In part one of this blog post, we described an important group of technical challenges process industry organizations face as they develop the ‘data foundations’ for their digital transformation and Industry 4.0 programs. In Part 1, we described the connectivity, network, security and data transport challenges these companies deal with, managing diverse OT data sets on the scale that is now possible with modern IT. In this post, we discuss the strategies and approaches organizations have been taking in addressing these challenges and make recommendations.

Solutions to Address OT Data Communication Challenges

Many across the process industries have began their data foundation programs experimenting with single edge to cloud connectivity solutions, which interface individual data sources to an OT or enterprise IT data lake. Each data source is handled independently, requiring the integration of a variety of connectivity, network tunneling, security, compression, load balancing and Store and Forward (SaF) solutions, as organizations have recognized the various challenges described. Often, starting with enterprise historian, companies have developed proprietary solutions or found commercial point connectivity solutions that allow them to reliably transport historical data as well as stream live data directly to on premise applications or even cloud base platforms. For many, this has enabled their data science teams to begin the thoughtful exploration of their operational data and act on a variety of use cases in modern cloud environments.

The digital transformation of organizations across the process industries is fully underway. At the very foundation of these programs is the data which lives inside these organizations in vast quantities. As never before, data of all types is being consolidated, organized, contextualized and analyzed in a myriad of use cases which drive operational and business improvements.

Leveraging your OT Data to Drive Business Improvement

Operations technology (OT) data of all types spanning the operating plants, refineries, mills, and factories is in demand at scales never before conceived by these organizations. Distributed control systems, programmable logic controllers (PLCs), safety systems, manufacturing execution systems (MES), supervisory control and data acquisition systems (SCADA), laboratory systems, maintenance systems, and process data historians are examples of OT systems which generate valuable data organizations are now seeking to make better use of.

As a result, technologies such as massively scalable cloud computing platforms, open source technologies, and modern machine learning algorithms, can be leveraged by these organizations in the same way as retail, financial, social media and digital entertainment companies have done to completely transform their business models.

Unanticipated by many in these organizations, however, have been the connectivity, data transport, organization and contextualization challenges in their digital initiatives. As it turns out, the ‘digital plumbing’ which enable the digital transformation of these businesses is a barrier for most. Truly, it is not as simple as some describe. It is more difficult than simply transporting all of the OT data to the enterprise cloud and turning it into gold with advanced analytics and machine learning.

In this two-part post, we first describe in some detail the challenges organizations are having accessing OT data. In the second post, we discuss known strategies and solutions to the challenges used by businesses today.