During the last decades, many companies have worked intensively on the expansion and continuous optimisation of their business process environment and their mapping in operative systems such as ERP, CRM, …. In many companies, the topic of data quality and data flow has not been established in a sustainable manner from a professional, organisational and technical point of view. However, a reliable exchange of information – within the company or with external partners (e.g., customers, suppliers, …) – is only possible with high-quality data. Thus, an efficient data flow is a critical success factor for process efficiency and represents an essential foundation for an integral support of business cases. Just as ‘data flow before material flow’ applies in logistics, ‘data flow before activity flow / processes’ applies to business processes in general.
Our Data Management Competences
In the Data Management Competence Centre, we support companies in the areas of ‘information processing’ and ‘information flow’.
- By examining the quality of master and transaction data, the data models and the data flows in the application environment
- Through a reorganisation, optimisation and migration of data based on the data quality investigations
- In the existing application environment
- During the implementation of new applications
- By establishing an optimal data flow from the operative applications to new or existing business intelligence solutions, and
- By conceptualising and implementing ‘product life cycle’ projects (i.e., linking CAD / CAM, PDM and systems engineering)
Electronic Business (EB)
Rationalisierung (FIR) e.V.
We offer support with the help of well-established methods and services in the following areas:
- Data analysis, data structures and dataflows
- Data cleansing of defective data and harmonisation of heterogenous databases
- Setup of clear structures (e.g., of variants and versions)
- Data migration
- Implementation of ‘data services’
- Organisational entrenchment of the data management as a company’s competence center by defining appropriate support processes, roles and key figures for a continuing data quality management