Data Driven Operating Models
Theodor Schabicki
April 2019
Enabled by Process Mining

LEGACY

DATA

CULTURE
NEW PIC

Culture ORGANIZATION

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

“Transformation into a fully
digital business cannot exist without
Data Driven Operations”

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
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
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

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
Process Mining is the
foundation and enables
everything that comes
afterwards
Over to Process Mining
and why it is so important

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