3 data analysis tips: Adapt data, leverage employee skills, innovate
Industry 4.0 requires an appropriate use of data structures, people skills and analytic tools.
- Adapt data for industrial data analytics.
- Leverage employee data skills for analytics.
- For best analytics, integrate data access with people, innovation.
Industry 4.0 initiatives include three critical aspects for success when moving from data to data-based decision making: data, people and analytics. If those sound familiar, they should, if you want to succeed in an organization then the same things will always matter most.
1. Adapt data for analytics
First and foremost is the data and ongoing access to it, in legacy systems and industrial applications, because the best laid strategies will be tweaked and improved in the course of Industry 4.0 initiatives. As the saying goes: “No battle plan survives contact with the enemy.”
The ability to start an advanced analytics project with the data where it is, in silos and different systems and of various types, is critical. Plans beginning with assumptions about what data will be required, prerequisites for data movement or aggregation, summarization are not compatible with the inevitable required changes and tweaks. Agility and adapting to change are the core of Industry 4.0, so starting with fixed expectations and expensive data transformation efforts before the benefits and proof of value from achieved insights is the wrong way around.
2. Leverage employee data skills for analytics
Second, a consistent finding in successful Industry 4.0 projects is the recognition and leveraging of employees’ skills because these people best know the plants, processes, and procedures. What this means in practical terms is bringing innovation and abilities to current employees, which results in an increase in the organization’s overall capacity for driving improved outcomes because insights and abilities are distributed instead of centralized far from the point of action.
This may sound counter to all the attention paid to data scientists and machine learning. What the hype about data scientists misses is the fact they don’t know the plants, the assets or the first principle model of how plants run. Because data scientists don’t have plant operations knowledge, their ability to find insights of value in a changing environment of raw materials, prices and schedules is limited. The employees who know the plant best, on the other hand, do know what they need for improved outcomes; they need improved advanced analytics software for easier and faster insights.
3. Integrate data access with people, innovation for best analytics results
Third, with access to the data and the right people, it’s time to bridge them together and deliver innovation to those with the greatest abilities and needs. Therefore, the imperative is bringing data science and innovation in analytics to the front lines of the workforce. “Isn’t that the point of software,” as Bill Gates, founder of Microsoft, once said.
This means the advanced analytics applications must wrap up and make accessible the innovations behind the scenes of the software, like the Google search bar wraps the MapReduce algorithm, or the Uber app integrates mapping, AI, and billing systems.
The result is an interactive, visual, and collaborative software experience (Figure). This enables the process engineer, scientist or analyst to accomplish in minutes what would have taken hours or days with a previous generation of software. Ease of use may not seem like a major aspect for manufacturing in 2020, but to successfully land analytics software, it’s a critical requirement.
Michael Risse, CMO & VP, Seeq Corp. Edited by Mark T. Hoske, content manager, Control Engineering, CFE Media, firstname.lastname@example.org.
KEYWORDS: Automation implementation advice
Make best use of existing data for industrial data analytics.