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FACT SHEET
SAS® Analytics for IoT
Empower your business users to quickly derive value from IoT investments
What does SAS® Analytics for IoT do?
SAS Analytics for IoT offers a proven way for business users to organize and act on high volumes of diverse IoT data using a secure, flexible and scalable IoT analytics solution.
It provides a single version of the data for a wide variety of users across the organization. So nontechnical users can quickly extract value from IoT investments − in days, not months.
Why is SAS® Analytics for IoT important?
Progress from predictive to prescriptive analytics has been slow as companies struggle with data complexities and with how artificial intelligence (AI) and machine learning (ML) can coexist with existing statistical models. SAS Analytics for IoT solves the data complexity issue across the entire analytics life cycle with a sensor-based data model, streamlined ETL and a unified, business-focused data selection interface. This can translate to millions saved, as it reduces unplanned downtime, improves operational efficiencies and creates opportunities for differentiated customer experiences.
For whom is SAS® Analytics for IoT designed?
It is designed for business users, engineers, data scientists and IT professionals across a range of industries, including manufacturing, consumer packaged goods, energy and retail. The software extends the power of analytics across your enterprise, enabling collaboration across the entire IoT analytics life cycle.
To accurately address compelling business and operational performance challenges, organizations need fast access to high- volume, diverse IoT data that they can transform into insights. Without it, they may not be able to respond quickly enough to market conditions, operational issues and customer requirements. Unfortunately, most organizations don’t have a common, sensor- focused data model that could empower all types of users to conduct analysis and act on new insights.
It’s a frustrating issue for business users and data scientists alike. Nontechnical users lack the skills required to manage high volumes of diverse IoT data − such as extensive coding, SQL or scripting knowledge. In most cases, IT has to provision data for business users. And business users wait in line for IT to create their data sources before they get data in the right form for analytics.
SAS Analytics for IoT empowers the enter- prise − from line-of-business users to engi- neers and IT professionals − to analyze sensor data and make fast, confident deci- sions that drive business performance. The intuitive, visual interface of SAS Analytics for IoT is built on SAS® Viya®, making it easy for business users to quickly select, launch, trans- form and operationalize data without coding − and without help from IT or a data scientist. The software runs in a fast, in-memory distrib- uted environment, giving users relevant results that can immediately drive better business insights.
Key Benefits
• AcceleratetimetovaluefromIoT investments − in days, not months. No specialized skills or coding are required to access, explore, visualize and trans- form data. Tasks are defined within a unified visual experience. Data selections can be called directly from other solu- tions (SAS, third party and open source)
to provide the latest data and make the most of your existing IoT investments.
• BroadenanddeepenIoTanalytics
use and collaboration across your enterprise. By streamlining ETL tasks and providing an extensible, sensor- based data model with an easy-to-use interface, all types of users (business, engineering, data science and IT) can access, explore, visualize and transform data into insights. As a result, there’s more effective collaboration and timely decision making across the organization.
• OptimizeyourentireecosystemofIoT solutions. Integration is hard if you don’t have the right methodology, the appro- priate underlying technology, and a scalable, flexible and secure solution
as the foundation. With SAS Analytics for IoT, you can prepare, organize, select and launch IoT data from an integrated business-focused interface into other solutions (SAS, third party and open source).

Product Overview
With the volume, velocity and variety of IoT data available today, users need to curate data to answer specific questions. This requires different views of the data, which often needs to be examined in different ways, multiple times a day. Even when IT has prepared and cleansed the data first, analysts still need to iteratively examine and prepare it further for their unique needs.
Edge-to-enterprise enabled, SAS Analytics for IoT uses an industry-leading streaming execution engine with AI to perform real- time analytics that drive timely and accurate decision making. This approach helps business users, engineers, data scientists and IT professionals who support critical processes and are working to achieve digital transformation. With streamlined ETL tasks and an extensible, sensor-based data model, users can perform ad hoc analysis and
analytics system development
in a self-service environment − without knowledge of the underlying data structure. By enabling diverse users to prepare, organize, select and launch IoT data from an integrated, business-focused interface into other solutions (SAS, third party and open source), the software optimizes IoT investments and skills − extending the power of IoT analytics and collaboration across the enterprise.
Easy-to-use IoT analytics
capabilities
With SAS Analytics for IoT, it’s easy to access, organize, select and transform your IoT data − without relying on IT or data scientist skills. Variables, attributes and hierarchies are automatically loaded into the data selection interface and presented in business terms. Smart filters, predefined time windows and other capabilities allow business users to reduce errors and get the exact data
they need.
Flexibility, speed and scale
IoT data can fuel business performance − but only if you can act on the data in a timely and accurate way. SAS Analytics for IoT has
a proven streaming execution engine with
AI capabilities that provides real-time analytics across the analytics life cycle, supporting rapid, confident decision making. Our secure, flexible and scalable IoT analytics solution can accelerate results from all your IoT initiatives.
Interoperability that
supports growth
IoT is all about the ecosystem. To extract the full value from your IoT ecosystem invest- ments, your analytics platform must support a complex, diverse environment. With SAS, users can embed open source code within an analysis and call algorithms seamlessly. Whether it’s Python, R, Java, Lua or Scala, modelers and data scientists can access SAS capabilities from their preferred coding envi- ronment. And with available application programming interfaces (APIs), data selec- tions and launchers can be surfaced in other SAS or third-party applications.
Figure 1. Create basic dashboards and reports, or apply advanced analytics and artificial intelligence, in a drag-and-drop environment.
Figure 2 .Compare results from multiple algorithms to automatically identify champion models.

Figure 3. Use hierarchies, smart filters and predefined windows to select data in business terms.
Key Features
Figure 4. Access advanced analytics and stream processing windows to analyze data in motion..
Streamlined ETL
Streamlined ETL automatically transforms and loads key data fields into the sensor-based data model. You can:
• RapidlyloadIoTdata,whetheryouhavethreefields(sensorID,valueanddatetime)orhundreds.
• Includesensorattributes,deviceattributes,hierarchies,measuresandevents.
• Integrateadditionalfieldandproductionqualitydatawithyoursensordata,usingcomprehensiveETLcapabilities.
Flexible data model for sensor data and related domains
The standardized and extensible, sensor-based data model provides:
• Anout-of-the-boxwaytostorecomplexIoTdata,hierarchiesandotherrelationships. • AprovenwaytoorganizelargevolumesofdiverseIoTdataforefficientanalysis.
• Asingleversionofthedataforadiversearrayofusersacrosstheorganization.
Integrated, business-focused data selection user interface
Nontechnical users can quickly select data for analysis without knowledge of the underlying technology and data structure. They can:
• Accessavailablevariablesandattributesintheirownbusinessterminology.
• Usesmartfilters,predefineddatewindowsandothershortcutstoincreaseefficiencyandreduceerrors.
• Selectdataforanycombinationofdevices,sensors,measuresandeventstosupporttheirindividualneeds. • Save,copy,reuseandsharedataselectionsacrosstheorganization.
Launchers
Users can easily prepare and transform data for analysis in SAS or third-party tools. Launchers allow you to:
• Transposedatafromanefficientstorageformattoananalytics-readyformat.
• Interpolatemissingvaluesinthedata.
• Applyafixedperiodicitytoreducedatasizeorcommonizeacrosssensors.
• OpenthedatainSASVisualAnalytics,SASVisualDataMiningandMachineLearning,andSASStudio,aswellasthird-party
and open source applications.

Key Features (continued)
Advanced analytics and machine learning
Data exploration, feature engineering and modern statistical, data mining and machine learning techniques are combined in a single, scalable in-memory processing environment. Users can:
• Analyzedatawithoutwritingcode,usingadrag-and-dropinteractiveinterface.
• Relyonbestpracticetemplates(basic,intermediateoradvanced)togetstartedquicklywithmachinelearningtasks.
• Applydiversemachinelearningalgorithms–includingdecisiontrees,randomforests,gradientboosting,neuralnetworks,support vector machines and factorization machines.
• Compareresultsofmultiplemachinelearningalgorithmswithstandardizedteststoautomaticallyidentifychampionmodels.
Streaming model execution
Streaming data (data in motion) can be analyzed and filtered in real time, so you can understand events while they’re happening − not just after the fact.
• Create,deployandmanageadvancedanalyticsmodelsrunningonstreamingdata.
• Scoredatainrealtimeandapplylearningmodelsthatcombinescoringandtraining.
• Cleanse,standardizeandfilterlivestreamdatabeforeit’sstored,reducingdownstreamprocessing.
Public APIs
Public APIs allow external systems to access data in a way that optimizes IoT investments across the enterprise. Use public APIs to:
• IntegrateSASorthird-partysolutionsintoyourIoTecosystem.
• Automaticallypopulateexternaldashboardsandreportswiththelatestdataorlistsofdataselections.
TO LEARN MORE »
To learn more about SAS Analytics for IoT, please visit: https://www.sas.com/analytics-iot.
To contact your local SAS office, please visit: sas.com/offices
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