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2 is there something Artificial about Intelligence? the dangers of climbing a ladder André De Locht Sr Business Value Consultant, Europe Unified Governance and Integration andre.de.locht@be.ibm.com IBM Cloud

3 Digital Transformation! PRESCRIPTIVE provide recommendations on what to do New Business Models Democratization Public Cloud Private Cloud PREDICTIVE anticipate what will happen Analytics, Data Science, Machine Learning Panoramic View Digital Transformation On Premises GOVERNANCE business ready data hub make data easy and accessible Finding Data Business Capability 3

4 Predict call volume in call center for staffing decisions Predict power usage in an electrical-distribution grid Detect fraudulent activity in credit-card transactions Critical Success Factors: 1. Governance 1. Use Case (How can I...?) 1. Future proof Architecture why? by when? who? Classify images from satellites for climate change models Forecast product demand and inventory levels Predict the price of cars based on their characteristics Predict the probability that a patient joins a healthcare program Predict whether or not registered users will be willing to pay or not a particular price for a product Understand product-sales drivers such as competition, prices, distribution, advertising, etc Optimize price points and estimate product-price elasticity 4

5 Lessons learned (sometimes the hard way ) Gather and Organize data 15% time spend Gather and Organize data prioritize insights needed prioritize value drivers success rate Analyze data deploy findings 85% 5

6 Business Value steps Capability Ladder Business Value Business Capability Foundational * DATA * Accelerate Teams through Governed Collaboration Break down silos through governance and lineage Get Data ready for AI Explore, visualize & understand data Build out an enterprise catalog - auto-discover and classify all existing data sources Provision trusted data Intermediate * ANALYTICS * Automation, collaboration & governance infused throughout Share analytics assets with other collaborators (and publish into the Enterprise catalog) Dynamically provisions users in minutes with no assembly required Access and act upon all enterprise data regardless of where it lives Advanced * ML * Automate seamlessly integrated crossfunctional workflows Deploy end - to - end information architecture to support AI development Build data movement & transformation flows to help prepare data for Analysts to consume Support Data Virtualization best practices - Define Federated ( Fluid ) Queries and Views to facilitate analytics Fully integrated data science self-service stack: find data, transform/enrich, analyze, publish models Train a machine learning model & expose a scoring service for prediction Facilitate cross-cloud Hybrid data access & movements Full Operational * AI * Use tools of choice Governed Data Lake, Data Warehouse Modernization, Industry Accelerators Reduce costs of manage multiple product stacks and enable use cases Allocate and manage resource usage and scale as needed AI Optimize Costs and Modernize data infrastructure : Cloud Native, behind the firewall Business Value Architects Infrastructure

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