Roadblocks in IoT Transformation : A Practitioner`s View

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      Curator 1 for Blogs
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        [ Arun Prasad TCS Japan  LinkedIn ]

        In the last week of Aug 2017, there were a couple of press releases and news articles that quietly was doing the rounds. There was a rumor that said that GE is contemplating selling “Predix” stakes and reportedly trim downed the “Digital Revenue” targets and wanted to focus more on consolidation.

        While in the hindsight, it is very easy to comment on “Why things didn’t work”, but it also provides us an opportunity to look into the problem and analyze what went wrong. Without getting into the postmortem analysis of Predix, Let us try to see a bigger picture and understand why crossing the Digital Transformation is indeed a Chasm. There is an insightful article from Forbes on “Why Manufacturing Companies fail in Digital Transformation“. Surprisingly Forbes states that 84% of the Digital Transformations in Manufacturing Firms are destined to fail and Mckinsey says 65% of Digital Transformation does not take off after the initial pilot.

        Some Key problems were

        • Technical Problems and Delays with Predix
        • Fragmented End Customer and Industry segments for Predix
        • Building Data Centers of their own – Go-it-alone Cloud Strategy
        • Lack of clear Business Case for a Measurable Return on Investment
        • Trade off between Poor Execution of traditional businesses over-focus on Future Innovations

        Based on my knowledge and experience that I gained from few key engagements and experiences Implementing Industry 4.0 and discussions with key customers, I have listed some of the reasons why Industrial IoT Digital Transformations could fail and what are some key considerations that need to be taken care of.

        1. Lack of Internal Alignment

        2. Ignorance Iceberg

         

         

         

        3. People need Solution to their problem, not IoT/Digital/Cloud Technology

        4. Platforms Thinking

        5. Build Platform or Partner for Platform

        6. Monetization Model

        7. Data Challenges

         

         

         

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