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An intelligent pipeline strategy enables companies to create new capabilities , new business models , and innovate ahead of the competition . By deploying information management systems , powerful analytics , automation of workflows , and driving behavioural change in workforce , oil and gas companies can evolve and change how work is performed to build a sustainable , profitable organisation .

Companies must strive to reduce operational costs and their carbon footprints across complex value chains , including a growing and ecosystem of business partners .

While SCADA and production measurement software provide features to identify issues with measurement , there are opportunities to perform analytics on measurement and raw data to provide earlier indications of issues . problems than they solve , as businesses must spend more time wrangling data than using it to deliver

business value .

Stuart Parker , Oil and Gas Industry Principal , AVEVA

When unstructured operational data builds up in data lakes , traditional IT technologies can create more problems than they solve .

Forward-looking oil and gas enterprises are installing intelligent pipeline frameworks to turn massive amounts of data into wisdom that generates business value . By using existing operational data as well as new data sources , companies can take a modelfocused approach that puts them on the path to operational excellence .

Data with business context

Oil and gas pipeline companies were collecting huge amounts of operational data long before the term Industrial Internet of Things , IIoT was coined . However , turning vast amounts of raw data from SCADA , pipeline applications , ERP systems , and more into contextualised information around equipment and processes is often challenging . Contextualising this data ultimately enables operational improvement .

Unfortunately , many companies are rushing to layer in new technologies and solutions such as cloud , machine learning , edge , IIoT , and predictive analytics before building the right data and analytics foundation . Adopting these new solutions can potentially deliver new and valuable insights , but pipeline companies must first enact solid data management and analytics strategies . Deploying an enterprise-level , real-time data management platform lays the foundation for future technology success .

Generating value from data

To produce actionable intelligence , data must be structured and accessible to those who can best use it , particularly subject matter experts who have the knowledge and experience to put data insights into action .

Not only does a wealth of raw data , devoid of context , structure , or quality , rarely pay dividends , those tasked with utilising that data often find it difficult and cumbersome to extract insights . If users are too slow to develop and implement sustainable solutions , the company will accrue significant lost opportunity costs .

Digital transformation success hinges on having a single source of truth . Operations data must first be standardised and contextualised before it can be analysed and visualised . Comprehensive data management systems can lay the foundation for operations data integration , data validation , and analytics .

When unstructured operational data builds up in data lakes , traditional IT technologies can create more

A centralised operations data management platform uses standardised and templatised tag-naming

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