SCADA & DATA ACQUISITION FEATURE STOP DROWNING IN DATA – ANALYSIS IS KEY


The Industrial Internet of Things (IIOT) is pushing SCADA to new levels of importance with the aid of advanced analytics. Micheal Risse, CMO/VP at Seeq Corporation advocates the advantages of data driven insights in achieving a connected, integrated factory


I


n the world of digital technology for process control, supervisory control and


data acquisition (SCADA) systems are hardly glamorous. But these systems— which have been monitoring, gathering and processing data in real time for decades—are seeing an opportunity to play an expanded role in the age of Industrial Internet of Things (IIoT) and IT/OT convergence. That’s good news, because process manufacturers need to extract maximum value from the vast amounts of data stored in SCADA systems. The merging of IT and OT is driving


SCADA growth in the water/wastewater industry, for example, according to ARC Advisory Group1


, as companies increasingly


tie SCADA to new data collection and analysis systems to better inform decisions. SCADA will become even more important as a source of data for insights as IIoT applications proliferate. It will be used to present monitoring and control data, while sensors connected to IIoT sensors will integrate new data sets into business intelligence systems to improve visibility and decision making. As a complement to IIoT, information


from SCADA will be more important than ever for driving better business decisions, optimising operations and enabling greater agility, but only if used in conjunction with today’s advanced analytics to improve production outcomes. Manufacturers have been collecting,


storing and analysing process data for years, but with the rise of IIoT, every stage of the data lifecycle has got cheaper over the last two decades by an order of magnitude, if not two. These stages include data generation at the sensor, connectivity and data storage. Before these advancements, only the


most critical assets were worth monitoring via SCADA. But now, less critical but still important disconnected or standalone assets are being monitored, either through existing plant networks or complementary IIoT platforms. The impetus behind this increased asset


connectivity and much more data is economic, spurred by industry trends such as big data, IoT, wireless networking and cloud data services. Computing is so cheap customers can justify generating, collecting and storing ever more data, driving the convergence of the OT/IT worlds. This data is valuable, but many end users find themselves data rich and information poor (Figure 1), and they are therefore searching for better solutions. While so much has changed on the data


side of the equation, the spreadsheet has remained as the most commonly used analytics tool for SCADA users. Just about every SCADA system has a historian that connects to spreadsheets for data cleansing, contextualisation, calculations and modelling. But with growing data volumes, there is


increasing interest and demand for improved analytics offerings – descriptive,


Figure 1: ‘DRIP’ Many end users find themselves data rich but information poor, spurring a quest for better tools to create insights (Courtesy of Seeq)


predictive, diagnostic, interactive and prescriptive – which go well beyond the scope of spreadsheets. These improvements are being delivered via what many now refer to as “advanced analytics.” Specifically, advanced analytics involves


the inclusion of cognitive computing technologies into visualisation and calculation applications. The introduction of machine learning and other analytics techniques accelerate an engineer's efforts when seeking correlations, clustering or any other needle within the haystack of process data. With these features built on multidimensional models and enabled by assembling data from different sources, engineers gain an order-of-magnitude improvement in analytics capabilities, akin to moving from pen and paper to the spreadsheet. For example, the ability to "search like


Google" across all the tags in a historian or other big data storage system is now available in some advanced analytics software, with other capabilities delivered in a similar manner. These technical advances provide the two critical components required for an advanced analytics approach. First, it should be a self-service offering


Michael Risse is CMO and VP at Seeq Corporation


for the engineers who have the required experience, expertise and history with the plant and processes (Figure 2). This enables engineers to work at an application level with productivity, empowerment, interaction and ease-of- use benefits. Second, the advanced analytics solution


should include a connection between the analysis that is created and the underlying data set, so users can simply click through and get to the underlying data. Advanced analytics offerings should be used to produce not just pictures of data in visualisations, but also to provide access to the analytics and sources that generated the outputs. These new capabilities can be used to enable the distribution of benefits throughout a plant and a company. IIoT envisions a sensored, connected,


Figure 2: Seeq R21 screen shot. Seeq is designed for self-service use by process engineers and experts, with no requirement for assistance from data scientists and other IT personnel





integrated factory providing increased and consistent visibility, accuracy and data- driven insights on production results thanks to advanced analytics. This is an exciting new world, and SCADA is finding its place within it. References: 1


SCADA Systems for Water &


Wastewater Industry, ARC Market Study: https://www.arcweb.com/market- studies/scada-systems-water-wastewater- industry


Seeq Corporation www.seeq.com


PROCESS & CONTROL | MAY 2019 17


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