Industry 4.0, an Ambitious Idea, Needs Data Democratization


The key to achieving Industry 4.0 success is data democratization, which provides broad accessibility to data and corresponding analytics tools.

The concept of Industry 4.0 — signifying the “Fourth Industrial Revolution,” comprised of interconnected factories, suppliers, and customers is enticing, and promises to deliver information instantaneously to facilitate product design, production, and delivery. The problem is there is still a lot of work that needs to be done to achieve this. A primary sticking point: the sharing of data across organizational boundaries.

The current state of Industry 4.0, and its obstacles, where the topic of a recent study by researchers from the Industry 4.0 Maturity Center and Aachen University, published at the MDPI site. The authors looked at 259 separate studies of Industry 4.0 adoption, concluding that many factories “show a lack of capabilities across all dimensions IT systems, resources, organizational structure, culture.” Companies need to step up their development of “technical backbone for a data pipeline as well as capability building and an organizational transformation.”

The key to achieving this, they state is data democratization or ensuring “the underlying broad accessibility of data and corresponding analytics tools.” Collaborative and decentralized decision making is key to this. This enables data-driven transparency, such a, in the context of a factory, “fast and precise diagnoses and corrections of deviations. Since a factory is a complex socio-technical system, multiple technical, organizational and cultural capabilities need to be established and aligned.”

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Those companies further along with Industry 4.0 initiatives have embraced data democratization in a more profound way, the researchers find. They make the following recommendations to advance further down the data democratization path:

Engage the business in technology-driven initiatives: “Create a ‘pull’ from the organization instead of first developing the technology with the risk of missing essential user requirements or overengineering,” they state.

Emphasize a data-first approach to decision-making: “Most companies still base many decisions on individual experience and intuition and not on data.

Bake data-sharing into the DNA of the organization: The democratization of data is often stymied by attempts to formalize knowledge management. “Set specific incentives to foster a mindset of knowledge sharing,” the researchers state. “For instance, existing practices to incentivize the participation in suggestion programs or in lessons-learned programs can be used as a reference.”

Promote and enable analytics self-service: Encourage “citizen analysts,” the researchers urge. “These are employees who are qualified in self-service analytics tools to cover their data needs own their own.” In addition, encourage the cross-fertilization of skills to develop richer innovation.


About Joe McKendrick

Joe McKendrick is RTInsights Industry Editor and industry analyst focusing on artificial intelligence, digital, cloud and Big Data topics. His work also appears in Forbes an Harvard Business Review. Over the last three years, he served as co-chair for the AI Summit in New York, as well as on the organizing committee for IEEE's International Conferences on Edge Computing. (full bio). Follow him on Twitter @joemckendrick.

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