Using data — the life blood of daily operations — situational awareness and facility systems, can be overwhelming due to the frequency of data arrival and massive volume. Many organizations face the challenge of cleansing, augmenting and gleaning insights from complex data provided from a multitude of data sources and data management systems. Whether you are using OSIsoft, a company well-known for its time series data historian, or another company, value is obtained by visualizing and obtaining insights. Without data organization, context and visualization tools you are not likely to achieve your goals with the application of analytics such as operational processes improvements, situational awareness and capital spending.
OSIsoft released significant new features and capabilities in 2010 as part of the Plant Information (PI) System making it easier and faster to access, contextualize, analyze and visualize their operations within a PI System. These innovations are why OSIsoft is the leader in operational analytics systems.
Contextualizing Data
Asset Framework is a crucial component of a modern PI System implementation. The framework provides contextualization and a repository of assets and process-based models as well as a user-friendly, hierarchical representation of assets, processes and systems. Organizations can augment operational time series data by integrating data from multiple sources, giving operational data vital context. Insights can be found from an organization’s data for anything from process adjustments to the ability to respond to weather conditions to overarching data incorporated from transactional or relational database systems.
Simply integrating multiple sources of data into a single platform won’t result in user-friendly or actionable insights. By also building out a modeled, hierarchical representation of assets and processes in the Asset Framework, a foundation is created to sort, analyze and visualize actionable insights. Corresponding digital initiatives for an organization can also be incorporated as additional technologies are implemented. Planning a well-developed Asset Framework implementation is the first critical step to identifying insights derived from operational data to inform strategic decisions.
Implementing an Asset Framework
Asset Framework implementation could also involve the migration of a legacy historian to the organization’s PI System, as well as additional data source interfaces from the more than 450 interfaces found in the PI System library. Once the additional data sources have been integrated with an organization’s operational time series data in the PI System, organizational and domain specialization can be applied to plan a thoughtful and effective Asset Framework.
Asset Framework templates streamline and standardize the build-out of asset and process models in the Asset Framework. This standardization creates consistency across the program implementation. Efficiency is gained with the reduction in time required to build similar or overlapping assets and processes. Templates also significantly expedite the rollout of future changes to assets and processes with a standard approach implemented in every facet of the program.
Importance of Domain Specialization
Leveraging domain and subject matter knowledge are key to success in deploying the Asset Framework. By having a team with the experience needed to implement and analyze a complex set of data, organizations know the information will be analyzed efficiently and accurately. Organizations will then be able to shift their investments to expand businesses, improve energy and resource efficiencies, as well as diversify traditional offerings through innovation to stay competitive.
Many industries are beginning to find value in digital twins, or digital replicas of physical assets and processes. Applying a more holistic and largely nontraditional approach to the planning and build-out of any digital twin, with the inclusion of the Asset Framework, could help an organization yield superior end-to-end life cycle results. Often, a team with the experience of taking a project from start to finish is vital for an organization’s data management success, rather than hiring a company to only complete the Asset Framework implementation.
Successful planning and implementation keeps asset life cycles top of mind by incorporating an owner’s requirements, engineering design intent, construction realities, commissioning and startup, as well as warranty considerations. Additionally, incorporating craft and engineering disciplines and domain specialization considerations backed by the operational data and associated insights, enables organizations to effectively monitor, analyze, operate and maintain the asset investment throughout its life cycle.
Discover how business analytics and intelligence can help your organization — regardless of industry or complexity — overcome data challenges.
