Performance Per Watt: kgnn-driven knowledge graph eco-system saves money on ibm power 11

 Kgnn-driven knowledge graph eco-system saves money on IBM Power 11

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Controlling costs and Improving Data

The KGNn-driven knowledge graph ecosystem, running on IBM Power 11, saves money for enterprise clients in several key ways;  1. Drastically reducing operational costs (ETL) and 2. Accelerating time-to-value (TTV).

With Automating the data ingestion, labeling and filtering functions. KGNN generates the knowledge graph that can act as the Enterprise Data Framework.  By removing the human interaction the ingestion level is sped up from "months to minutes".  

1. Automation and Reduced Labor Costs

• Automated Data Ingestion: The system's "Automated Ingestion" and "Unstructured Data" components eliminate the need for manual data processing and complex ETL (Extract, Transform, Load) pipelines. Instead of a team of data engineers spending weeks or months creating and maintaining data pipelines for each new dataset, the system automatically ingests, cleans, and connects data.

• Self-Constructing Knowledge Graph: The KGNN engine automatically builds a knowledge graph from the ingested data, including from "Siloed Data" and "AI Video Feeds." This autonomous process removes the immense human effort and expertise traditionally required to manually define a data schema and map relationships, which can take months to complete. One benchmark showed the KGNN system completing a data mapping task in 35 minutes that would have taken 55-85 hours of manual work.

• Reduced Need for Data Scientists: By creating "AI-ready" data, the system reduces the time data scientists spend on data preparation—a task that often consumes 80% of their time. This allows them to focus on higher-value tasks like model development and analysis, increasing their productivity and the ROI of their salaries.

2. Infrastructure and Energy Efficiency

• No GPUs Required: IBM Power 11 servers are designed with a Math Matrix Accelerator (MMA) specifically to handle AI and machine learning workloads without the need for expensive, power-hungry GPUs. Since the Equitus KGNN system is "Power-native," it leverages this in-core AI acceleration. This translates to significant savings on hardware, energy consumption, and cooling costs, especially for large-scale AI projects.

• Performance Per Watt: IBM has stated that Power 11 provides twice the performance per watt of comparable x86 servers. This energy efficiency directly reduces ongoing utility bills, which can be a major expense for data centers.

• Consolidated Footprint: The high performance of the IBM Power 11 system, combined with the efficiency of the KGNN, allows organizations to handle massive workloads with fewer servers, reducing the physical footprint, and associated costs of a data center.

3. Business Value and ROI Acceleration

• Faster Time-to-Insight: By automating the entire data-to-knowledge process, the KGNN system dramatically reduces the time it takes to get from raw data to actionable insights. This rapid "Data Analytics" allows clients to make faster, more informed decisions, respond to market changes, and gain a competitive advantage.

• Enhanced AI and Decision-Making: The knowledge graph provides context and explainability for AI models ("AI Agents" in the diagram), leading to more accurate and reliable outputs. This reduces the costs associated with poor decisions, incorrect predictions, and flawed AI-driven processes.

• Zero Planned Downtime: IBM Power 11 systems promise high availability and can perform maintenance without taking applications offline. For mission-critical systems, this "zero planned downtime" saves money by avoiding service interruptions, which can lead to significant financial losses.