tapes containing about five million bytes. Now we can get 1.2 terabytes in the size of a K-cup.

As for key milestones over the 40 years, Goodnight said two things came to mind. One was the introduction of multivendor architecture in the mid-1980s so our software could run on all platforms, and the other was the advent of massively parallel computing.

Not surprisingly, given the milestone anniversary year for SAS, the Opening Session ended with a video retrospective looking back on world news from the 1970s through today, with a cameo appearance by Goodnight from the early days of SAS.

If you want to view a recording of Opening Session, visit the SAS Global Forum Video Portal.

tags: SAS Analytics for IoT, SAS Customer Intelligence 360, SAS Global Forum, SAS Viya

Highlights from SAS Global Forum: Opening Session was published on SAS Users.

4月 202016
 

As we look at the last 40 years of innovation using analytics, it can be both humbling and inspiring. I mean, who would have anticipated 40 years ago that SAS® would be used to analyze genomic data and help develop specialized medications as a result? Who would have guessed that […]

How to embed advanced analytics in your biggest ideas? was published on SAS Voices.