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Extract meaningful information from data

The goal of Data Value Management is to extract valuable information from the enormous amounts of data, which is important to lead the company into the future. Building a data value chain makes it possible to monetize data.

To be able to draw the right conclusions from the qualitatively enriched data and to implement data-based decisions regarding business processes and models, adequate skills and processes, as well as effective data governance, must also be established permanently.

Understanding and managing data as capital

To make the best decisions, you need the right information. Today, the required data is generated everywhere, and the responsibility for its collection and provision must be established in the company. However, the enormous amount of data is also a challenge. To obtain the right information, it must be prepared, qualified and analyzed. If the right conclusions can be drawn from the data, they offer the potential to transform the entire company and create new value-adding business models.

Data Quality Management & Data Analytics

The value of the data is also based on its quality. Whether master data for your ERP or the analysis of your customer data based on Big Data: High data quality is necessary to achieve the highest possible added value. The establishment of a data quality management system creates the basis for sustainably high data quality. With the involvement of the right employees, processes, and technologies, the information that is relevant and decisive for action can be specifically compiled and enriched from the mass of incoming data. The construction of a well-thought-out, interlocking, and target-oriented data lifecycle provides the framework for obtaining valuable results from the data.

Data Security & Privacy Strategy

Data as a basis for new business models and thus for further competitive advantages, represent an immense value. The protection of data and privacy is not only required by law but should be seen as safeguarding the value of the company. The strategic protection of the collected data is, therefore, a cornerstone of all data strategies.

Based on our many years of experience in the various levels of data value management, we support you in strategy development, project planning, and sustainable implementation and work with you to design the path to monetize your data.

 

Together with you, we will achieve this - and even more:

  • Data value assessment
  • Data procurement strategy
  • Data Security & Privacy Strategy
  • Master Data Governance Approach
  • Data quality assessment
  • Data traceability
  • TOM for a Data-Driven Organization
  • S/4 Data Transition Approach
  • MDM tool selection
  • Data analytics platform selection
  • Master Data Governance Establishment
  • Cloud Technology & Big Data / IoT Analytics
  • Data Cleansing & Harmonization
  • Data migration
  • Data analytics use cases
  • Cloud Infrastructure Setup and Maintenance
  • MDM Tool Implementation

Your contact persons

Stefan Bronzel Head of Data Value Management
Predictive Analytics / Churn Management, Data Analytics Use Cases
Porträtfoto von Andreas Zschimmer
Andreas Zschimmer ERP Master Data Management, S/4 Data Transition Approach, MDM Tool Selection
Christian Holthaus Master Data Governance, Business Process Management, ERP Integration
Jörg Schwarzländer Data Security & Privacy Strategy​
Stark Burns Data Analytics, Machine Learning & AI, Data Quality & Monetization
Stephan Weber Data Migration, Master Data Governance,
Master Data Strategy
Thomas Reckewell Data Management, Data Quality and Data Monetization, Data Analytics and BI Solutions