In early October, we kicked off the Smart Industry fall INSIGHT webinar series with the keynote “Tips to Quicken Your Process in Making Industrial Data Profitable.” Here with chat with that webinar’s presenter, Inductive Automation co-director of sales engineering Travis Cox, to discuss simple fixes to common data problems. Take a look…
Smart Industry: What's the most common problem you encounter with actually making industrial data profitable? What's the simple fix?
Travis: A common problem with making industrial data profitable is the lack of data context and standardization. Approaching digital transformation from the top down results in lots of mapping and transformation of data, inconsistencies with data, and difficulties scaling across the enterprise. It is extremely important to define the models and assets at the edge, leverage open standards, and supply a single source of truth of that data. You need to democratize the data—in other words, to make data available across the enterprise with as few restrictions as possible. And you need to facilitate cooperation between departments, because no one department or team can make digital transformation happen.
Smart Industry: Do manufacturers over-estimate the time / difficulty in digital transformation?
Travis: Most manufacturers tend to overestimate the time and difficulty in digital. The main reason is the belief that digital transformation is hard or that it requires a completely new set of tools. The reality is most manufacturers are already sitting on a gold mine of data. Manufacturers tend to think top down versus building the right foundation from the bottom up. Another reason is that most manufacturers fail in pinpointing specific use cases or goals that everyone in the organization can align to. Lastly, it can be difficult for manufacturers to pick the right technology partners that enable them to innovate without being locked into proprietary nightmares and non-transparent services.
Smart Industry: What is one, common valuable pool of data in most industrial settings that your clients overlook? How does this data translate into wins?
Travis: One common valuable pool of data is energy data. Overall, energy costs have increased for manufacturers. Most manufacturers have no means to correlate energy data with their machines, processes, operator-usage patterns, and more. That data is easily accessible and can help identify ways to reduce overall energy costs. Experts estimate that manufacturers can reduce energy costs by 8-10% by using analytical tools.
