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INDUSTRY 4.0/IIOT
UNLOCKING VALUE FROM DATA
Figure 1
IIOT and Industry 4.0 initiatives are rapidly adding to the amount of available data, driving the need for advanced analytics applications to create insights, says Katie Pintar, analytics engineer at Seeq
n the age of the industrial internet of things (IIoT) and Industry 4.0, the sheer amount and complexity of data has greatly increased. Add the emergence of artificial intelligence (AI) and machine learning (ML), and the process industries have the potential to uncover more meaningful insights than ever before.
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However, for process manufacturers, the journey from raw data to meaningful insight is still disjointed. To meet these companies where they are today, leading issues such as data access and connectivity, the lack of time-series specific analytical tools, and collaboration limitations must be addressed. For most process manufacturers, numerous data sources exist, from equipment and process data to quality and inventory data, but this information is stored in a variety of different databases. Using standard spreadsheet-based tools to collect, cleanse, and align this type of process data from a variety of sources is time intensive for subject matter experts (SMEs), typically process experts and engineers. These inefficiencies are
compounded by a lack of live data connectivity, which leaves analyses perpetually out-of-date. These hurdles make it difficult for SMEs to gather the data, let alone prepare it for meaningful analysis. Traditional solutions also make sharing data and analyses across teams difficult
or impossible, limiting the power of collaboration and knowledge transfer. Traditional spreadsheet-based tools are not optimised for time-series data analysis and are decoupled from real-time data visualisations, which makes quick, iterative data analysis prohibitive. While SMEs bring a wealth of process knowledge and insight, they have long been underserved when it comes to effective and efficient data analytics tools, but better solutions are now available.
Advanced analytics applications make handling disparate data sources much easier by connecting them to a single cloud-based or on-premises application. These types of applications, such as Seeq, can be used to cleanse and contextualise data, and to perform time stamp alignment in the background, enabling SMEs to quickly derive
meaningful insights across all available data. Equipped with live data
connections, these applications allow users to apply analyses to near real-time data. With these data access barriers removed, SMEs are empowered to leverage the application’s purpose-built, time-series, analytical tools — provided in a no-code or low-code point-and-click format — to discover transformational data insights. These tools are coupled with trending and data visualisation, empowering SMEs to visualise the impact of their data analysis in real-time, and allowing for quick, iterative analyses.
In addition, advanced analytics applications enable more streamlined
Figure 2: A treemap created in Seeq displays truck health across the fleet to determine when predictive maintenance should be scheduled. All figures courtesy of Seeq
16 MARCH 2022 | PROCESS & CONTROL
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