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Data transformation succeeds when business,

operational and technical management go hand in hand Digital World System

Architecture Real/Analog World Operational

management level Technical

management level Business

management level Data

Strategy Information

Governance Essential Questions System

Architecture Data

Strategy Information

Governance How can our business data help us

improve? How can we assess the value of data and

make it a long-term priority? How can we ensure continued data transparency and

accessibility of the data universe in the required quality? Which roles, responsibilities, processes & tools

are required? Which technical systems & architectures can we

use to manage our data treasure? How can these systems be efficiently developed &

operated? Targeted Results Everyone understands the commercial value of the data Data utilisation is anchored within the business

model & managed systematically The data universe is transparent and mastered

through metadata management Roles, responsibilities, processes and tools

are embedded Technical systems and architectures provide

efficient access to the data treasure? The costs and effort of data management are

appropriate. Data

Strategy Information

Governance System

Architecture The three management levels of the data & information-centric economy

Information governance is the central key Maintanance Data

Strategy Information

Governance
System

Architecture
Business management level Operational management level Technical management level Information governance is key

to synchronising the

real/analogue world

and the digital world.


It facilitates data transparency,

quality, reliability and flexible

consistency between the

the digital and analog world.

digitalen und analogen Welt

ermöglicht. Requirements
Management
Business
Capabilities

Business
Architecture


Metadata
Management


Data
Analysis
Information
Management
Data Quality
Management
Securety, Risk,
Compliance
konformität
Use Cases Development Standards Management IT Capabilities The target utilisation rank defines the degree of maturity stages

company is aiming for across all management levels The W&R Utilisation Rank Model has four stages of development, culminating in ‘data champion’. Differentiation into four categories allows for precise derivation of the target maturity. The steps required to implement the transformation efficiently are then determined from the company’s current utilisation rank and the derived target. Data Strategy Information Governance System Architecture Business management level Operational management level Technical management level DATA

ISLAND DATA

HUB DATA

NUGGETS DATA

CHAMPION Data in silos - retrospective at best & requires significant effort to leverage Data (partially) linked & usable for selected business purposes Actively managed data -
valuable data nuggets found
on a regular basis Comprehensive data automation - available in self-service for business users Failure to recognize the value of data "Data is an effort" Specific roles, tasks or responsibilities for data management not established
Decentral operational systems & individual data processing ("departmental Exel")
Value creation from data is appreciated - targeted leverage of potential in isolated cases
des Potentials in Einzelfällen Sporadic definition of data
responsibilities, limited standardisation of data
quality and data transparency Integrated data warehousing,
active architecture management with guidelines & development concepts Value creation from data is appreciated - targeted leverage of potential in isolated cases
des Potentials in Einzelfällen Data responsibility fully demonstrated throughout company, automated metadata results in real-time data transparency Streaming of Big Data,
end-to end dynamic
architecture demand management Data is systematically
exploited & constributed to
optimising performance Data responsibility clear
throughout company, high level
of data transparency through
metadata management Big Data, structured & unstructured mass data, automated management of the system architecture What we truly believe in: Characteristics of successful data initiatives Commitment Fast Success Sustainability Anchoring Join Objectives Hero Use Cases Flexible Data Lab Appropriate Data Complete Data Map Future Proof System Open Communication Interdisciplinary Collaboration Comprehensive Skills The organization is ready to
to leverage its valuable data treasure Business-relevant use cases
exist and are actionable Explorative implementation of data
ideas (lean, agil and hands-on)
is feasible Required data is available in
the proper quality Guidelines ensure systemreuseability
& scalability Guidelines ensure systemreuseability
& scalability Business, operational & technical
levels are synchronized Domain Knowledge, data
understanding & tech expertise work together Data landscape gets navigation-ready
through metadata management ~1-2 Monate ~12+ Monate ~3-6 Monate The Data & Information-Centric Economy journey leads from

the initial assessment to sustainable standard operation Standard Operation Scaling Implementation of
Prototypes Target Design Sustainable Governance Use Case Prioritization Assessment Scalable Systems

& Architectures Initial Implementation Vision Implementation of Roadmap Data transparency,
metadata management,
automation Roles, responsibilities &
processes for data
management Selectionof business-relevant data use cases (value & feasibility)
Diagnosis of data inventory &
current data utilisation
Ambition for future level of data
utilisation within the company
('data islands' ->'data champion')

Requirements management, data
engineering solution design
implementation as Minimum Viable System as a „Minimum Viable System“
Data reference & target architecture,
IT guidelines & development concepts System architecture with focus on reusability,
sustainability, automation
automation Action plan &
data use cases (long list)