Product Lifecycle & Big Data

Big Data as defined by Meta Group in its initial paper on this topic defines it with Three Dimensions (3 V’s).

1. Data Volume : Large Amount of Data generated through Transaction or Data generated by Machines

2. Data Velocity : Speed and Pace of data used to support interactions and support interactions

3. Data Variety : Data format which are inconsistent in nature and incompatible to each other, non aligned data structure & so on.

Let’s try mapping each of the Big Data dimension to Product Development Lifecycle

1. Data Volume : In case of Product development, most of the data generated is in context of Product definition. So the volume of traditional product data is generally low, even though the size of data may be large because of Design & Analysis data.

2. Data Velocity : Traditionally the velocity of product data is low. If we look at Product development lifecycle, many of the products lifecycle runs into months.

3. Data Variety : Data generated in product development is in context of Product and using standard tools. So the data is generally coherent.

So if we try to apply the Big Data concepts to a traditional Product Lifecycle data, then it may not fit. But let’s look beyond the traditional data, the data generated these days.  Lets look at data which is traditionally not managed in PLM but can be useful for the overall product development lifecycle. These days there is lot more data generated from different sources like, the Sensors on the product, electronic data points generated during Testing process, data generated from Warranty. And even eMail related to that product, which are very unstructured in nature and finally the lot of data generated in internal or public Social Media.

Bid Data & PLM

Bid Data & PLM

When we look at all this data, it fulfills all the three dimensions of Big Data.  This data is very useful for product development, as it can give feedback at different point of the Product Lifecycle. Ideally all this data should be processed analysed continuously and then it can become useful for the product development and decision making.


Conclusion : Big Data concept can’t be applied to traditional data. External data which is generated in context of the Product, can be used beneficially for a better result. This data if need to be used structurally, then we can apply Big Data concepts. These are my views..

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About the Author

Rahul Deshpande

Rahul is Principal Consultant at Brainwave Consulting Group – a cutting edge domain consulting group for New Product Introduction and Product Lifecycle Management.   Rahul brings over 16 years of experience in developing Technology and Business Solution for the Manufacturing Industry. He has worked with several clients in the manufacturing space in multiple roles spanning consulting, account management , Solution Architect and Program Management roles across the New Product Value Chain from Requirement / Enquiry management to launch of Product. He brings in cross functional experience covering Pre-Sales& Marketing, Business Planning, &IT Solution deployment. Rahul has worked along with the PLM ISV & Customers to define innovative solution and developed products for BOM Cost analysis and Sourcing Analytics on ENOVIA platform. He has penned different papers on usage of PLM for different processes & industries while addressing their business challenges. He brings a unique 360o view of the IT industry, and has significant contributions in the areas of IT Strategy, Program Management & Product Management.   Apart from holding a Bachelor of Technology in Electronics Rahul holds a Management degree from SIBM(Symbiosis Institute of Business Management). Rahul is associated with SIBM as visiting faculty for Innovation Management & International Business

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