Smart Industry: You note that we’re in the middle of a “data analytics revolution.” What do you mean by that?
Nick: As companies drive toward achieving the connected factory, the amount of machine data is overwhelming many organizations. This is resulting in a critical need to deliver data analytics that interpret machine data into a business context. The organizations that create data models that combine machine-level data with meaningful business context and dimensions will have a competitive advantage.
Smart Industry: What’s the most common misconception about using data analytics to optimize industrial processes? What’s the solution?
Nick: Many feel that simply connecting a BI tool to IoT data will provide the necessary information to make decisions at every level; machine data does not always correlate with business and operational metrics. The quickest way to leverage your connected data is to have a purpose-built manufacturing-analytics data model that can link the IoT data to your business dimensions. As an example—being able to look at material scrap by process, product family, machine type, plant, geography, line of business etc. This is how people make decisions and the data needs to correlate to that context.
Smart Industry: You note that about 85% of manufacturing data is not properly leveraged. Why is that? What’s the fix?
Nick: It's a common approach that creates this problem; most organizations that begin to connect their machines and factories tend to err on collecting as many data points as possible. This has a few negative impacts: it tends to make these projects long and expensive and they also tend to deliver too much information that does not get leveraged.
Smart Industry: How does strategic data analysis boost organizational visibility?
Nick: Having an enterprise-data model that standardizes all your factories enables organizations to do a comparative analysis to highlight problem areas and discover opportunities to drive improvement in machines, areas, plants, lines of business, and geographies that may be underperforming. Or conversely, identifying available capacity to grow revenues and meet customer demand.
Smart Industry: What was the most challenging plant implementation you worked on? What was the lesson learned there?
Nick: Probably the most challenging implementation was with an organization that had spent more than 18 months implementing a solution that had set the expectation to collect over 30 data points per machine to drive manufacturing and predictive analytics. Ultimately, the client reengaged with our organization to replace the prior solution and deliver their connected factory. The biggest challenge was resetting expectations around what data is required, initially, to drive an efficient implementation plan that delivers ROI in a reasonable timeframe.
Smart Industry: What most excites you about the possibilities/opportunities hidden within industrial data?
Nick: Probably the most exciting possibility I see after working with manufacturers for more than 19 years is that we will start moving to an industry that has typically been resistant to technology and change and moving them forward to a status that they are now seen as progressive thought-leaders.
