In a series of explorations pertaining to the use of the PI System™ and various types of "NoSQL" type solutions, great emphasis was placed upon the implementation and scope of the target problem space. Specific attention was placed on producing better quality data so that validated, trustworthy data was produced from a single, reliable infrastructure. This helped maximize the reusability of the effort involved in delivering trustworthy, instantly usable, operational data in context. Because of the similarities of certain elements of the PI System to certain NoSQL technology types, the qualities that differentiate the results of an implementation will substantially rely upon dividing the tasks of maintaining a rich, durable "system of record" and an agile analysis platform. The long term total cost of ownership involved in solving the initial problem, maintaining the solution, and solving new use cases as they arise in an "infrastructure-oriented" (rather than point-solution) manner will guide architecture choices as these implementations cross operational and business boundaries.
Year: 2014

Business analytics, machine learning and Internet of Things (IoT) technologies will transform virtually every aspect of the economy in the coming decades. One of the biggest stumbling blocks, however, remains capturing, organizing and delivering the vast amounts of data from sensors, industrial equipment, power supplies and other operational technologies (OT) in a timely fashion to achieve genuine, actionable insights for information technology (IT). Too often, this data—the raw material of business analytics—remains landlocked in disparate systems and getting it out requires inordinate amounts of time and money. OSIsoft brings OT and IT together through its PI System and PI Integrator technology. The PI System captures, cleanses, augments, and shapes OT data from thousands of disparate systems, often using hundreds of connectors, and then transmits analytics-ready data in a coherent fashion to IT systems. By combining the PI System with Microsoft technologies such as Microsoft SQL Server, R Services, Azure IoT and Cortana Intelligence (Azure ML), companies can dramatically accelerate their digital transformation and IoT strategies.
Year: 2016

Deploying software across the enterprise is easy. Making sure it works and delivers value... that’s another story. Mera Group has implemented PI Systems for industry leaders in oil and gas, pipelines and mining organizations. The deployments vary from small site solutions with hundreds of tags to complex enterprise PI Systems with millions of tags. Over 17 years, Mera has developed a proven process to transition clients to an enterprise PI System. In this white paper, Heather Quale, President of Mera Group, shares their seven-step process for successful enterprise deployments.
Year: 2018

Organizations in all industries are awakening to the value that operational data can bring to the enterprise. Combining operational data with other business information allows organizations to make data-driven decisions that optimize performance. However, different people use the same data in very different contexts and need information in a format that they can easily consume. And that's where the PI System comes in...
Year: 2019

As the oil and gas industry becomes increasingly digitised, torrents of data are being unleashed that can easily overwhelm companies if not properly managed. The deluge of new data raises important questions. How should it be organised? Can it be presented in a meaningful way? Who should have access to it? In addition, at any modern-day oil and gas operation, three big changes are taking place that one should consider when looking for the best approach to the digital transformation: the accelerating growth of data generated by assets; the increasing importance of emerging technologies, such as Internet of Things (IoT), cloud and data analytics; and a growing appetite for collecting and leveraging data from an increasing number of groups and specializations. Together, these drivers are making oil and gas operators focus on designing a centralized strategy for managing their operational data as they pursue “intelligent information” – rather than merely collecting volumes of data that will languish unused in data swamps.
Year: 2018