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Data-Driven Operating Models Enabled by Process Mining

  1. Data Driven Operating Models Theodor Schabicki April 2019 Enabled by Process Mining
  3. DATA
  6. 6 Important market trends • Open Innovation • User centricity • Blockchain • Skunk Works • Purpose driven • Event driven • Open API • QuantumComputing • Mobile / P2P Payments • Customization/ batch size 1 • Natural UI • DevOps • Cyber Security • Virtual Assistants • Banking as a Service • Internet of Things • Internal product mgmt. • DesignOps • Serverless architecture • Poly cloud • New banking rails • Streamlined UX • Digital Twins • Platformification • Brain Computer Interface Technology • AR/VR • Beyond Banking Services • Cognitive Agents • Smart Workspace • Agile management • Next Gen Workflow • Microservices • Machine/ Deep Learning Process Mining Robotic Process Automation BPM-Tools Cognitive Capturing Artificial Intelligence RPA AI With technologies hitting the next level of maturity, automation will expand its reach significantly. Combined with the right use cases, competencies and next generation tools, automation can be elevated to the next level Technologies hit a new level of maturity enabling innovative architectures fuelling the rapid growth of automation in this decade Data Analytics
  7. “Transformation into a fully digital business cannot exist without Data Driven Operations”
  8. 8 100% digital & structured Normalization DataInput Smart Distribution STP NI BPO >80% <20% Execution Processing Integration Service Layer Systems Operating Model Transformation Enabler RPA 5%R PA The Vision The combination of strict data and process orientation together with core elements from isolated automation enables new architectural concepts and efficiency levels Data Driven Operations Current State of Operations Quantification & Analytics 5%R PA Non-digital and unstructured Support Functions ...on too many different operating systems Externa l 65% Manual 30% STP Processing approx… 5% RPA Order ProcessingOperations Human-centric, only a view Robots & no real STP … … operating with a lot of interfaces AI The transformation of operations with a truly Data Driven Operating Model driven by operational efficiency will increase quality levels while reducing costs Größer
  9. 9 Potential target vision of Intelligent Process Automation: Smart Workflows serving as an IPA-link Blue Print: Process chain for Operations 4.0 (Video)Schematic representation of Operations 4.0 Structured and unstructured data (forms, E-mails, call center) for different clients from different entry channels is recorded/targeted by open API and the integration layer The digitalization layer and the normalization layer represent the central part of the automated processing on a huge scale An intelligent distribution allows the work steps to be processed modularized as well as parallelized by using Workflow-Tools or RPA and AI Generally the modules are set up independently and self-optimizing NEW
  10. 10 Three horizons of automation The leap from isolated to integrated automation through the use of new technologies enables a significant increase in efficiency Managed Autonomy Autonomous, human-controlled systems with minimal intervention EfficiencyLevel Isolated Automation Automation initiatives that combine STP and RPA of isolated processes Integrated Automation Integrated and combined automation of value streams with OCR, workflow tools, RPA & AI Maturity Level API Business Model Fully Digital Input New CollaborationModel IntelligentAutomation Operations 4.0 RPA AI & Scaling RPA RPA Farms Three waves of automation are currently affecting banking operations. The services and solutions to be developed require not only IT know-how, but in particular methodological knowledge in process analysis and process optimization
  11. Process Focus is Key to build the vision of data driven operating models How do I achieve fully Data driven Ops? → Process orientation to gain efficiency reduce costs etc
  12. 12 Process Mining is the foundation and enables everything that comes afterwards Over to Process Mining and why it is so important
  13. 13 After Sales Business Model NPA IKS Monitoring Process Design RPA & AI & BPM Sourcing KPIs Cause-Effect STP-Rate Quality SLA’s Benchmarking Personnel costs Customer Journey Cross-Selling Growth Variations Contract Management Non-Financial Risk