The scale and complexity of the data is growing tremendously these days. If you go back 20 years, the challenge for PDM / PLM companies was how to manage revisions CAD files. Now we have much more data coming into engineering department. Data about simulations and analysis, information about supply chain, online catalog parts and lot of other things. Product requirements are transformed from simple word file into complex data with information about customers and their needs. Companies are starting to capture information about how customers are using products. Sensors and other monitoring systems are everywhere. The ability to monitor products in real life creates additional opportunities – how to fix problems and optimize design and manufacturing.
Here is the problem… Despite strong trend towards cheaper computing resources, when it comes to the need to apply brute computing force, it still doesn’t come for free. Services like Amazon S3 are relatively cheap. However, if we you want to crunch and make analysis and/or processing of large sets of data, you will need to pay. Another aspect is related to performance. People are expecting software to work at a speed of user thinking process. Imagine, you want to produce design alternatives for your future product. In many situations, to wait few hours won’t be acceptable. It will be destructing users and they won’t use such system after all.
Manufacturing leadership article Google’s Big Data IoT Play For Manufacturing speaks exactly about that. What if the power of web giants like Google can be used to process engineering and manufacturing data. I found explanation provided by Tom Howe, Google’s senior enterprise consultant for manufacturing quite interesting. Here is the passage explaining Google’s approach.
Google’s approach, said Howe, is to focus on three key enabling platforms for the future: 1/ Cloud networks that are global, scalable and pervasive; 2/ Analytics and collection tools that allow companies to get answers to big data questions in 10 minutes, not 10 days; 3/ And a team of experts that understands what questions to ask and how to extract meaningful results from a deluge of data. At Google, he explained, there are analytics teams assigned to every functional area of the company. “There’s no such thing as a gut decision at Google,” said Howe.
It sounds to me like viable approach. However, it made me think about what will make Google and similar computing power holders to sell it to enterprise companies. Google ‘s biggest value is not to selling computing resources. Google business is selling ads… based on data. My hunch there are two potential reasons for Google to support manufacturing data inititatives – potential to develop Google platform for manufacturing apps and value of data. The first one is straightforward – Google wants more companies in their eco-system. I found the second one more interesting. What if manufacturing companies and Google will find a way to get an insight from engineering data useful for their business? Or even more – improving their core business.
What is my conclusion? I’m sure in the future data will become the next oil. The value of getting access to the data can be huge. The challenge to get that access is significant. Companies won’t allow Google as well as PLM companies simply use the data. Companies are very concerned about IP protection and security. To balance between accessing data, providing value proposition and gleaning insight and additional information from data can be an interesting play. For all parties involved… Just my thoughts..