Mobile, Wearables, Big Data and A Strategy to Move Forward (with NTT Data Enterprise Services, Inc.) #BarcodingEF4

1. To know or not to know, that - is no longer an option!

2. Agenda • Innovation • Technology Solutions – How Mobile Changed Everything! – Wearables and Augmented Reality – Big Data and Predictive Analytics • Big Data Maturity Model and The Journey • Actionable Strategy

4. The Data Explosion

5. Megatrends Driving Significant Profile Shifts

6. Growth of Everything

7. Agenda • Innovation • Technology Solutions – How Mobile Changed Everything! – Wearables – Augmented Reality – Big Data and Predictive Analytics • Big Data Maturity Model and The Journey • Actionable Strategy

8. How Mobile Changed…. Everything • Asset intensive industries were out in front of the mobile curve • The process was cumbersome as any analysis or additional information required connection to the back office • In certain industries, this extended cycle produced longer downtimes and lowered the return on investment (ROI) on those very expensive assets Source: Fieldconnect

9. How Mobile Changed…. Everything • Smart devices and Mobile technology provides right real time information to the field service technician • Technician can visualize the work instructions in 3D and can perform the job • If the job requires new part technician can research the options and initiate purchase request • Mobile access to analytical data provides technicians knowledge of what is going on at the shop-floor level so they can assess it against historical or benchmark data and optimize asset performance

10. Wearables – Augmented Reality • “The market for company-provided wearables will be larger than the consumer market within the next five years, as wearables represent the next phase of the mobile revolution.” – Forrester • Within Wearables smart glasses, has huge potential for any workforce that could benefit from access to hands free computing. • The next wave is having the right analytics presented to the right user instantly, rather than requiring a query. This is the thinking behind the concept of “augmented reality.”

11. Wearables – Augmented Reality • A service technician can scan an item and instantly know all of its technical data, including the equipment number, the status of the asset, and more. The technician can be simply guided there by a system that uses both facility and GPS data • The key to the effectiveness of augmented reality is the presentation of data. With so much data available, important analytics and its presentation is critical • Handsfree actionable insight will help make better business decisions and improve worker productivity

12. Big Data – Predictive Analytics • The explosive growth of social media, mobile devices, and machine sensors is generating a wealth of data that either didn't exist or wasn’t accessible a few years ago • Until now analytics was used to simply access huge sets of data that might have been aggregated for reporting purposes • New Analytics applications based on in-memory computing, advanced models and predictive algorithm can facilitate instant access to actionable knowledge in seconds

13. Big Data – Predictive Analytics • Asset-intensive industries can employ predictive maintenance strategies that increase uptime and speed ROI on assets and equipment • Sensors track a machine’s performance, provide an alert when the machine’s performance dips below a threshold • But predictive analytics can forecast the machine’s condition, which reduces downtime and improves safety • Analysis on the machine’s uptime and performance quality can then be integrated into the product design to improve the next generation of machines

15. Big Data – Maturity Model Source: SAP Big Data Maturity Model

16. Big Data – Journey Niche Intentional Strategic Business Impact Lighthouse Champion Change Agent Big Data Maven Strategy emerging from experience - Silo’d stages: No Big Data business programs, seen only as technology programs - No specific skills, executive audience, - Big Data projects across business - Understanding and managing Big Data insights - Emerging coherence in Big Data architecture Competency Center. Big Data Governance. - Integrated stages: Data drives continuous Business innovation - Analytic insight optimizes processes and is often automated - Rapid capability enabled by centralized platform, uniform standards and procedures

18. Key Considerations • Involve Business upfront – What tangible benefits can big data initiatives provide my organization? – What is the ROI for a big data initiative? • Fire Bullets not Cannons. – Pick something that's manageable. Start on a small scale to prove your hypothesis. • Useful Data can come from anywhere (and Everywhere) – What data should you consider? – How is data captured? • You will need new expertise for Big Data. – Big Data relies on data modeling and data science

19. Big Data Strategy Purpose Opportunity Assessment Strategy Definition q Understand current state Ø Where is Big Data Analytics being used today? Ø What technology, application and providers are being used? Ø How is Big Data evaluated for productivity and cost effectiveness? q Rethink and Re-engineer Business Model Ø What jobs can be done more efficiently with Big Data Analytics? Ø What standard applications are available from vendors? Ø How are competitors using Big Data Analytics? Ø What are quick wins for the new applications? Ø How will the data driven organization look and work? q Standards and Technology Provisioning q Security Practices q Data Management System

Mobile, Wearables, Big Data and A Strategy to Move Forward (with NTT Data Enterprise Services, Inc.) #BarcodingEF4

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