niologic consolidated project management into Kanban flows for the recently formed BI team. In addition, regular meetings and common communication channels were structured to ensure correspondence among the BI experts.
In a further step, the requirements were structured from a business perspective and modelled into a common data model (Snowflake, Kimball approach). Dimension data were to be presented equally in all subsidiaries.
Subsequently, several product hierarchies were introduced and stored in a structured and maintainable manner using Microsoft Master Data Services™ (MDS). In this process, each product was assigned to several product hierarchies. Different hierarchies were thus introduced to administration in the subsidiary, to product management and the financial area. Through a special modelling of the product hierarchy, key figures were made calculable on every branch and every level of the product hierarchy. By using an InMemory database, all combinations of products and key figures could be calculated.
After the data for the first milestone had initially been supplied by the subsidiaries as a data export (CSV), a standardized data view was created in each subsidiary in a further step.
Parallel to the teams from BI and Controlling, niologic worked with the IT department on a connection of the network infrastructure and firewalls between the BI area and subsidiaries (mostly VPN connections, NAT, firewalls).
After the technical infrastructure of the network and databases was in place in the subsidiaries and also the parent company, ETL processes were created to load transaction data for accounting and controlling on a daily basis and to update the product hierarchy.
In addition to the normal KPI reporting, corresponding calculations for customer inflows and outflows as well as accruals according to IFRS/HGB and US-GAAP were prepared. Furthermore, reports on the quality assurance of the ETL process and data quality were implemented successfully.
In addition, historical invoice data and EOL products were restored during the creation of the data insights.