1. Sas is open (for business)
Sean RObson, sas canada

2. Tamara Dull, SAS Best Practices
Steve Holder, National Analytics Lead, SAS Canada Tina Schweihofer, Senior Solution Specialist, SAS Canada
acknowledgements
Thanks and acknowledgements: Tamara Dull, SAS Best Practices & Steve Holder, National Analytics Lead for SAS Canada

3. the open source myth… …and the reality It’s free.
5 open source myths
the open source myth…
…and the reality
It’s free.
Licensing is free: that’s it.
It’s ‘Geekware’.
At first… but not over time.
It’s ‘not ready’ for the Enterprise.
2010: 42%. 2015: 78%1
It’s hard to support.
Strength of community!
It’s not secure.
55% believe it’s more secure. 1
1 Source: 2015 Future of Open Source Survey, North Bridge and Black Duck Software, April 2015

4. Insert Title
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5. SAS Offers: Open Source Offers:
comparisons
SAS Offers:
Productivity for users regardless of skillset.
Scalability to address any problem or dataset.
Governed analytics and data.
The support organizations require for production and operational analytics.
Open Source Offers:
A robust online community.
An extensive array of algorithms.
Low cost barriers to entry.
Fast adoption of new innovation.

6. Open source revolution….
… means the evolution of SAS to embrace and extend the capabilities of open source as part of an analytics ecosystem.

8. SAS EMBRACES

10. Integration possibilities

11. Open source in sas
SAS integrates open source within some software. SAS® Enterprise Miner offers score code support for 6 different R packages and allows users to import any type of R code. The open source node can also be used to import any open source model. Allows users to create ensemble models using open source and SAS. Models can be converted to score code for operational deployment from within a drag-and-drop interface. This results in improved productivity, deployment and scalability.
You can take two models, put them together, to make an even better model. A blended model combines the best of both SAS and Open Source to achieve the greatest lift, and thus the greatest improvement in overall performance

12. Why is this important?
Improve model lift and performance by creating blended models that combine the best of SAS and Open Source.
SAS automatically generates documentation capturing best practices, promoting collaboration and helping reduce turnover risk.

13. Facilitating Interoperability
A simple example

14. Facilitating Interoperability
A simple example

15. In real life What if you had to CODE this in your language of choice?2 3 4 5
Leverage reusable data preparation tasks.
Develop models using multiple statistical methods.
Leverage advanced Machine Learning.
Integrate R Models.
Choose Champion and Deploy. 1
What if you had to CODE this in your language of choice?

16. SAS Procedures can be called from open source tools.
Sas in open source
Base SAS offers a Java object to incorporate a variety of external languages, including Python.
SAS Procedures can be called from open source tools.
The Jupyter kernel for SAS brings the power of SAS data manipulation and analytics capabilities to the Jupyter notebook.

17. Why is this important?
This allows data scientists to code in their language and interface of choice, while allowing SAS to extend open source applications with productivity and the ability to scale to any data volume. Using SAS in open source can ease the transition of non-SAS users: calling SAS via stored processes/APIs from other programming interfaces is a simple way for open source programmers to access SAS.

18. The benefits of using SAS in open source include delivering more information and performance metrics automatically in a familiar user experience.

19. Bringing SAS to R
SAS can be called from R Studio allowing programmers to access the power of SAS in R.

20. The power of models
SAS supports analytic model deployment with inventory, scoring, monitoring and retraining capabilities for SAS and Open Source models.
Supported PMML
Non-PMML
Inventory n
Publish & Score
Batch
In-Database
Web Service
Streaming
Monitor
Retrain
The Predictive Model Markup Language (PMML) is the de facto standard language used to represent predictive analytic models. It allows for predictive solutions to be easily shared between PMML compliant applications.

21. The future is now…

22. CLOUD OPEN Elastic Multi-threaded hyper-computing POWERFUL UNIFIED
SAS viya
SUPPORTING CURRENT INDUSTRY TRENDS
Elastic
Multi-threaded hyper-computing
POWERFUL
CLOUD
UNIFIED
OPEN
Scalable
Memory spillover
Charge-back capable
Integrated solutions
Micro-services architecture
End-to-end
Easy installs
RESTful API’s
Backward compatible
‘Any data, any platform’
Analytics lifecycle support
Python, Java, Lua support
Advanced machine learning
Plug n’ play
What makes SAS® Viya™ different?
Develop, deploy and manage throughout the complete analytic lifecycle with a single architecture.
Execute In-Stream, In-Cloud, In-Database, In-Memory, In-Hadoop or In-Device.
Inventory and govern models build with SAS and non-SAS tools.
Integrate SAS enterprise analytics through Python, Lua, Java and REST.
Run SAS code anywhere, desktop, server, grid, cluster or cloud.
Deploy and run on any public/private cloud.

23. open source by including it and leveraging it where we can
SAS AND Open Source
SAS
EMBRACE
open source by including it and leveraging it where we can
EXTEND
open source by improving its interoperability and utility for the enterprise
Embrace – we use it… we understand how open source can really help analytical application development. We know what our customers go through to adopt open source in their applications.
Extend – open access to analytics, putting the right tools in everybody’s hands (data scientist who doesn’t know SAS, data miner who does, application developers, domain experts, business analytics, IT security professional, system administrators, executives, you name it) – so they are more productive .. Every individual that needs analytics can get to it and get results fast. For development – use the skills you have. For processing – run in the cloud (public or private), or on Bare OS – extending analytic workloads with in-memory, parallel processing – fast answers, fast innovation.

24. Visual Analytics & Visual Statistics
14-day Free Cloud Trial, up to 5 users
Your Trial, Your Data
Visual Analytics – Register for Trial
Smart data exploration with self-services analytics makes this product usable for anyone. Interactive reporting makes it collaborative. Scalability and governance make it fit the needs of your organization, no matter the size.
Visual Statistics – Register for Trial
Multiple users can explore and visualize data, then interactively create and refine descriptive and predictive models. Distributed, in-memory processing reduces model development time so you can run complex analytic computations – and get precise results – in minutes.
Matt – essentially they get to upload their data into the cloud to start playing with the product, exploring their data, etc. if they like the product after 2 weeks, they can buy/subscribe after the period is up if they like what they see and the data that they have already played with will remain in the state that they left it.
Let me know if you’d like some further talking points.

25. Thank you Sean.Robson@sas.com Matt.malczewski@sas.com
Linkedin: Sean Robson
Twitter: Malchew
Linkedin: Matt Malczewski