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A 5-Point Checklist for Process-Industry Analytics 1.0.0

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Process manufacturing organizations run on data—from a manufacturing, operations, and business perspective. 

The data generation and collection strategies at the center of their manufacturing processes have evolved dramatically, especially in recent years. These organizations now collect and store huge volumes of data across their operations, both on and off premise, across multiple geographic locations, in an increasing number of separate data silos. 

These advances have coincided with the proliferation of connected sensors and increasingly inexpensive storage, leading to an Industrial Internet of Things (IIoT) projected to generate more than 4 trillion gigabytes of data per year by 2020, according to IDC Research.

New advanced data analytics have a huge positive impact on the growing volumes of data in many sectors, from retail to financial. So why aren’t all these new analytics widely leveraged in process manufacturing? With so much data and the promise of so many new technologies, why is it so difficult to apply these technologies to process manufacturing and gain the same benefits as other sectors? 

Why do these organizations still feel like they have too much data and too little insight? 

At Seeq, we believe this gap—between the data these organizations have and the insight they want—exists because existing data analytics solutions fail to completely grasp the unique challenges and opportunities presented by process manufacturing. 

When we talk about data analytics, we mean any software enabling process engineers or scientists to: 

  • create a cleansed, focused data set for analysis through assembling, aggregating, or wrangling data from various sources, including data historians, offline data, manufacturing systems, and relational databases
  • investigate operations data using “self-service” tools to rapidly analyze alarm, process, or asset data for ad hoc or regular reporting
  • publish or share insights and reports across the organization to enable data-driven action, or enable predictive analytics on incoming data

Many data analytics solutions claim to offer some or all of these things—with the goal of finally closing the gap between data and insight. But are they successful, and how do you evaluate if they are? 

In this paper, we propose five questions we believe every process manufacturing buyer should ask when evaluating an advanced analytics solution. 

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