Data capture is the process of using a variety of sensors to collect relevant data that will later be processed and used for predetermined purposes. Data capture is a costly exercise, and planning will help ensure that the captured data is valid and supports reuse. Data capture tools must provide ways to organize and structure files, including data validation components that ensure captured data meets the required type and range. Data tools should allow data to be moved to the targeted destination quickly and with high quality. Oil and gas facilities typically have a large number of metering points with various departments within the organization using this data, including compliance with regulatory reporting requirements. It is important to collect data in a timely and accurate manner so that it may be cataloged and used in a larger data model.
A data-centric outlook is a core concept in digital project execution architecture where data is viewed as the most important and perpetual asset used in support of applications to produce deliverables. Within a data-centric architecture, the data model precedes implementation of a given application and remains valid long after the application is gone. In a data-centric approach, data must drive the development of projects, designs, business decisions, and culture. The emergence of cloud computing and storage enables organizations to remotely access and analyze large databases in order to make more objective, risk-mitigating, and profitable decisions.
A data model is an abstract model that organizes data elements to standardize the relationships between elements within the mode and to properties of the real-world entities they represent. In digital execution architecture, a single data model is used to represent all the informational elements that an organization needs to manage the lifecycle of a real-world asset. Within the execution ecosystem, the single data model supports all business applications and the organization’s uses for the data.
Digital asset management (DAM) is a business process to organize, store, and process digital information related to real-world assets. In the energy sector, DAM refers to organizations analyzing digital information about an asset to optimize performance, identify changing external and internal conditions, and to assess investment options through data aggregation and real-time monitoring. DAM involves the development of dedicated infrastructure, such as a technical data portal, that allows users to easily manage and preserve digital assets from any web-enabled device.
A digital engineering environment is the part of a digital project hub that encompasses the various software applications required for engineering tasks. Where under a traditional execution model, the work of engineering disciplines would be segregated and linear, a data-centric execution model requires near-live, cross-discipline collaboration to take place in a digital engineering environment. The environment also hosts any digital representations of the real-world assets recreated from data captured in the field.