InTech

MAY-JUN 2018

Issue link: http://intechdigitalxp.isa.org/i/989516

Contents of this Issue

Navigation

Page 11 of 53

COVER STORY 12 INTECH MAY/JUNE 2018 WWW.ISA.ORG time-series signals, is by definition sep- arated from other data sources, which store the related context. So, before any investigation can take place, an engineer has to deal with the variety issue—in particular the integration of continuous analog signals with the relational or discrete data sets stored in other databases. This integration, usually done by hand, is one of the biggest drivers of spreadsheet use within organizations. Even organizations with information models in enterprise manufacturing intelligence (EMI) solutions have to rely on spreadsheets for ad hoc ana - lytics, because if a data set is not inte- grated and modeled in the EMI, and it rarely is, then it is back to square one and interpolation, alignment, and time matching by hand. There are many terms for the align- ment and integration of unlike data types in the industry. Data blending, data harmonization, and data fusion are three examples—but for process manufacturing firms, the term typi- cally used is contextualization, which advantage of the data management, storage, and analytics capabilities now available to improve production and business outcomes. Context across data sources The three "Vs" of big data—velocity, va riety, and volume—are well known and have been part of the big data defi- nition for longer than the term big data. But of the three, one of them is far more of an issue in process manufacturing than the other two. The issue is not volume, because process historians and other sources have plenty of data stored and avail- able for analysis. Similarly, velocity has a number of solutions with high capac- ity networks and faster ingest rates for historians. Variety, however, presents the biggest challenge to advanced ana lytics, and new big data solutions are working to address it. The challenge with variety is that most existing plant sensors support only a limited data set of time, value, and perhaps state. Therefore, the most typical data type in manufacturing, improvement, to the tune of $50 billion in the upstream oil and gas industry alone (figure 2). Help is on the way as big data offer- ings and expectations have changed to better fit process manufacturing requirements. The market has moved from a Model T, "any color so long as it is black" product to a variety of sizes and shapes to meet customers' needs. The interface or user experience with many big data applications, for example, is no longer an erector-set experience. In fact, a new set of themes around big data is emerging just as more com- panies are open to and interested in new advanced analytics experiences. If plant evolution is measured in decades, and big data awareness and innovation is approaching a decade of investment, the time should be ripe for implement- ing new technologies. This article will discuss four of the expectations associated with a modern big data experience in process manu- facturing firms. Fulfilling these expec- tations will result in a more polished and higher-level experience by taking Figure 2. Better use of big data presents a $50 billion opportunity in upstream oil and gas facilities, with hundreds of billions of dollars in opportunity across other process industries. Source: McKinsey

Articles in this issue

Links on this page

Archives of this issue

view archives of InTech - MAY-JUN 2018