Raw intelligence: how big data flows work

Raw intelligence: how big data flows work, and why they matter

Raw intelligence: how big data flows work, and why they matter

Through advances in digital manufacturing, raw materials are fast becoming intelligent assets. Thought of another way, material flows are becoming information flows. In this series we explore the implications for the circular economy. In part one we investigated the technological advances that are encoding intelligence into materials. Here we look at the trends in storing, communicating, and using materials data. Part three will turn to the impact on material supply chains and business ecosystems that result, and discuss the business models that stand to benefit from emerging trends.

InPart 1we explored how raw materials are becoming intelligent assets – how we are encoding information into a material’s composition and structural form. In this article we examine the tools that allow the use of these new types of data, asking the important questions of who has access to materials data, who produces it, and who consumes it. Answering these questions will help us use advances in materials science to accelerate the transition to a circular economy.

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When data is organised, it becomes information, but it is only when we understand how to use information that it becomes knowledge. Data may be neutral, but the ways it is organised into information has implications for who can access and use it. Some of the most well documented and intractable problems facing the circular economy transition involve a lack of access to useful information about materials. Many initiatives are focused at making information on materials more transparent, for example throughout product value chains. Those with the tools to turn data on materials into useful information and new knowledge may hold the keys to a circular economy.

Let’s look first at the trend in how data on materials is stored, communicated, and used in manufacturing. Data on materials is stored in large, searchable databases that usually contain information on the chemical composition of a material, and how it behaves under certain environmental conditions. Many companies license access to material databases. An example is Granta, one of the market leaders that offers a massive repository of data on materials for manufacturing, coupled with sophisticated analysis software, to mainly industrial users. Another large database is MatWeb, which offers free materials data targeted at engineers and industrial users. There are many other material property databases available, with some tailored more to specific industries or academic uses.

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Database companies work hard to make sure they have the latest materials in their system, and in doing so secure their position in the marketplace.For example, Granta and MatWeb are both integrating 3D printing materials into their databases: Granta’s recent partnership with Senvol (www.senvol.com) has resulted in a growing list of 3D printing (i.e. additive manufacturing) materials being integrated into its set of tools and services. However, their market dominance is being challenged by the very nature of the new wave of materials data. This data is far more complex and multilayered than traditional engineering data that has powered manufacturing for the last century. For 21stcentury manufacturing, engineers increasingly need to compute the properties of a material at nano and micro millimeter scales, and model how these properties interact with the object’s structural form to produce the characteristics of the final product.

The volume and complexity of such data is transforming material science into an information science. This is where the tools of ‘big data’ come in, with smart search algorithms and advanced analytics that help make sense of the messy heap of available data. If big data is the new black goldin today’s economy, data on materials is no exception.

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