Remember E, the product manager who used Context Graphs to unravel a complex web of customer complaints? Her success story inspired a company-wide shift in data-driven decision-making.
“This approach could change everything,” her CEO remarked during her presentation. “How do we implement it across our entire operation?”
E’s answer lay in a comprehensive framework designed to unlock the full potential of their data. In this article, we’ll explore Tiger Analytics’ innovative 5-point Graph Value framework – a roadmap that guides businesses from establishing a foundational Knowledge Graph to leveraging advanced AI capabilities for deeper insights.
The 5-Point Graph Value
At Tiger Analytics, we have identified a connected 5-point Graph Value framework that enables businesses to unlock the true potential of their data through a phased approach, leading to transformative insights and decision-making. The 5-point Graph Value framework consists of three distinct phases, each building upon the previous one to create a comprehensive and powerful solution for data-driven insights.
Phase 1: Knowledge Graph (Base)
The first phase focuses on establishing a solid foundation with the Knowledge Graph. This graph serves as the base, connecting all the relevant data points and creating a unified view of the business ecosystem. By integrating data from various sources and establishing relationships between entities, the Knowledge Graph enables businesses to gain a holistic understanding of their operations.
In this phase, two key scenarios demonstrate the power of the Knowledge Graph:
1. Connect All Dots
Role-based Universal View: Gaining a Holistic Understanding of the Business
A business user needs to see a connected view of Product, Plant, Material, Quantity, Inspection, Results, Vendor, PO, and Customer complaints. With a Knowledge Graph, this becomes a reality. By integrating data from various sources and establishing relationships between entities, the Knowledge Graph provides a comprehensive, unified view of the product ecosystem. This enables business users to gain a holistic understanding of the factors influencing product performance and customer satisfaction, leading to context-based insights for unbiased actions.
2. Trace & Traverse
Trace ‘Where Things’: Context-based Insights for R&D Lead
An R&D Lead wants to check Package material types and their headspace combination patterns with dry chicken batches processed in the last 3 months. With a Knowledge Graph, this information can be easily traced and traversed. The graph structure allows for efficient navigation and exploration of the interconnected data, enabling the R&D Lead to identify patterns and insights that would otherwise be hidden in traditional data silos. This trace and traverse capability empowers the R&D Lead to make informed decisions based on a comprehensive understanding of the data landscape.
Phase 2: Connected Context Graph
Building upon the Knowledge Graph, the second phase introduces the Connected Context Graph. This graph incorporates the temporal aspect of data, allowing businesses to discover patterns, track changes over time, and identify influential entities within their network.
Two scenarios showcase the value of the Connected Context Graph
3. Discover more Paths & Patterns
Uncover Patterns: Change History and its weighted impacts for an Audit
An auditor wants to see all the changes that happened for a given product between 2021 and 2023. With a Connected Context Graph, this becomes possible. The graph captures the temporal aspect of data, allowing for the discovery of patterns and changes over time. This enables the auditor to identify significant modifications, track the evolution of the product, and uncover potential areas of concern. The Connected Context Graph provides valuable insights into the change history and its weighted impacts, empowering the auditor to make informed decisions and take necessary actions.
4. Community Network
Network Community: Identifying Influencers and Optimizing Processes
A business user wants to perform self-discovery on the Manufacturer and Vendor network for a specific Plant, Products, and Material categories within a specific time window. The Connected Context Graph enables the identification of community networks, revealing the relationships and interdependencies between various entities. This allows the business user to identify key influencers, critical suppliers, and potential risk factors within the network. By understanding the influential entities and their impact on the supply chain, businesses can optimize their processes and make strategic decisions to mitigate risks and improve overall performance.
Phase 3: Auto-Answers with AI
The final phase of the 5-point Graph Value framework takes the insights derived from the Knowledge Graph and Connected Context Graph to the next level by augmenting them with AI capabilities. This phase focuses on leveraging AI algorithms to identify critical paths, optimize supply chain efficiency, and provide automated answers to complex business questions.
The scenario in this phase illustrates the power of AI integration:
5. Augment with AI
Optimizing Supply Chain Critical Paths and Efficiency
A Transformation Lead wants to identify all the critical paths across the supply chain to improve green scores and avoid unplanned plant shutdowns. By augmenting the Knowledge Graph with AI capabilities, this becomes achievable. AI algorithms can analyze the graph structure, identify critical paths, and provide recommendations for optimization. This enables the Transformation Lead to make data-driven decisions, minimize risks, and improve overall operational efficiency. The integration of AI with the Knowledge Graph opens up new possibilities for business process optimization, workflow streamlining, and value creation, empowering organizations to stay ahead in today’s competitive landscape.
A 360-Degree View of Your Product with Context Graphs
By leveraging Knowledge Graphs, businesses can unlock a complete 360-degree view of their products, encompassing every aspect from raw materials to customer feedback. Graph capabilities enable organizations to explore the intricate relationships between entities, uncover hidden patterns, and gain a deeper understanding of the factors influencing product performance. From context-based search using natural language to visual outlier detection and link prediction, graph capabilities empower businesses to ask complex questions, simulate scenarios, and make data-driven decisions with confidence. In the table below, we will delve into the various graph capabilities that can enhace the way you manage and optimize your products.
Use Cases of Context Graphs Across Your Product
Graph Implementation Roadmap
The adoption of Context Graphs follows a structured roadmap, encompassing various levels of data integration and analysis:
- Connected View (Level 1): The foundational step involves creating a Knowledge Graph (KG) that links disparate enterprise data sources, enabling traceability from customer complaints to specific deviations in materials or processes.
- Deep View (Level 2): This level delves deeper into the data, uncovering hidden insights and implicit relationships through pattern matching and sequence analysis.
- Global View (Level 3): The focus expands to a global perspective, identifying overarching patterns and predictive insights across the entire network structure.
- ML View (Level 4): Leveraging machine learning, this level enhances predictive capabilities by identifying key features and relationships that may not be immediately apparent.
- AI View (Level 5): The pinnacle of the roadmap integrates AI for unbiased, explainable insights, using natural language processing to facilitate self-discovery and proactive decision-making.
Leveraging LLMs and KGs
A significant advancement in Context Graphs is the integration of Large Language Models (LLMs) with Knowledge Graphs (KGs), addressing challenges such as knowledge cutoffs, data privacy, and the need for domain-specific insights. This synergy enhances the accuracy of insights generated, enabling more intelligent search capabilities, self-service analytics, and the construction of KGs from unstructured data.
Context Graph queries are revolutionizing our machine learning and AI systems. They are enabling these systems to make informed and nuanced decisions swiftly. With these tools, we can preemptively identify and analyze similar patterns or paths in raw materials lots even before they commence the manufacturing process.
This need to understand the connections between disparate data points is reshaping how we store, connect, and interpret data, equipping us with the context needed for more proactive and real-time decision-making. The evolution in how we handle data is paving the way for a future where immediate, context-aware decision-making becomes a practical reality.
