Complex regulatory environment as well as transparency and control requirements for institutional investors (extent, frequency) require professionalisation of data management
Current gaps
Deficits in data quality
Difficulty in short-term provision of data
Redundancies due to multitude of parties involved
Provision of data from various internal and external sources generates additional data qualities as well as interfaces leading to sources of error
Different processing systems increase the complexity and cause a higher control effort
Project approach
Screening of data streams (sources, processing, recipient)
Analysis and assessment of reporting requirements
Analysis of processes, structures and systems
Deduction and evaluation of structure options consideration of make or buy (sourcing options)
Preparation and evaluation of business cases based on determined criteria like ensuring of highest-possible flexibility, efficiency as well as profitability
Deduction Target Operating Model (TOM)
Determination and description of realisation measures as well as organisational and resource-related consequences
Results
Improvement resp. ensuring of data quality (content, frequencies, timeline), transparent data streams
Efficient process model (minimisation of sources of error and redundancies)
Adapt value creation depth
Professionalised data management – efficient and economic
