Slides: Five Data Valuation Pillars

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The document discusses five pillars for data valuation: predictively spotting new opportunities, innovating in an agile way, demonstrating transparency and trust, providing unique personalized experiences, and always being on and operating in real time. It provides more details on the analytic lifecycle for predictively spotting opportunities, the need for data agility in application development, establishing a data ethic of transparency and trust, using data to provide personalized experiences, and reliably storing data to always be on and operating in real time.

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2. 2© Copyright 2016 EMC Corporation. All rights reserved. FIVE VALUATION PILLARS BUSINESS IMPERATIVES FOR THE INFORMATION GENERATION Predictively Spotting New Opportunities Innovate in an Agile Way Demonstrate Transparency & Trust Unique, Personalized Experience Always On, Operating in Real Time

3. 3© Copyright 2016 EMC Corporation. All rights reserved. LEARN THE ANALYTIC LIFECYCLE PREDICTIVELY SPOTTING NEW OPPORTUNITIES DATA ANALYTICS LIFECYCLE 6 1 2 3 4 5 Discovery Model Building Data Prep Model Planning Operationalize Communicate Results Do I have a good idea about the type of model to try? Can I refine the analytic plan? Is the model robust enough? Have we failed for sure? Do I have enough good quality data to start building the model? Do I have enough information to draft an analytic plan and share for peer review?

4. 4© Copyright 2016 EMC Corporation. All rights reserved. DATA AGILITY IN APPLICATION DEPLOYMENT INNOVATE IN AN AGILE WAY 75%of application development supporting digital business will be “built” not “bought” by 2020 Source: Gartner Zero Downtime Upgrade to Production Commit Code Change 1 Automate Build & Test (Unit Test, Static Code Analysis) 2 Store Binaries & Build Artifacts 3 Automated Integration Testing 4 Acceptance, Performance & Load 5 6

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