A Conceptual Framework for a Flexible Data Analytics Network

Daniel Tebernum, Dustin Chabrowski

Abstract

It is becoming increasingly important for enterprises to generate insights into their own data and thus make business decisions based on it. A common way to generate insights is to collect the available data and use suitable analysis methods to process and prepare it so that decisions can be made faster and with more confidence. This can be computational and storage intensive and is therefore often outsourced to cloud services or a local server setup. With regards to data sovereignty, bandwidth limitations, and potentially high charges, this does not always appear to be a good solution at all costs. Therefore, we present a conceptual framework that gives enterprises a guideline for building a flexible data analytics network that is able to incorporate already existing edge device resources in the enterprise computer network. The proposed solution can automatically distribute data and code to the nodes in the network using customizable workflows. With a data management focused on content addressing, workflows can be replicated with no effort, ensuring the integrity of results and thus strengthen business decisions. We implemented our concept and were able to apply it successfully in a laboratory pilot.

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