Data Insights Monitor

A large manufacturing company had difficulties with understanding their complex data sources and their connections due to data silos. This could have led to potential discrepancies during changes like migration and decommissioning systems. Improved configuration management is crucial for compliance, production growth, and customer support.

DIM was used as solution to provide deeper insights into their interconnected data, aiding informed decisions in complex data landscapes.​

A production company was experiencing a data quality issue in pricing data, which caused their Bills Of Material (BOM) to lack accuracy. The absence of valid prices blocked proper BOM cost build-up and prevented the release of machines to work order. It is challenging to have a quantifiable overview on the impact of missing pricing information and there was no (automated) solution in place to resolve their missing pricing information.

DIM helped to make an analysis on the parent-child relationships and subsequently assess the impact of the data quality issues.

A large bank, grappling with the limitations of spreadsheet-based Risk & Control frameworks, faced difficulties in visualizing the risk landscape and identifying overlaps and dependencies. Discussions about the risk and control landscape with stakeholders were also difficult due to the constraints of Excel.

With the implementation of DIM, the bank's risk management was revolutionized. DIM provided a comprehensive and visual overview of risks and controls, facilitated seamless mapping to external regulations, and enabled detection of hidden patterns and control redundancies. This tool significantly modernized the traditional Excel-based control frameworks, marking a significant milestone for the bank's risk management strategy.

A major bank sought to enhance its end-to-end risk reporting and modeling data programs. Despite various team-specific dashboards for data insights and progress tracking, management requested an overarching dashboard for a comprehensive view. A data control process was essential to identify vital data elements and monitor gaps, missing links, redundancies, and inconsistencies in the existing connected data.

DIM helped analyze the bank's complex connected (meta)data in scalable manner to control end-to-end data mappings and to find data quality patterns while preserving the required regulatory & compliance lineage.