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  • data-discovery-plm

    For the last year, I’ve been sharing my thoughts about something I call new PLM paradigm. PLM started few decades ago from controlling data of engineering department. It grew up, especially in a large companies as a discipline to control business process and to transform organizational performance. The last turned PLM is over-complicated and usually oversold paradigm that requires first to transform organization before even thinking about PLM.

    To transform organization is inevitable thing and companies have to think about business models, processes and tools. But from PLM standpoint, I’d not recommend to mix these two happiness together. Therefore, one of my recent recommendation was to focus on data silos and not on organizational silos as a target of PLM implementations. Check more here – How PLM can separate organizational and data silos. In my view, focus on data and intelligence in the company and value chain will bring future paradigm shift.

    With latest trend on digital transformation, product innovation platform, we missed big data, which was very much interesting topic in PLM. But PLM vendors missed big data, in my view. It was too much challenge for PLM vendors. However, the opportunity might be not lost completely.

    My attention was caught by SiloconAnge article – Software, not hardware, will catapult big data into a $103B business by 2027.

    Deriving value from data and then putting it to good use in the business world has become the holy grail of success and a key predictor of whether a company will rise or fall. The formula for success thus becomes based on how effectively the enterprise world can build its own system for harnessing true data value. To draw from another famous Drucker quote, “The best way to predict the future is to create it.”

    The following picture looks very interesting:

    big data-service-ops

    Here is an interesting passage:

    “The services business is going to undergo enormous change over the next five years as services companies better understand how to deliver big, data-rich applications,” Burris said. The second key number is 2017, the year that the software market surpassed hardware in big data. Hardware isn’t going away, but the shift represents an important move towards finding new ways of software-driven data movement and analysis. “How do I make data available to other machines, actors, sources of process within the business?” Burris mused. “Architect your infrastructure and your business to make sure the data is near the action in time for the action to be absolute genius for your customer.”

    Data about products, business activities, services. Manufacturing companies are turning into services because they realize that this is a next source of revenues. The data about product, intelligence behind who is using what and how to optimize it, ability to communicate between people and teams. All together will have to deliver intelligence in product related manufacturing business. To make this data available for business looks like a problem PLM companies can be finding future source of revenues. Ultimately data is hold by CAD, PDM, PLM, ERP and variety of other legacy applications.

    What is my conclusion? Data will be a key element of future paradigm shift. Ability to collect, share and communicate around data will become an essential prerequisite of future PLM intelligent applications. How to get the data? How to share right data in a company? How to get information about what product is actually customer using. All these questions are hard to answer with today PLM infrastructure and tools focusing on control of data and business processes. So, leave the process of crushing organization silos to companies and business teams. Focus on how to break data silos and deliver right data to users. Just my thoughts…

    Best, Oleg

    Want to learn more about PLM? Check out my new PLM Book website.

    Disclaimer: I’m co-founder and CEO of OpenBOM developing cloud based bill of materials and inventory management tool for manufacturing companies, hardware startups and supply chain. My opinion can be unintentionally biased.