Connected Inventory

Construct a network of digital assets by linking your organization’s data.

Implement a Connected Inventory of enterprise data assets, based on a knowledge graph, to get business insights about the current status and trends, risk and opportunities, based on a holistic interrelated view of all enterprise assets.

Provide a hub for the federated management of all your information assets and get access to the answers you need in seconds to save time and resources, and to address issues in real time.


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How You’ll Benefit

Improve capability:

  • Discover and identify the key assets of the business;
  • Structure, unify and standardize meaning;
  • Measure quantitate and qualitative information;
  • Analyze insights based on the holistic view of the enterprise;
  • Predict risk and opportunity scenarios.

Enhance control:

  • Look at real-time big-picture and drill down insights;
  • View key success/failure factors;
  • Analyze performance and ROI;
  • Audit and demonstrate ongoing compliance;
  • Enable risk identification, measurement and management;
  • Assess company capabilities.

Reduce cost:

  • Achieve cost optimization of risk, controls, time, repeatable manual efforts, physical and IT infrastructure expenses, etc.
  • Enjoy new revenue streams by adding new and improved monetization of existing information and data assets, resulting in new products, services and ways of doing business.

Increase capacity:

  • Unify – have a consistent view of the enterprise assets;
  • Standardize – comply with standards and regulations, reduce risk error;
  • Automate – get the best practices of the enterprise;
  • Scale – perform on-demand at speed and scale;
  • Be agile – iteratively improve and extend in parallel;
  • Comply – no vendor lock-in and technical W3C standards compliance.

Typical data assets of financial institutions:

  • Services and Product Inventory – a master repository of the characteristics and associated risks of products or services offered to customers;
  • Financial Asset Inventory – non-physical assets the value of which is derived from a contractual claim such as bank deposits, bonds and stocks;
  • Data Asset Inventory – the set of data assets held such as data and its associated metadata that are of value to the enterprise;
  • Software and Technology Product Inventory – a comprehensive list of the hardware and software applications, technology products and hardware resources utilized;
  • Other – such as Human Capital, Model and Scenario, Report, Governance, Business and Capital Assets Inventories.

How it Works

Semantic data modeling

  • Standardize the “meaning” of your information assets across the entire enterprise and across different domains.
  • Enable clear identification of the assets with all their inherent relationships as they appear in real-life.
  • Include additional information assets, regulatory obligations, data sources, etc. as your graph data model evolves with your business demands.

Semantic data integration

  • Integrate your information assets virtually while keeping your individual legacy systems and their resources.
  • Adopt a unified access point to all your information assets by integrating both structured and unstructured data.
  • Apply reasoning on data at scale to infer new facts from the available data.



Data federation

  • Implement data integration strategy, based on the data quality and consumption patterns;
  • Utilize data federation, virtualization, integration with indices, message buses, document stores and third-party systems to ingest data;
  • Provide semantically aligned model for data consumption and reuse.

Advanced analytics

  • Analyze and predict impact in various scenarios based on high-quality data (technological risk, regulatory and political changes, crisis and disaster events, etc.).
  • Receive up-to-date quantitative and qualitative information about your asset inventories (products, financial assets, data assets, software and hardware, human capital, etc.).
  • Extract powerful and relevant insights from your analytics by putting your information into context and drawing new conclusions from it.

Case Studies

Products, Solutions and Services Involved


Semantic Data Modeling

Knowledge Graph Enrichment