Better Data Quality – Better Decisions

Perfect decisions need reliable information. Only if you have high-quality and relevant data can you avoid analysis errors and misinterpretations and tap the potential of business analytics to the full. Creating a comprehensive data quality management system is therefore not an end in itself, but a key requirement to:

  • Observe compliance regulations, for example, the minimum requirements for risk management, Basel II and III, Solvency II, and the Money Laundering Act
  • Manage and control risks effectively, for example, using scoring models or fraud management
  • Receive consistent and transparent results when determining key business figures, for example, when calculating fuel quantities in the energy sector
  • Ensure optimal process orientation along the entire value chain, thus gaining competitive advantage and saving costs

mayato_dqm_en_smallData quality management can only be successful in the long term if it is not seen as a purely IT topic, but is implemented across departments and becomes an integral part of business. From the company perspective, this means that the three dimensions of data quality management – strategy, governance, and technology – are viewed holistically and aligned with each other carefully.

Would you like to increase the quality of your data and profit from the associated benefits? mayato can offer you comprehensive services to design and implement your individual solutions. For example, we support you in:

  • Status quo analyses to identify and cleanse areas critical for data quality, for example, in the form of data profiling, data cleansing, and enhancing
  • Drawing up a tailored DQM strategy with a focus on the business goals to be achieved
  • Transfer of your lessons learned from DQM projects to process-oriented operations
  • Enhancement of your DQM architecture, for example, by implementing metadata management or by implementing internal control systems for proactively avoiding DQ problems
  • Long-term integration of data quality management within the organization and within your business processes, for example, through process integration and data governance

If that sounds interesting and you’d like to know more about data quality management, e-mail us at This email address is being protected from spambots. You need JavaScript enabled to view it. .