Paul Chang Senior Consultant, Data Scientist, IBM Cloud paulyc@ tw.ibm.com 2
no AI without IA 3 3 3 3 3 3 AI Machine Learning Analytics Data The AI Ladder 3
Most are here Data Driven Insight Driven Digital Transformation Outcomes Culture Change Breaking Silos Discover What Understand Why Prediction Optimization Automation Collaboration New Business Models Disruptive Technology Real-Time Decisions Capabilities Self Service Reports Business Intelligence Models Visualization Applications Instrumentation Orchestration Integration Drivers Cost Reduction Modernization Competitive Market Leader Value from Data 4
Predictive & Prescriptive Cognitive Analytics BI What happened? When and where? How much? Descriptive Reporting High latency reporting Spreadsheets Limited view reports Departmental data marts What is happening? Who is at risk? What does it cost? Enterprise-wide Data Insights Data Governance Centralize Data Platform (EHA) 360 View on Structured Data Performance / Quality metrics Regulatory compliance Risk Stratification Population Health Analytics What could happen? Who will be at risk? Where to optimize care for patients? Proactive Interventions and Improved Outcomes Predictive Analytics Resource Allocation Streaming Analytics Similarity Analytics Claims Fraud Management Patient / Member Insights Image Analytics Unstructured Analytics Cost Transparency What is the optimal treatment based on knowledge & evidence? Why is this the best protocol or treatment? Dynamic Learning for Optimal Care Guidance Natural language understanding Guided consumer experience Watson Applications (Oncology Advisor, EMRA, Genomics Advisor, Clinical Trial Matching) Genomics Exogenous Data analysis Evidence-based medicine Personalized Healthcare 5
There is no Artificial Intelligence (AI) without Information Architecture (IA) Data Ecosystem Data in silos Difficult to access No lineage Analytics Tools Discrete tools Different preferences Difficult to manage Workflow Not integrated Not governed Lack dev/prod parity Culture Not collaborative Slow provisioning Lack trust in AI 6
Collect Organize Analyze Hybrid Data Management Collect all types of data, structured and unstructured Fit-for-purpose data repositories Information Governance & Integration Masking, cataloging, and finding data Integrating and shaping data Data Science & Business Analytics Empower teams to tackle their analytics use cases through self-service Descriptive, predictive, and prescriptive models & business reporting 7
& - - - - Collect, Connect, and Access Data Govern, Search, and Find Data Understand and Prepare Data for Analysis Build Descriptive, Predictive, and Prescriptive Models & Reports Model Management and Deployment Create Analytics Applications Connect and discover content from multiple data sources across your organization. Provision databases and federate data access. Auto classify. Ingest metadata, auto assign terms and rules. Grant user access levels and enforce business policies. Index for search, visualize consumers and producers of assets with lineage, metrics, and quality profiles. Find data and analytics assets in the Catalog. Understand, cleanse and prepare your data to create data preparation pipelines visually. Use popular open source frameworks to prepare structured and unstructured data. Scale data integration and transformation on performant engines. Create Machine Learning, Deep Learning, Optimization, and other advanced mathematical models. Design your models programmatically or visually with popular open source tooling and IBM frameworks. Visualize data. Create dashboards and business reports. Manage your models across dev, test, staging, and prod. Deploy your models and scale automatically for online, batch or streaming use cases with SLAs. Monitor model performance and automatically trigger retraining and redeployment as rolling upgrades. Incorporate trusted and governed models into applications, dashboards, and operational systems. 8
AI Apply machine learning everywhere Scale your insights on demand Build a trusted analytics foundation Make your data simple & accessible 9
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Extract Features Train Model Ingest Data Data Engineer Data Scientist Developer Deploy Model Make Predictions Surface Problem Business Analyst Human intervention 12
( ( ) ) ) Create Collaborate Learn 13
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Make your data ready for AI Make your data simple and accessible Build a trusted analytics foundation Scale insights from your data on demand Reimagine your workflows with AI 15
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