Data Insights Monitor
Untangle deeply connected data
Apply graph technologies and AI to generate actionable insights in a non-invasive overlay dashboard, thereby effectively steering your data delivery programs.
Our solution
Sort through data and unlock powerful and actionable insights.
Data Insights Monitor provides data-driven insights and decision support on a complex data landscape. It empowers companies in various industries, including the financial sector, public services, and technology, media, and telecommunications, to swiftly derive actionable insights from highly connected data through graph technology, AI, and an intuitive user interface.
Use cases
Benefits
End-to-end
By linking existing data lineage across different sources and monitoring tools, DIM provides a 360-degree view of data progression.
Plug & play
With immediate operability, you begin integrating your data sources and leverage built-in recipes to hit the ground running and generate your first data insights.
Customizable recipes
Enhance the relevance of your data lineage insights with the development of incorporated business-level logic views.
Unlimited data sources
The flexible structure of a knowledge graph makes it is easy to add new data sources with unique structures and data types.
User-friendly comprehension
DIM simplifies interaction with complex data and provides intuitive data insights for further actions.
Supporting all data types
Connect an unlimited amount of data silos from different systems and include even unstructured data in the data sources.
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Capabilities
360° view on data lineage & insights
Map the entirety of your end-to-end, data-driven process and track the implementation of data requirements over time.
Effective business-in-mind implementation
Align your data lineage with your unique business needs and policies for better execution.
Greater versatility in views
Encourage multiple viewpoints and interactions with your data lineage and insights to gain even more clarity.
Context-aware analytics
Harness the power of graph-based artificial intelligence (AI) for predictive decision-making support.
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Tapping into GenAI
Enhanced Contextual Understanding: Utilize Knowledge Graphs to provide structured context and prevent misinformation in AI outputs.
Improved Accuracy and Reliability: Ground AI responses in curated, factual information from diverse data sources.
Efficient Data Management: Streamline data processes using AI and Knowledge Graph synergy for better governance.
Facilitated Knowledge Discovery: Support innovation and strategic decisions with robust information retrieval and pattern detection.
Use the power of GenAI to chat with your graph and get the most out of your data:
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Client outcomes
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.
DIM deployment timeline
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Setup the DIM environment (1-2 weeks)
In the initial stage of implementing the Data Insights Monitor (DIM), the focus is on setting up the environment and platform with basic recipes. This phase supports the proof of concept (PoC) and requires effective project management. Understanding the client business context and managing relationships are crucial for tailoring the solution to meet specific needs, laying the groundwork for successful implementation.
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Build out the graph (2-3 weeks)
In the second stage, the focus is on building out the knowledge graph by ingesting data into it. The DIM platform is connected to this initial graph to ensure seamless integration. During this phase, a pilot is run, and the design of the graph model is scaled in collaboration with stakeholders. Managing change and providing training are essential to facilitate smooth adoption. Additionally, data pipelines and scanners are built to support continuous data flow and analysis.
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Design custom recipes (3-4 weeks)
In the third stage, custom recipes are designed by writing queries that retrieve new insights, represented as graphs and tables. The DIM platform is enhanced with a set of these custom recipes to provide tailored analytics and insights. This phase involves driving alignment across stakeholder groups to ensure that everyone is on the same page. Building graph queries is crucial for extracting valuable information and supporting informed decision-making.
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Create custom views (3-4 weeks)
In the fourth stage, client-specific views are developed for their recipes on the DIM platform, incorporating custom recipes and views. UI customization is applied to meet client needs, and interactive views are designed for different stakeholders. Integration with strategic visualization tools enhances data presentation.
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Expand the graph (2-3 weeks)
In the fifth stage, the graph is expanded by adding more metadata sources to enrich the context within the DIM platform. This enrichment enhances the solution, providing broader insights by adding context to existing recipes. The success rates are monitored, and the benefits are tracked to ensure the platform's effectiveness and value.
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Deploy the solution (4-8 weeks)
In the final stage, the solution is deployed, either on-premise or cloud-based, as per the client's preference. The DIM platform is fully implemented within the client environment. Various deployment scenarios are explored to ensure optimal performance and fit. Training is provided on the maintenance of the platform to ensure its smooth operation post-deployment.
Get in touch
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