Big-Data IoT, VORA = Digital Enterprise Hari Guleria VP Big-Data & SAP HANA hari.guleria@pridevel.com Roadmap to Digital Monitization

Start and End With Business Benefits The success of any new technology lies in the impact it has on the Company’s operations

Agenda The Digital Reality Today ROI The Digital Reality Today Big-Data Explosion  Digital Disruption Inverting the Pyramid Business Needs Business Case 1- O&G Business case 2- Retail The ball is now in your court

- BIG-DATA- Digital Disruption I d e a ! - BIG-DATA- Digital Disruption Digital Disruption Starts with an Idea What Disruption!

Accelerating Data Creation PaperHost SLI Systems NetSuite OpSource Joyent Hosting.com Tata Communications Datapipe PPM Alterian Hyland NetDocuments NetReach OpenText Xerox Google Microsoft IntraLinks Qvidian Sage SugarCRM Volusion Zoho Adobe Avid Corel Serif Yahoo CyberShift Saba Softscape Sonar6 Ariba Yahoo! Quadrem Elemica Kinaxis CCC DCC SCM ADP VirtualEdge Cornerstone onDemand Kenexa Workscape Exact Online FinancialForce.com Intacct Plex Systems Quickbooks eBay YouTube Viber Qzone Amazon Web Services GoGrid Rackspace LimeLight Jive Software salesforce.com Xactly Paint.NET Business Education Entertainment Games Lifestyle Music Navigation News Photo & Video Productivity Reference Social Networking Sport Travel Utilities Workbrain SuccessFactors Taleo Workday Finance box.net Facebook LinkedIn TripIt Pinterest Zynga Baidu Twitter Yammer Atlassian MobilieIron SmugMug Amazon iHandy PingMe Associatedcontent Flickr Snapfish Answers.com Tumblr. Urban Scribd. Pandora MobileFrame.com Mixi CYworld Renren Xing Yandex Heroku RightScale New Relic AppFog Bromium Splunk CloudSigma cloudability kaggle nebula Parse ScaleXtreme SolidFire Zillabyte dotCloud BeyondCore Mozy Fring Toggl MailChimp Hootsuite Foursquare buzzd Dragon Diction SuperCam UPS Mobile Fed Ex Mobile Scanner Pro DocuSign HP ePrint iSchedule Khan Academy BrainPOP myHomework Cookie Doodle Ah! Fasion Girl Every 60 seconds 148,000+ tweets MRM Claim Processing Payroll Sales tracking & Marketing Commissions Database ERP CRM SCM HCM PLM HP EMC Cost Management Order Entry Product Configurator Bills of Material Engineering Inventory Manufacturing Projects Quality Control SAP Cash Management Accounts Receivable Fixed Assets Costing Billing Time and Expense Activity Management Training Time & Attendance Rostering Service Data Warehousing 1,245,000 status updates IBM Unisys Burroughs Hitachi NEC Bull Fijitsu 22 million instant messages DIGITAL DISRUPTION Mobile, Social, IoT, Big Data & The Cloud ERP BUSINESS Client/Server CONNECTIVITY The Internet Mainframe 1,523,216 Google searches 368 million+ emails sent 3,820TB of data created 916 new mobile web users  Every 7-10 years, technology delivery undergoes a tectonic shift; one that opens up new business and access models. A shift that changes the way technology is consumed and the value that it can bring. A change in what is possible. A removal of inhibitors that unleash the power of innovation. Today, mobility, social, big data, and the advent of cloud computing are representative of such shifts offering a new means for IT to help organizations accelerate progress towards solving their most pressing challenges (including speeding innovation, enhancing agility, improving financial management). These shifts can unleash the power of IT to not only support but help shape the business. Some of it about your products

The Digital Data Explosion A poor fit for the traditional relational database As-Is To-Be 2005 2018 2010 More than 90% is unstructured data Approx. 500 quadrillion files Quantity doubles every 2 years Most unstructured data is neither stored nor analyzed! 1.8 trillion gigabytes of data was created in 2011: 10,000 GB of Data (IN BILLIONS) STRUCTURED DATA – MIDSTREAM (Repetitive Data) UNSTRUCTURED DATA UPSTREAM AND DOWNSTREAM (Non-Repetitive Data) 90% of the DIGITAL ‘Business Value Attainment’ lies 10% / 90% 99% of CURRENT Focus lies 90% / 10% Source: Cloudera

Disruption just needs an idea Digital connections are changing the definition of an enterprise Image: The Economist Attribute Old New Leadership Innovation World Class Higher Quality + Lower Cost Data Store On-Premise Cloud’s Operations Assets Self Owned Crowd Sourced (Uber, airbnb) Customer Survey Annual Instant - Real-Rime Decisions Periodic Real-Time Trade-Promotion Real-Time (Gaming) Welcome to the world of ‘IoQ’

Customer Centric Decisions Operational Management Inverting the Pyramid.. Legacy Organizations On-Premise IT Manager is always right Quarterly/Annual review of customers 100% Focus inside the Midstream Low connectivity to Upstream Low connectivity to Downstream Very Little Predictive Analysis Field Information Flows Down-to-Up Most decision flow Top-to-Down DIGITAL Pyramid Customer Centric Decisions Operational User Feedback Power Users & CONNECTED Consumers = Customer Loyalty Operational Management ‘C’ level Executives Leaders Need Audit Customer Expectations Flow down Real-Time deployed to Exceed IMPROVE Executives become Conductors to the Symphony of Customer Satisfaction Customer Inclusive DIGITAL Connectivity Identifying, Prioritizing & Meeting Expectations Real-time Customer Satisfaction Admin Connect Leaders Instructions Flow down Level-by-Level Operational Reality Summarized up Level-by-Level ‘C’ Level Executives Disconnected Periodic Operational Management Operational Assets Operational Supply Chains Operational Stocks and Services Sales persons driven Quotas Disconnected Operational Users Customer Exclusive Methodology The Digital disruption is inverting traditional ‘Command & Control’ organizations existing in ‘Brick & Mortar’ enterprises with a tear tht is totally services and customer focused. In this new world ‘customers’ are the new CIO’s LEGACY Decision Pyramid Enterprise Centric Decisions

Data-Explosion + Real-time ‘Value-Chain’ Decisions I d e a ! What is THIS CReating Data-Explosion + Real-time ‘Value-Chain’ Decisions Emerging Pressure of..

IoT- The Connected Enterprise Midstream This is our enterprise Our Plants Our Systems Our Employees Behind our Firewall Our most familiar place Upstream Our Vendors & Suppliers Our Manufacturing Units Downstream Our Retail Our customers Our Buyers

The Connected Enterprise = The Digital Enterprise Disrupt or Be Disrupted The digital economy is disrupting everything. In every industry, data is being created in places it never has before. Creating hyper-distributed data environments. It is becoming ever increasingly hard to reach that data, secure that data, and much less draw an insight and enable a person or process to take action on the data. But data is not the problem, connected data is the problem. If every single employee is a decision-maker, organizations must focus on enhancing the quality of each decision taken. The ability to secure, aggregate, automate, and draw insights from an organization’s own data – with speed – will define value for that organization. When you connect People, Process, Data and Things, new opportunities emerge: New market opportunities New business models New way to operate New ways to consume technology Technology becomes an enabler

The Digital Enterprise Evolution MIDSTREAM VENDORS SUPPLIERS COMPANY PLANTS Inbound LOGISTICS DRIVERS EMPLOYEES VEHICLES SENSORS DOWNSTREAM CUSTOMERS RETAIL WHOLESALE CONTRACT MFGS Outbound LOGISTICS SENSORS UPSTREAM CORP HQ VP Sales VP Service CIO Planners VP BU VP Supply Chain Customer Service 45,000+ CONNECTED PARTNERS ~100 FACTORIES 4,000+ DEALERS 100M+ VEHICLES AND DRIVERS ON THE ROAD 500+ APPLICATIONS 450,000+ CONNECTED USERS What do we mean by hyper distributed operations? Let’s take an automobile manufacturers…they may have hundreds of factories, thousands of dealers, tens of thousands of partners, and millions of vehicles on the road…it doesn’t get much more distributed then this…and they want and need to share systems and data across all of these participants in their value chain…

Data Processing Must Evolve Too CENTRALIZED DECENTRALIZED FEDERATED HEADQUARTERS PARTNERS VEHICLES DRIVERS FACTORIES DEALERS FIELD SALES Enterprise Applications Dealer Access Machine Apps Customer Mobile Apps Mobile Workforce Applications On-board Computer Supplier Exchanges This evolution of the network requiring a new approach to computing… Starting with the need to support computing at the edge. Today’s environment requires that you support application development and hosting across fog, cloud and mobile. It requires enabling an Application-centric Infrastructure with application-based policies that decouple application requirements from network configurations to reduce the impact of application changes on performance, security, availability and scale Enabling Streaming Analytics and Aggregation…streaming analytics across data-in-motion and data-at-rest and rapid logical aggregation of data It requires Secure Interaction – the growing ecosystem of partners requires pervasive security policies that support the new B2B and B2C interactions. And last but not least, it requires application integration across hybrid computing environments Cisco is uniquely positioned to help our clients address these new requirements with: • an intelligent network and IoX that supports network distributed workload and edge computing • new streaming analytics and data virtualization capabilities with our Connected Analytics software • and best in class application integration with our Cisco Integration Platform that allows you to connect disparate applications across hybrid environments. To further support these evolving requirements, we are announcing a number of new software capabilities that serve as the foundation for digital business. across hybrid computing environments

Digital Enterprises Will Process Data at the Edge Widely Distributed, Streaming, Short Shelf Life, Too Big to Move “By 2020 Most Decision-Data will be processed at the edge” (mobile devices, appliances, routers) 86% HYPER FEDERATED (Inter-Cloud) Three years from now, where will most data generated by IoT solutions be processed?

What is Changing? New data processing must take the processing to the data and send subsets for Decisions & Analytics Traditional data processing moved the data for Decisions & Analytics Fog Node Edge Node IoT Device Real-Time Decision Processing Hind-Sight Decision Processing Data All Select Data Only Filtered Data

A real world example: Sensor data from a Boeing jet flying from New York to SFO 2,600+ sensors per engine 30 TB 30 TB of Data per engine every hour 2 twin-engine Boeing 737 60TB / Hour 5 five-hour, flight from New York to San Francisco 300 TB / flight 28,537 # of commercial flights in the sky in the United States on any given day. days in a year 365 30 GB 8.5 TB 312.4 TB 300 TB /flight 8,561,100 TB/day 3,124,801,500 TB/year Predictive Patterns: 99.9% of this data simply states ‘I am OK’

Real-Time Decisions & Predictive Analytics Business NOW! Iot Internet of Things & Everything Real-Time Decisions & Predictive Analytics

IoT vs. IoE From Internet of Things to Internet of Everything Enterprise Unit 1 Unit 2 Unit ‘Z’ Unit ‘N’ Step 1: IoT Hub & Spoke Step ‘N’: IoE Intra-Connected Unit 1 Enterprise Unit 2 Unit ‘Z’ Unit ‘N’ *Unit= Sensor, Car, component, Person, Patient, Product Meter, Gauge, Engine, Bearing in an engine = any thing that that can be digitized CONNECTED TO A HUB *Unit= Sensor, Car, component, Person, Patient, Product Meter, Gauge, Engine, Bearing in an engine = any thing that that can be digitized HYPER-CONNECTED

Business Needs by Priority – Digital Enterprise REAL-TIME Decision Feedback Loop What is happening in the field Right-Now Automated Reality Checks and Alerts systems Predictive Analytics Link History to Predict the Future Prescriptive Solutions How do I fix that I can now Predict Highest Quality & Lowest Cost Solutions Do it Right the First Time, Every Time

Deconstructing Predictive Analytics Those who do not learn History, tend to make the same mistakes again Present Historical Cone PAST Unlikely FUTURE Likely Algorithms Patterns Data Time Predictable Experience Patterns Data & Patterns Algorithms OBSERVER Likely Probability Future Cone Unlikely

Preventive Maintenance & Monitor Condition Cost to Repair IIoT - Predictive Maintenance and Service Visuals: The P-F Interval Curve Preventive Maintenance & Monitor Condition Repair or Replace Equipment Unusable “Can” Effect of PdMS Potential Failure P Early Signal 1 – Ultrasonic Energy Detected Machine Capability / Resistance to Failure Early Signal 2 – Vibration Analysis Fault Early Signal 3 - Oil Contamination Detected Audible Noise Hot to Touch Mechanically Loose F Functional Failure “Want” Total Failure Ancillary Damage Time

Real-Time Decision Enablement VORA Business CASE 1 O&G Real-Time Decision Enablement

What is SAP VORA Real-Time Streaming Data Integrator Between For Streaming Unstructured Data (Non-Repetitive Data) Streaming Semi-Structured Data (Semi Repetitive Data) Enterprise Structured Data (Predictable+ Repetitive Data) For Real-Time Alerts Predictive & Prescriptive Analytics Operational Analytics

Operational Facts 4 Unplanned Downtime ‘Unplanned Downtime’ Cost per unit Instance On-Shore Drilling $3,000-5,000 per hour = $72k to 120k per day Off-Shore Drilling Rigs $500,000 to $2 million per day or $15,000/hr Average Unplanned downtime is 1-5 days Depending on the severity of the damage

Asset failure for Predictable Maintenance Acquisition Start time Acquisition end time Tool Status ( Temp) Scintillator detector Parameters Data Inputs Depth Angle of Penetration Internal temperature External Temperature RPM Internal Pressure External Pressure Torque Video + Audio

Solution Architecture On Cloud 1 Scintillator Detector Temperature Pressure HDFS SAP HANA VORA SCALA Programming SPARK SQL SPARK 2 On- Premise SAP EAM SAP ERP SAP FI SAP HANA Information Model VORA Virtual Tables VORA Remote Data Source SAP HANA 3 Assets Master data ECC operational Data OLAP – SAP Lumera OLAP Analytics Predictive Analytics Predictive engine Algorithms 4 Self-Help Analytics HANA SPARK Adapter

Data Flow Real-time Streaming Data Cloud HANA Modeler HDFS Alerts For ‘Zero-Unplanned Downtime’ Predictive Analytics Real-time Streaming Data Alerts 0101001010001001010100100101010101010010010001001010100001001001 Cloud 0101001010001001010100100101010101010010010001001010100001001001 SPARK VORA SAP ECC HDFS HANA Modeler SPARK VORA Predictions 0101001010001001010100100101010101010010010001001010100001001001 SPARK VORA SAP EAM Analytics

Business Benefits Unplanned Downtime reduced by 62% 9 Month Cost savings of $7.6 million Zero Downtime increased from 72% to 93% OSHA 300A scores improved by 13%

Real-Time Decision Enablement VORA Business CASE 2 Retail Real-Time Decision Enablement

Operational Facts Per Store Indirect Costs Sales Target vs. Actuals From total 760 stores - ’12 Stores data’ Cost per unit store Per Store Indirect Costs $3,000-$4,000 per day Sales Target vs. Actuals $17,000 good day ; $ 5,000 bad day Store Manager concerns Delighting a customer Finding what customer needs Keeping store employees motivated- Decreasing Employee Churn Training / on-boarding costs are around $3,000 to $10,000 Store employee concerns Normally a temp job- waiting for a better opportunity Unable to predict earnings from commissions Average employee employment is 4-6 months

Analytics for Customer Experience Store ID, + Geo Loc Location Heat Map, Traffic, Locality, etc Customer Loyalty Card Store Opening time Store Closing Time Data Inputs Location & Movement Analytics Store Manager Store Employees + Invoice Employee Attendance In/Out Store Racks Store Stocks Store Sales, Order Line Items Permission based services Store POS Data Weather Temperature

Data Flow Real-Time Customer Real-time Feedback Alerts Cloud For ‘Store Employee Real time’ Retention Analytics – Grew Customer satisfaction by 16% in 3 months Real-Time Customer Feedback Real-time Alerts 0101001010001001010100100101010101010010010001001010100001001001 0101001010001001010100100101010101010010010001001010100001001001 Cloud SPARK VORA SAP ECC HDFS HANA Modeler SPARK VORA Predictions 0101001010001001010100100101010101010010010001001010100001001001 0101001010001001010100100101010101010010010001001010100001001001 SPARK VORA Analytics Real-Time POS Streaming Data POS SAP ECC SAP EAM

Business Benefits Employee Retention increased by 43% Per store sales increased by 17% Customer satisfaction went up by 13% Overall Per Store Sales went up by 21%

The ball is now in your Court Option 1 Option 2 Stay as you are! Hope for the Best Disrupt – Don’t get Disrupted Become the competitive Disrupter Undertake a Design thinking workshop (80% Buss/20% IT) Follow the Digital Enterprise Structured Framework Build the Digital Enterprise for the Future Ask us for our D-BAS (Digital- Business Assurance Services) B-QAS (ERP selection- Business Quality Assurance Services)

Thank You The Base for Business Success is a strong foundation of Technology

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