As the world increases its need for connectivity, oil and gas producers require people who understand data. According to Gavin Rennick, president of software integrated solutions at Schlumberger, speaking at Automation Perspectives, the best results were obtained when a successful connection was established between people who understood the technology and those who understood the domain. Automation Perspectives is Rockwell Automation’s media event that leads up to the Automation Fair. He added that it sounds simple, but the amount of work that can be done in such a short period of time is simply amazing.
At another event focusing on modernizing water systems, a professor at the University of Michigan called on many students perched at the back of the room. Even though their focus lay on environmental engineering and studying water quality, the students were pursuing a dual degree in computer science – which will help them use the tools available and make sense of the data collected.
While there is a need for data scientists in all kinds of industries, there is a continuing trend in the number of rollouts from automation suppliers trying to make data useful and more accessible to domain experts. FactoryTalk Analytics helps industrial analytics overcome the complexity of data and makes it easier to combine structured and unstructured data from any virtual source and derive meaning from the data with natural language searches that make sense to domain experts.
With the increase in connectivity and good computing power, there is no longer a shortage of data. But to make that data useful for running and maintenance of ongoing operations isn’t easy. Project Scio reduces the hurdles and gains access to actionable information to fuse the data and deliver intelligent analytics into intuitive storyboards. Users can perform self-serve drill-downs and reduce time to value.
What is scalable analytics? A dimension of scalability is the type of analytic. Descriptive analytics tell you that the motor has stopped, but diagnostic analytics will also provide you with the reason. By using resources available from IIoT, there is a need for predictive and prescriptive analytics – what is going to fail and what can be done to avoid it.
Data scientists trained to make better sense of data spend about 60% of their time cleaning and filtering useful segments of it. The ease of use for such scientists will be a fundamental concept required by the customers.
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Andrew Ellis, the global commercial engineering manager for information solutions at Rockwell, used the example of a fictitious customer to show the ability of options for visualization and different charts that could be downloaded for presentations. He showed how efficiently all the data was drilled down to get information about one of their customer’s plants. He took the position of the plant’s operations manager and wanted to reduce energy. Using the service, he saw the kind of product that was using more energy through an intuitive storyboard.
He drilled further down to a line operator level and took a closer look at how each of them performed about energy consumption. Different operators run the line differently, and the most efficient were the ones he was interested in and how they would do their jobs. He could tabulate the data by time rather than the product to figure out how the energy was consumed from changing shifts by operators or the machine itself. A domain expert would be puzzled to develop a solution and reduce energy consumption and not know how to summarize the data. By typing a search query, he created data sets at the click of a button.
The key attributes included here are device auto-discovery, the central location of all the data that can be continually refreshed, flexible machine learning, closed-loop analytics, open platforms, and an application marketplace that can be introduced for in-house and third-party application development.