As industrial operations move to the state, residents find that their drinking water has been promised to companies. In 2019, Corpus Christi, Texas's eighth-largest city, moved forward with plans to build a desalination plant. The facility, which was expected to be completed by 2023, at a cost of a hundred and forty million dollars, would convert seawater into fresh water to be used by the area's many refineries and chemical plants. The former mayor called it "a pretty significant day in the life of our city." In anticipation of the plant's opening, the city committed to provide tens of millions of gallons of water per day to new industrial operations, including a plastics plant co-owned by ExxonMobil and the Saudi Basic Industries Corporation, a lithium refinery for Tesla batteries, and a "specialty chemicals" plant operated by Chemours.

According to a McKinsey Global Survey, approximately 30% of executives reported active pilot projects, while 71% were expecting a significant increase in AI investment. However, the survey found that progress remained slow, most companies didn't have a clear strategy or infrastructure for sourcing data, and organizations were lacking the foundational building blocks to create value from AI at scale. Deploying AI in industrial operations is difficult for a variety of reasons – complex data management, challenging integration, enterprise security requirements, real-time analytics and capability to handle thousands of models in the production environment. However, a fundamental problem is finding skilled people to implement AI. To circumvent this issue, companies are relying on citizen data scientists – subject matter experts with domain expertise in operations – and providing them with advanced analytical tools.

According to a McKinsey Global Survey, approximately 30% of executives reported active pilot projects, while 71% were expecting a significant increase in AI investment. However, the survey found that progress remained slow, most companies didn't have a clear strategy or infrastructure for sourcing data, and organizations were lacking the foundational building blocks to create value from AI at scale. Deploying AI in industrial operations is difficult for a variety of reasons – complex data management, challenging integration, enterprise security requirements, real-time analytics and capability to handle thousands of models in the production environment. However, a fundamental problem is finding skilled people to implement AI. To circumvent this issue, companies are relying on citizen data scientists – subject matter experts with domain expertise in operations – and providing them with advanced analytical tools.

While the term "machine learning" generally relates to understanding structures or patterns in data, it can also refer to a very diverse set of activities and techniques. Most of us have experienced machine learning in our everyday lives with natural language processing (Alexa, Siri), image recognition (Facebook, Pinterest), purchase recommendations (Amazon) and search optimization (Google). These approaches generally use many different types of algorithms (e.g., neural networks, decision trees, clustering, support vector machines, etc.) Industrial operations, on the other hand, need more specialized approaches that can provide actionable insights to reduce downtime as well as improve throughput, operator safety, and product quality. Whether you call it Industry 4.0 or Industrial IoT or Digital Operations, the increased access to operational data, combined with the spread of computing, connectivity, and storage, has created the perfect environment for transforming industrial operations. The real opportunity is in unlocking the value of this data.

The next industrial revolution is here. Whether you call it Industry 4.0 or Industrial IoT or Digital Transformation, the increased access to machine and operational data, proliferation of two-way communication, speed of data flow, combined with the lower cost of computing, connectivity and storage has created the perfect environment to transform industrial operations. The time series data generated by these operations, if harnessed, can provide actionable insights to reduce downtime as well as improve throughput, operator safety and product quality. McKinsey & Company predicts that the next 20 percent productivity rise in operations will come from digital analytics, and machine learning-enabled pattern recognition is playing a significant role in enhancing production operations. Time series data generated in discrete and process manufacturing operations is very rich in information that can provide insights on the current and future health of the production equipment and lines.