江西快三和值走势图 www.jr2ya.cn There is no magic bullet for turning data from the plant floor into business advantage. But, to thrive in the global digital industry era, UK enterprises must harness the power of the data they produce.
The Data Analytics Journey
Data has been on a journey, how we use it in industry has developed over the years and the pace of change is accelerating.
The first stage of data being really useful to production on the plant floor is known as descriptive analytics. Essentially, in this phase, data tells us what happened to the plant – be that how much was produced, or what caused downtime.
The second stage for data is diagnostic analytics. It tells us why something happened. Great - we can try and stop that from happening again - or, if it was good, we can seek to emulate best practice.
The third stage is where many top UK enterprises are heading at the moment, and something we often talk about at Rockwell Automation, predictive analytics. We can start to see what will happen - when something will wear out, when something will break down. So, we can decide when to have planned downtime based on real-time understanding of the system, that sort of thing.
Recent analytics and Artificial Intelligence (AI) take us to an important new phase – prescriptive analytics - this is where the data tells us what we should do - and/or in some cases, it will act without manual intervention to make decisions for us, based on models and insights. With the right tools, it's becoming possible for managers to interrogate data through a dashboard that collates it from the whole plant, and helps contextualise it for us. So, as the predictive analytics informs the manager that "plant A will be behind plan soon", we can ask, "What action should be taken to avoid plant A falling behind plan?". Or at a device level, predictive data may say "a fault will happen soon" we can ask "what action should be taken to avoid the fault?"
The business value of data grows quickly along the journey from descriptive to prescriptive, but so does the complexity of delivering the value. This is a journey from hindsight, through insight, to foresight.
The use of prescriptive analytics is not common in industry yet, but it is not an idea for the future – it is possible now and it is something that UK industry should be working towards in order to take the significant opportunity presented by the Industrial Internet of Things (IIoT) era.
One challenge though, is the global shortage of data scientists who can turn the data into insight. And not just in industry, as this article in Computer Weekly explains, they are in demand across all businesses.
The lack of data scientists is part of a broader skills issue in industry of course, and it may not even be the most pressing skills issue in your business today, but as the IIoT helps industry to produce more and more actionable data, the need to crunch the numbers increases.
A data scientist has several invaluable skills for the IIoT, namely, programming (or coding), statistical analysis skills, and problem solving. While cleansing data is another vital skill for a data analyst, it stands to reason that the more time they spend performing this function, the less time they have to add value by getting insights from data and helping to implement productivity improvements. What’s more, with the shortage of data scientists out there, if your enterprise is bringing in these skills as a service, then it will cost a lot more if they must first wrangle your data into something usable.
It could be said that the convergence of IT and operational technology (OT) is causing this challenge as it brings about the need for a hybrid skillset. But convergence may also be a part of the solution. Modern automation software is coming to the market with data analytics functionality that can help collate and rationalise data so that your existing operational experts can offer insights previously the preserve of data scientists. Technology developments then, will likely bring more intelligence around the analytics feed, but automation technology and software developers and vendors are not the manufacturers and plant managers. The expertise in industry, and the value of the IIoT remains within the skills and experience of the operational technologists on the plant floor and in the control room.
In my view, one of the most important ways that data can be turned into business advantage is through data analytics. The future will be easy-to-use data analytics tools that can be used by operational technology experts and it will be this combination that will start to deliver the full promise of the fourth industrial revolution for the UK.
But now, I want to hear your thoughts, let us know if you have started to recognise the power of the data in your business in the comments.