Transparency, Accelerated Processes, Reliable Data for Informed Decisions
Good business decisions highly rely on analytical insights derived from current and correct data. Our solution guarantees you sustainable and automated Data Quality Management processes, strengthening the trust in your decision making. ifb's Data Quality Reporting Framework allows you to store and combine your data from various sources like SAP Information Steward, SAP PowerDesigner, Issue Management and other applications in just one repository. Thus, you can easily access all the data quality relevant information. Our solution comes with pre-defined reports in SAP Analytics Cloud and a pre-configured data model accelerating time to Go Live significantly. With our expertise and experience we offer you a cross-project coordination and implementation of all your Data Quality requirements.
- Benefit from best practices derived from relevant project experiences
- Establish sustainable and automated DQM processes
by gaining access to pre-defined DQM data integration steps and reports which enable you to automate your data quality reporting while included guidelines keep the complete process sustainable with minimal effort.
- Rely on your data and strengthen the trust in your decision making
through effective tools that keep your data quality on a high level.
- Realize a faster time-to-value
thanks to pre-defined data models and ready-made deliverables underlying your data quality reports and that can easily be deployed to your SAP or other system environments.
- Improve and accelerate your reporting processes
through coordinated activities enhancing your capability to process your data quality KPIs as well as data faults. Gain a much faster insight into your data quality.
- Recognize and reduce data quality errors early and significantly
thanks to a clear overview of functions and data lineage, providing you and everybody involved in the DQM process full transparancy regarding the breakpoints in your companys data flow and reducing the time and effort for troubleshooting.
- Introduction of an individualized DQ Reporting
A predefined, yet highly adjustable reporting platform based on SAC, provides the tools to design your DQ reporting to your needs while the business content toolbox gives you direction and ideas as to what is possible.
- Interface to issue management
Use your issue management tool to track data quality issues and connect it to our repository. Monitor the duration of the error processing.
- Interface to modern methods and tools (AI, ML)
Our solution is highly adaptable to your existing application landscape and can easily be integrated. It maintains all the features of your current, familiar system while adding structure and quality to your DQM and DQ reporting.
- DQM integration in all processes and throughout organization
The DQ repository enables you to spread knowledge and responsibilty throughout your company thanks to a set of proven and well defined data quality processes ready for deployment.
- Overview of impact on KPIs
A KPI Matrix allows you to define the impact of a single data quality result on a KPI, enabling you to aggregate the data quality scores of attributes on different levels.
- Leverage Pre-defined reports in SAP Analytics Cloud
- You receive preconfigured data model for DQ Repository and rules catalogue for SAP PowerDesigner and SAP Information Steward
- Benefit from state-of-the art analytics based on Artificial Intelligence
- The Package includes specification of technical design, assessment methodologies, installation guide, test concepts with defined use cases
- Introduction and establishment of a DQ process: Design and implementation of data and integration architecture with the SAP PowerDesigner
- Design, management and documentation of all control-relevant data flows, communication
- Design and introduction of a data quality management: tool evaluation, piloting and implementation
- Cross-project coordination and implementation of the DQ requirements
- Design and implementation of a data quality management including integration with development processes and data governance