Data Management

Challenges

  • 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