Shop.org 2017 Tech talk do more that just analytics open platforms and sas driving innovation_gangichetty
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Tech talk do more that just analytics open platforms and sas driving innovation_gangichetty
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Shop.org 2017 Tech talk do more that just analytics open platforms and sas driving innovation_gangichetty
- 1. Open Platforms for Driving Innovation Do more than just Analytics! Sathish Gangichetty, Senior Solutions Architect SPONSOR
- 2. Copyright © SAS Institute Inc. All rights reserved. What’s coming up ? • SAS – A Quick Intro • What Innovation strategy does research support? • Data to Decisions in a transforming business landscape • What, Why and How Open Platforms Deliver Value • Demo • Imagine the Future
- 3. Copyright © SAS Institute Inc. All rights reserved. Customer analytics Data science platforms Digital marketing analytics Merchandise Assortment Planning & Sizing Optimization Multi-channel campaign management Data quality / Data integration SAS is a leader in:
- 4. Copyright © SAS Institute Inc. All rights reserved. What Innovation Strategy Works? http://www.sup.org/books/title/?id=26534 Ambidextrous Organizations stand the test of time. Explore & Exploit! Not Explore OR Exploit!!
- 5. Copyright © SAS Institute Inc. All rights reserved. Data as a force multiplier for Growth! “It’s true that we have the largest retail platform globally, but it doesn't necessarily mean that we are a retail company. Alibaba is a data company.” - Jack Ma, Founder, Alibaba Group New
- 6. Copyright © SAS Institute Inc. All rights reserved. “In the new world, it is not the big fish which eats the small fish, it’s the fast fish which eats the slow fish. Klaus Schwab Founder and Executive Chairman World Economic Forum “ Need to Deliver fast Results with a LOT of Data!
- 7. Company Confidential – For Internal Use Only Copyright © SAS Institute Inc. All rights reserved. Digital Transformation permeates everything – IDC (2017) Data, Data Everywhere!
- 8. Copyright © SAS Institute Inc. All rights reserved. DIGITAL TRANSFORMATION DIGITAL TRANSFORMATION
- 9. Copyright © SAS Institute Inc. All rights reserved. Focus on the right customers Which customers should you target for the ideal customer lifetime value? Offer the right assortments Based on online & in-store sales are the right products available to the trade area and are they profitable? Cover Demand Enough inventory to cover demand? Efficient Stores Is your Energy usage, Staff and Equipment performing at it’s best ? Personalize Customer Experience What is the best tuned path to purchase for each and every visitor ? Protect your business With the vast number of returns, transactions, purchase orders and Network connections how do you determine what is legitimate? Analyze for opportunities How do we detect, discover and determine root cause of issues effecting your business?
- 10. Copyright © SAS Institute Inc. All rights reserved. Cloud enabled analytics Machine Learning and AI Everyone is a data scientist The future of analytics is OPEN Internet of Things FORCES SHAPING OUR DATA STRATEGY Changing Data Landscape
- 11. Copyright © SAS Institute Inc. All rights reserved. Open Analytics Ecosystem
- 12. Copyright © SAS Institute Inc. All rights reserved. Cloud Analytic Services (CAS) In-Memory Engine Microservices ON PREMISE Public CloudSAS Cloud ANY DATA Cloud Database Hadoop Real time Programming Experience Visual Experience Applications (API) ! Solutions The SAS Platform Enabling Technology for Accelerating Analytic Innovation Disrupt Computing Model, Approach and Delivery
- 13. Copyright © SAS Institute Inc. All rights reserved. Workers Controller proc print data = hmeq (obs = 10); run; df = s.CASTable(‘hmeq’) df.head(10) df <- defCasTable(s, ‘hmeq’) head(df, 10) [table.fetch] table.name = “hmeq” from = 1 to = 10 CAS Action APIs
- 14. Copyright © SAS Institute Inc. All rights reserved. Open Platform for Data & Analytics COHESIVE - Provides a unifying enterprise platform to govern all your analytic assets. - Deliver a common governed execution platform. INCLUSIVE OPENDEPENDABLE POWERFUL Embed MonitorAccess Explore ModelAnalyzePrepareCleanse Govern DISCOVERYDATA DEPLOYMENT AUTOMATE & ORCHESTRATE
- 15. Copyright © SAS Institute Inc. All rights reserved. DEMO Openness Fuels Innovation Data Source ML Pipeline Globally Accessible Data Streaming Real Time Analytics 1 2 3 4 5 6 7 Visual Analytics Real Time Data Stream (customer/product/call logs etc.) Business Value! Platform Streaming Real Time Analytics
- 16. Copyright © SAS Institute Inc. All rights reserved.
- 17. Copyright © SAS Institute Inc. All rights reserved. DEMO Openness Fuels Innovation Data Source ML Pipeline Globally Accessible Data Streaming Real Time Analytics 1 2 3 4 5 6 7 Visual Analytics Real Time Data Stream (customer/product/call logs etc.) Platform Streaming Real Time Analytics Business Value!
- 18. Copyright © SAS Institute Inc. All rights reserved. DEMO Openness Fuels Innovation
- 19. Copyright © SAS Institute Inc. All rights reserved. DEMO Openness Fuels Innovation Data Source ML Pipeline Globally Accessible Data Streaming Real Time Analytics 1 2 3 4 5 6 7 Visual Analytics Real Time Data Stream (customer/product/call logs etc.) Platform Streaming Real Time Analytics Business Value!
- 20. Copyright © SAS Institute Inc. All rights reserved.
- 21. Copyright © SAS Institute Inc. All rights reserved. • Huge shared memory. • Blazingly fast speeds. • Amazing agility – DIY or Off the Shelf • Current machine learning techniques. • Cloud-ready. • Deep Learning Tool Kit with GPU support • Increased accessibility through Open APIs Platform Benefits Innovate Fast . Go To Market Faster. RESULTS AT SCALE
Hinweis der Redaktion
- This photo is cropped to a shape. Follow these steps on slides 28 and 29 to create your own shapes and insert your photo.
- What he’s articulated is the need for speed… at the heart of organizational requirements today. Like conceiving of something on a Monday, and having it in production on or before Friday. At SAS, we’ve responded to this need with SAS Viya. Enabling organizations to respond faster than ever before with analytically-driven results. Let’s dig into three core aspects of why we created SAS Viya (next slide)
- Businesses today need to recognize change as an exponential force and remain agile in order to keep up with the changing times. Analytics allows you to take advantage of the digital transformation, using data to create new revenue opportunities and identify potential threats faster than ever before. Analytics allows your business to harness the power of the digital transformation.
- Retail is facing its most revolutionary period. The potential of the Data retailers have and can get is Phenomenal . Analytics is the enabler for Retail to be successful with all of its new technology opportunity as well as the new engagement model that the customers and consumers demand. SAS has recognised that as Retailers reorganise themselves to stay competitive and profitable - ‘Omnichannel Analytics ‘ are required to best serve the Customers with the most relevant and timely message- and equally to enable the commercial decision making across the organisation to ensure Sales and Profits can grow. As the diagram depicts – the Customer moves central to the whole organisation , and what were once channels become Communication routes to the customer. Often one single customer might use 3 or 4 of these communication routes in one shopping mission with the retailer- Challenging for the retailer in terms of Content and efficiency, How often of you found yourself using a retailers web shop to try to see if the product you want is in a store near you ? A recent Survey told us that only 15% of Retailers In the UK have visibility of the assortment at a store level and not all of these have a read of live stock – massive opportunity to satisfy more customers. Our Customers find themselves exposed ,in situations such as major Marketing campaigns – For example : where that Dress featured on Bus Shelters in every city and on landing pages of the web shop never quite got planned as that kind of ‘featured product’ by Merchandising - and Supply chain scrabbling to Fulfill orders. These situations arise because the Data is often in silos , marketing separate from merchandising and supply chain. Omnichannel Analytics facilitates bringing this data together and even allowing users to model the possible outcomes of situations in order to future proof their decisions- - That Dress that’s now in short supply and availability- Should I consolidate stock to limited outlets , online only ?– When it comes back to stock where should we put it for maximum Sales and Profitability? Omnichannel Demand is a key strength of SAS – Our ability to Model and Forecast the ‘signals of demand’ is perfect for the complexity that retailers now face. Where in the past the strongest demand signal was in the store at fillpoint or at the empty fixture or shelf its now shifted to be a complex layering of signals across these communication channels – What if you could use Browsing behaviour form your webshop as a signal of demand ? Lets delve into the Kind of Business challenges that SAS is resolving in each business function:
- We are seeing a Broad Scale adoption of analytics across Retail organizations. It is the influence of Omnichannel strategies and the need to address a more unified approach. An analytical platform must be open, massively scalable, and flexible enought to work with multiple data sources Democratizing analytics through self service, easy to understand interactions. The Data Landscape continues to experience significant changes in volume, velocity, and value. Lots of data coming from many sources. We need to be prepared to interact with that data regardless of it’s source or capabilites of a given IT organizations skill set. Artificial Intelligence is giving us the ability to anticipte what a users question is and what is the best possible outcome From all of the sensors we are getting alot more data at a lower cost, which gives us the opportunity to move analytics to the edge and on the devices the make real time interactions
- We are seeing a Broad Scale adoption of analytics across Retail organizations. It is the influence of Omnichannel strategies and the need to address a more unified approach. An analytical platform must be open, massively scalable, and flexible enought to work with multiple data sources Democratizing analytics through self service, easy to understand interactions. The Data Landscape continues to experience significant changes in volume, velocity, and value. Lots of data coming from many sources. We need to be prepared to interact with that data regardless of it’s source or capabilites of a given IT organizations skill set. Artificial Intelligence is giving us the ability to anticipte what a users question is and what is the best possible outcome From all of the sensors we are getting alot more data at a lower cost, which gives us the opportunity to move analytics to the edge and on the devices the make real time interactions
- (** note: slide from Jesse Luebbert). This is one example of how code written in a SAS interface, like SAS Studio, or in Python or R, written in a Jupyter notebook works. You can see the code is different in any of these application programming interfaces – but they all are calling the same CAS action – distributing the analytics processing to the multi-threaded environment of CAS for faster results. (NOTE: this is different that how SAS Event Stream Processing can do this – so please refer to the SAS Event Stream Processing teams materials to understand how you can create a Python executable, with SAS Decision Manager, that can execute in a live stream).
- tUncover insights faster with computational processing optimized for analytic workloads. Free yourself from mundane data manipulation tasks using prebuilt routines designed for analytics, without moving the data. Ensure trusted results with analytics built for rapid deployment. s.
- (note: slide from adapted from Phil Weiss’s original) Huge shared memory. Holds ALL the data… and persists it for shared use… Specially written multi-threaded software. Ground-up rewrite… Blazingly fast speeds. Massively Parallel Processing is 100x faster than single-threaded… Amazing agility. The DATA step is multi-threaded and FEDSQL is incredible… Current machine learning techniques. Platform is specifically designed for iterative analytics… Cloud-ready. elastic/flexible/scalable architecture… Increased accessibility. Interface of choice ..