“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.

The post Implementing Context Graphs: A 5-Point Framework for Transformative Business Insights appeared first on Tiger Analytics.

The company’s traditional methods of analysis, rooted in linear data-crunching, were proving to be insufficient. They pointed to various potential causes: a shipment of substandard raw materials, a series of human errors, unexpected deviations in manufacturing processes, mismatches in component ratios, and even inconsistent additives in packaging materials. The list was exhaustive, but the connections were elusive.

The issue was complex-no single factor was the culprit. E needed to trace and compare the key influencers and their patterns, not just within a single time frame but across the tumultuous period between 2021 and 2022. The domino effect of one small issue escalating into a full-blown crisis was becoming a daunting reality.

To trace the key influencers and their patterns across the tumultuous period between 2021 and 2022, E needed a tool that could capture and analyze the intricate relationships within the data. At Tiger Analytics, we recognized the limitations of conventional approaches and introduced the concept of the Product Knowledge Graph, powered by Neo4j. The concept of the Context Graph, a term we coined to describe a specialized graph-based data structure. This specialized sub-graph from the Master Graph emphasized the contextual information and intricate connections specific to the issue at hand. It provided a visual and analytical representation that weighted different factors and their interrelations.

The Context Graph illuminated the crucial 20% of factors that were contributing to 80% of the problems—the Pareto Principle in action. By mapping out the entire journey from raw material to customer feedback, the Context Graph enabled E to pinpoint the specific combinations of factors that were causing the majority of the complaints. With this clarity, E implemented targeted solutions to the most impactful issues.

What is a Context Graph and Why we need it?

In today’s complex business landscape, traditional databases often fall short in revealing crucial relationships within data. Context Graphs address this limitation by connecting diverse data points, offering a comprehensive view of your business ecosystem.

“The term Context Graph refers to a graph-based data structure (sub-graph from Master Graph) used to represent the contextual information, relationships, or connections between data entities, events, and processes at specific points at the time. It might be used in various applications, such as enhancing natural language understanding, recommendation systems, or improving the contextual awareness of artificial intelligence.”

At Tiger Analytics, we combine graph technology with Large Language Models to build Product Knowledge Graphs, unifying various data silos like Customer, Batch, Material, and more. The power of Context Graphs lies in their ability to facilitate efficient search and analysis from any starting point. Users can easily query the graph to uncover hidden insights, enhance predictive analytics, and improve decision-making across various business functions.

By embracing Context Graphs, businesses gain a deeper understanding of their operations and customer interactions, paving the way for more informed strategies and improved outcomes.

This comprehensive approach is set to redefine the landscape of data-driven decision-making, paving the way for enhanced predictive analytics, risk management, and customer experience.

6 Ways Graphs Enhance Data Analytics

1. Making Connections Clear: If data is like a bunch of dots, by itself, each dot doesn’t tell you much. A Context Graph connects these dots to show how they’re related. This is like drawing lines between the dots to make a clear picture.

2. Understanding the Big Picture: In complex situations, just knowing the facts (like numbers and dates) isn’t enough. You need to understand how these facts affect each other. Context Graphs show these relationships, helping you see the whole story.

3. Finding Hidden Patterns: Sometimes, important insights are hidden in the way different pieces of data are connected. Context Graphs can reveal these patterns. For example, in a business, you might discover that when more people visit your website (one piece of data), sales in a certain region go up (another piece of data). Without seeing the connection, you might miss this insight.

4. Quick Problem-Solving: When something goes wrong, like a drop in product quality, a Context Graph can quickly show where the problem might be coming from. It connects data from different parts of the process (like raw material quality, production dates, and supplier information) to help find the source of the issue.

5. Better Predictions and Decisions: By understanding how different pieces of data are connected, businesses can make smarter predictions and decisions. For example, they can forecast which product combo will be popular in the future or decide where to invest their resources for the best results.

6. Enhancing Artificial Intelligence and Machine Learning: Context Graphs feed AI and machine learning systems with rich, connected data. This helps these systems make more accurate and context-aware decisions, like identifying fraud in financial transactions or personalizing recommendations for customers.

The power of Context Graphs in solving complex business problems is clear. By illuminating hidden connections and patterns in data, these graph-based structures offer a new approach to decision-making and problem-solving. From E’s product quality crisis to broader applications in predictive analytics and AI, Context Graphs are changing how businesses understand and utilize their data.

In Part 2 of this series, we’ll delve deeper into the practical aspects, exploring a framework approach to implementing these powerful graph structures in your organization.

The post Connected Context: Introducing Product Knowledge Graphs for Smarter Business Decisions appeared first on Tiger Analytics.