How to overcome obstacles to industrial data analytics
Common barriers and concerns of modernizing data analysis in industrial applications.
- Identify and understand obstacles of industrial data analysis.
- See whose cooperation is needed to apply industrial data analytics more effectively.
- Learn strategies to more easily implement industrial data analytics.
Industrial Data Insights
- Taking the next step in industrial data analytics requires identifying and moving past various obstacles. Whose help are you going to need to get to apply industrial data analytics more effectively?
- Information technology (IT) and operational technology (OT) personnel can collaborate to implement industrial data analytics using combined knowledge about sources of data accessibility. Everyone can benefit and has an interest in making this succeed.
Identifying and overcoming common barriers and concerns of modernizing data analysis can accelerate how information is used to optimize operational goals. Laurie Cavanaugh, director of business development, E Tech Group, and Matt Ruth, president of Avanceon, explain data analytic bottlenecks and how to overcome them in this partial transcript from the April 21, 2022, RCEP PDH webcast (archived for 1 year), “Just enough industrial data analysis?“ This has been edited for clarity.
Related data analytics articles
Relate articles in this data analytics series are:
– Webcast preview: What is just enough industrial data analysis?
– Data analysis disruptors in the industrial space
– How to get started with industrial data analytics
– Industrial data analysis case studies, effectiveness.
Cloud willingness helps with adoption of industrial data analysis
The first barrier to effective data analytics is cloud willingness. It’s very productive to have information in a cloud setting. Almost all of us have personal information on smartphones, and we send it to the cloud all the time, but there’s been some lack of willingness to engage in a cloud strategy for manufacturing.
Cloud willingness is very important. Additionally, the ability to connect operational technology (OT) and business data through the cloud is another barrier that is a challenge. It involves getting engaged with the right IT resources and having them allocate resources to OT analytics.
Addressing security concerns and accessibility issues and being able to feel confident with information technology (IT) for the right security decisions overcoming the barrier of information access.
The final barrier is data warehousing. IT and OT cooperation is needed, and executive sponsorship is really vital to make data warehousing a reality.
Non-technical barriers to effective industrial data analytics
Some non-technical barriers include things related to “What’s in it for me” or WIIFM. Some people say, “Well, why me? Why now? I’m one year from retirement.” I don’t want to deal with this stuff.” That’s often when their tribal knowledge, their years of experience, are needed most.
Once everybody agrees, now how do we master selling those inside the organization on industrial data analytics? Are there key stakeholders for industrial data analytics at all levels? Many CEOs use smartphones to do secure banking transactions. Plant-floor experts can do the same thing. Everybody has a vested knowledge or awareness, and we just have to connect the dots to show them why they should be interested in getting information industrial data analytics can provide. This is the reason why the analytics is really going to increase agility and competitiveness.
The other components are trust. Are we trusting the reasons for change? Do IT and OT trust each other? That relationship has been damaged in the last 20 years. Now, there’s a huge need for us to all work together.
Next is the value proposition. Has this been funded or has the value been articulated? Has it been quantified enough? It’s difficult to always put a number to something. Is there a compelling reason to persevere and move ahead, knowing costs and benefits?
Sometimes those in the C-suite must think, “If I can get 10 billion results on a search, why can’t I get that on the shop floor? What’s the planning and execution to get there. What are the steps and stages? What are the strategies? What’s informing the plan? Is it based on empirical data or gut instinct? What’s that desired instinct? Can all involved agree and how will they be accountable and transparent? What does success look at like and how do we get on the same page?”
IT and OT teams need to work together. There is a lack of software tools, lack of understanding of in-house knowledge. These are somethings that might be preventing advancement to the next level in data analytics. One of the highest concerns about industrial data analytics remains lack of knowledge and understanding of in-house knowledge.
– Edited by Tyler Wall, assistant editor, CFE Media and Technology, email@example.com, from a Control Engineering RCEP/PDH development hour webcast.
Overcoming industrial data analytics obstacles
Have you identified hurdles and solutions related to industrial data analysis?