KoGen Savings

KoGen, with its KGNN architecture, significantly reduces expenses on IBM Power 11 systems, 

Cost savings are achieved by automating labor-intensive processes and optimizing hardware utilization. The platform achieves this by replacing traditional, rigid data pipelines with an intelligent, graph-based system that works seamlessly with the Power 11's architecture.

ETL Cost Reduction

Traditionally, the Extract, Transform, and Load (ETL) process is a major expense, requiring large teams of data engineers to manually write and maintain scripts for data pipelines. KoGen's approach fundamentally changes this by:

• Automated Ingestion: The platform uses its KGNN to automatically ingest data from diverse sources, such as documents, databases, and APIs. It analyzes the raw data and builds a knowledge graph that captures the relationships and context, bypassing the need for extensive, hand-coded extraction logic.

• Intelligent Transformation: The "T" in ETL is the most costly and time-consuming step. KoGen automates this by inferring data relationships and semantic meaning. Instead of rigid, pre-defined rules, the knowledge graph dynamically organizes and connects data, making it ready for analysis or AI models without manual transformation.

• Reduced Data Redundancy: By creating a single, interconnected knowledge graph, KoGen eliminates the need for multiple data marts and warehouses. This reduces storage costs and the complexity of maintaining redundant data copies, which are common in traditional ETL environments.

This automation means less time and fewer people are needed to prepare data, leading to a substantial decrease in operational costs.

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FTE Cost Reduction

The labor-intensive nature of data management and AI development is a primary driver of high Full-Time Equivalent (FTE) costs. KoGen addresses this by:

• Streamlining Data Science Workflows: By providing a clean, connected knowledge graph, KoGen allows data scientists and AI developers to spend less time on data wrangling and more time on building models and generating insights. This increased efficiency means a smaller team can accomplish more.

• Enabling Citizen Data Scientists: The platform's intuitive, graph-based interface allows users with less technical expertise to explore data and build simple applications. This reduces the reliance on highly paid data engineers and analysts for routine tasks.

• Reducing Operational and Maintenance Staff: The automated nature of the KGNN reduces the need for ongoing maintenance and support staff. The system is designed to be self-healing and continuously learns from new data, minimizing the need for manual oversight and problem-solving.

The Role of IBM Power 11

The synergy with IBM Power 11 is crucial for maximizing these savings. IBM Power 11 provides the powerful, on-premises infrastructure that allows KoGen to operate efficiently and securely. The server's built-in AI accelerators and high-performance architecture enable the KGNN to process immense amounts of data and perform complex graph traversals at speeds that would otherwise require more expensive and less efficient hardware. This combination of KoGen's software automation and Power 11's hardware efficiency leads to an overall reduction in total cost of ownership (TCO) for data and AI workloads.