The Katana Graph Intelligence Platform, Part 2

By: Katana Graph

October 14, 2021

The Katana Graph Intelligence Platform, Part 2

One of the greatest obstacles to using data to solve problems is that 80% of the world’s data is unstructured. Businesses are struggling to use data to gain important and timely insights to solve pressing problems. But another equally paralyzing challenge is the congestion resulting from the sheer bulk of relevant business data. Katana Graph has a strategy and technology to help businesses with the daunting task of utilizing their data so that business can grow and produce.

Part 2 of 3: Katana Graph Intelligence Platform: Data Flow and Strategy

In part one of this three part series we introduced the Katana Graph Intelligence Platform and explained and described Katana Graph Data Platform. One of the greatest challenges in business intelligence is the unintentional creation of a data swamp causing congestion of uncurated and disorganized data. The associated lack of contextual metadata leads to information loss, data retrieval snags, and obstructed flow of important information that a business needs to run productively and grow.

With this understanding of the purpose of the Katana Graph Intelligence Platform, in part two we can now describe the Katana Graph Flow and Data Strategy.

The Katana Graph Flow is a cloud 3.0 data-flow strategy that enables the value of GraphOps on the Katana Graph Intelligence Platform. Imagine having complete command of the what, who, why, where, and how of your data infrastructure. Then picture untangling the data web to allow easy navigation and the elucidation of gaps, risks, and threats. The Katana Graph Flow blends GraphOps with common strategies in modern Data Architecture, DevOps, and MLOps.

Traditional MLOps is made up of eight interlinked layers that work consecutively:

Data Collection → Data Processing → Feature Engineering → Data Labelling → Model Design → Training → Optimization → Deployment & Monitoring

Here’s where Katana Graph Flow is indispensable: GraphsOp provides two distinct advantages to existing ML strategies: accuracy and flexibility. More context makes implicit influences explicit. Graph AI has been shown to improve the accuracy of traditional ML pipelines.

The framework provides the flexibility to alter the choice of which data is chosen to train on, without requiring an entire rearchitecting of all systems in a data pipeline. In part one, The Katana Graph Intelligence Platform, we cited a critical problem with restructuring data to accommodate a new attribute that can introduce crippling delays when timely decisions need to be made.

Let’s examine the clear-cut requirements needed when augmenting GraphAI to traditional ML pipelines. MLOps steps, in terms of data collection, processing, feature engineering, data labelling, and model design, are easier, but a different strategy is needed to take advantage of data modeling and graph flexibility.

From a business operations and costs perspective, Cloud 3.0 DevOps Strategy allows for predictable pricing and Cloud 3.0 Data Architecture makes the raw storage costs virtually free.

Cloud 3.0 DevOps and Cloud 3.0 Data Architectures then become even more valuable, making storage and processing independent, offering on-demand fine-tuning of resources on an elastic basis, and enabling architectural designs to be flexible. Budget conscious business operations will get a break in costs, having less work in architecting and restructuring data, and a savings over time.

Utilizing an underlying compute core and the fundamental characteristics of graphs, the Katana Graph Flow Strategy can treat interconnected data pipelines as a graph and serve multiple applications in a unified experience. Compared to traditional static data pipelines, Katana Graph Flow enables enterprises to explore and develop multiple use cases, applying the Katana Graph engine to diverse data views.

The Katana Graph Flow Strategy treats interconnected data pipelines as a graph that serves multiple applications. Unlike legacy static data pipelines, Flow supports multiple data views, allowing easy exploration of diverse use cases. The elegance of the Katana Graph Flow is supported by the Katana Graph Intelligence Platform, taking advantage of a serverless computing architecture to optimize resources on a per GraphOps step and application basis. This makes Katana Graph Intelligence Platform accessible, cost effective, and data decisioning timely.

The final installment of the Katana Graph Intelligence Platform three part series discusses the Katana Graph Engine.

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