Introduction

During the past 10 years, as a result of addressing the need for better resource use and increased efficiency from integrating disparate applications, we have witnessed the emergence of integrated enterprise applications in the IT realm. That has resulted in the rise of Chief Information Officers (CIOs), who have catalyzed the maturation of IT environments by seizing control of the acquisition, deployment and support of IT whose requirements for integrated and interrelated systems have necessitated standards and architecture plans to maintain consistency and interoperability. Although interoperability is still important in the OT systems — as witnessed by many initiatives, such as the International Electromechanical Commission (IEC) common information model (CIM) or North American Electric Reliability Council cyber security standards — in many cases they have been deployed as independent and stand-alone systems that might interact with each other irregularly.

Enterprise-level GIS applications are becoming the centerpiece to utility enterprise information management (EIM) processes and systems. Sorting out data issues is neither just an operations nor an IT issue. It is a corporate issue that must be solved with process, accountability and systems. Utilities can only achieve desired business performance by jointly working through and managing data quality with defined processes, clear accountability, and Key Performance Indicator (KPI) metrics tied to performance goals. This paper provides insight on how to leverage GIS as a core utility application to improve asset data quality through Enterprise Information Management (EIM).

Historical Perspective

Geographic information systems (GISs) have their roots in field automated mapping products that came from linen and mylar drawings that were scanned into raster images. Originally, IT only provided a platform. Utility Operations Support functions provided drafters to produce maps. Outage management systems (OMS) gave GIS a boost in to manage the required network connectivity model. GIS became the best platform to maintain connectivity data and provide mapping products. Asset management and engineering functions have realized that the rich asset data in GIS can help drive many investment decisions to optimize performance.

Enterprise GIS Strategic Perspective

By definition utility must manage geographically diverse assets. Whether its electric wires, gas pipelines, telecom equipment, water pumps and pipes, or wastewater infrastructure, they all must manage assets spread across their service areas. GIS network models are now being used across energy and utility companies to provide the data infrastructure to drive long-range financial decisions, as well as real-time operational decisions. Most utilities realize the value of GIS but struggle implementing real enterprise-level GIS and related integration. Many utility operations and engineering business units are too busy keeping up with day-to-day customer and stakeholder demands to consider the strategic implications of IT enterprise applications to help drive performance.

Utilities can’t solve these issues by throwing yet another application at it. There are already too many stand-alone unsupported applications in utilities IT portfolio. Because utilities must manage geographically dispersed assets, over time, many have developed, maintained and managed geographic-based asset information in many stand-alone applications and databases. Investments in GIS-like projects have been recommended without a clear business unit and IT strategy. An IT strategy should provide a road map where they are going with GIS in terms of priority applications, data consolidation and long-term system requirements. Solving performance issues with new disparate applications has led utilities to multiple databases of record without methods to reconcile or maintain data quality, in addition to excessive application support costs.

Managing disparate applications is wasting IT and business unit resources in maintaining the applications and managing the underlying data. If utilities are going to use technology to drive performance, they must deal with the underlying data management issues. Enterprise GIS must be an integrated part of the IT fabric. Stand-alone systems can no longer be cost justified, and strategic alignment is critical. Most utilities are talking about geospatially enabling, rather than building, GIS-like applications. In fact, the semantics are changing. GIS is increasingly described as a geospatial or location-based service.

As GIS gets extended across the enterprise, there is an increased emphasis on the openness and connectedness of the system. This leads to increased business benefit and value but also an increased dependence on the system and data that drives it. See Figure 1, below. Data is an asset shared by managers, designers, operations and engineering personnel, and customers.

GIS Business Value with Enterprise Integration
-> Figure 1: GIS Business Value with Enterprise Integration

Energy and utility companies need a GIS strategy that incorporates an Enterprise Information Management (EIM) framework, use core enterprise applications to simplify the IT environment, and deliver effective model-driven integration via enterprise service bus (ESB) and Common Information Model (CIM).

Utility GIS Applications
Enterprise GIS addresses issues that have constrained the legacy-automated mapping applications. Modern GIS applications provide the capabilities required to extend the GIS to every facet of the utility enterprise. As utilities push more advanced GIS applications, the need for quality data significantly increases. Here are some examples of utility Enterprise GIS enabled applications available today given the data that drives them:

– Core GIS (mapping, network connectivity, layouts, and single-line diagram/schematics)
– Graphic design transmission and distribution design and estimating
– Customer location generation
– Customer-to-network relationship management
– Engineering analysis
– Maintenance and inspection management
– Vegetation management
– Joint-use attachment management
– Streetlight management
– Right-of-way (ROW) management
– Real estate management
– Environmental management and plant relicensing
– T&D asset geospatial financial tracking
– Crew scheduling and dispatching
– Storm damage forecast and assessment
– Spatial land-based load forecast
– Strategic spatial asset management/business intelligence
 
Asset Data Management Requires a New Enterprise Approach
As a result of continued focus on operational performance business units are very focused on day-to-day operations vs. strategic applications. As enterprise applications have modernized utility IT infrastructure, many asset data management business processes have not kept up.

Business processes have not been streamlined across the utility, which means asset data gets created and managed in multiple systems. Besides inefficiency, multiple asset data sources results in a number of data issues including: poor quality, reconciliation, compatibility, reporting, confidence, and reliability and accuracy problems. Poor asset data quality limits utility GIS performance and their ability to leverage more-advanced asset management science, business intelligence and analytics.

In many utilities, the practice of managing information remains undisciplined and unfocused, as evidenced by complex integration (for example, high costs and resource lock-in associated with the development and maintenance of redundant and overlapping point-to-point interfaces), an unmanaged glut of information (such as an accelerating volume and velocity of information sources) and an inflexibility of system design, causing a delay in response to evolving business needs. Whether it’s information in GIS, a database, or on a fileserver or whether it’s about assets, customers, products, financials, employees, vendors or other areas, the needs are the same: Information must be organized, structured and safeguarded to maximize its value, usefulness, accessibility and security.

New approaches are needed for structuring and managing information as an asset. Information is seen as a strategic priority and a source of competitive differentiation and operational efficiency. However, organizations cannot use their information assets without some plan, framework, structure or model. Indeed, without an information architecture, organizations incur additional cost, complexity and risk. As we move from tightly coupled (self-contained systems) to loosely coupled systems (where we build applications on the fly), knowing the location, format, structure, context and use of information becomes critical. Accordingly, we need better approaches for structuring and managing every type of information from an enterprise perspective.

Enterprise Information Management (EIM)
Gartner defines EIM as an organizational commitment to structure, secure and improve the accuracy and integrity of data assets and solve semantic inconsistencies across any boundary, thus supporting the technical, operational and business objectives within the company’s enterprise architecture strategy. In 2004, Gartner identified EIM as a trend requiring focus and investment. EIM solves many of the issues caused by decades of individualized application development and addresses the underlying complexities that impede cross-boundary attempts at innovation, agility and transformation. However, some organizations have yet to recognize the importance of managing information at the enterprise level and the need to apply the same emphasis as other strategic assets (such as people, real estate and finances). It has taken a number of utilities a major event that caused the systems to fail to perform, leading to regulatory inquiries and subsequently mandated data improvement programs. Although adoption has been slow, the drivers, issues and pressure to deliver EIM in utilities are growing.

EIM is not a technology, it a corporate commitment to data quality. These are the essential building blocks to implement an EIM strategy:

– Vision — Organizations need accurate, consistent, secure and transparent data that flows seamlessly and continuously.
– Strategy — Organizations make a strategic commitment to EIM, which is operationalized to include projects, budgets, personnel and governance.
– Governance — The organization determines what data governance is needed and how it affects the organization, addressing development, maintenance, communications and the enforcement of data management policies/procedures.
– Organization — Parallel to governance, EIM organization handles enterprise/project-level modeling; metadata management/semantic reconciliation; master data management (MDM); implementation of technology infrastructure; data quality management, such as profiling and data stewardship; enterprise content management; infrastructure for continuous flow (among analytical, operational and transactional systems); and EIM program administration (for example, budgeting, resource management, education and project tracking).
– Process — Stewardship addresses compliance requirements for the increased accountability and transparency of information across the organization. Another process is model management.
– Reference model — This defines the technology components required, including data services, integrated content, metadata management, data quality and profiling, MDM and closed-loop information flow.
– Metrics — Data quality metrics (for stewardship and compliance initiatives), data redundancy metrics (for cost reduction) and standards adoption metrics. Metrics highlight gains from consistent, accurate master data to improve responsiveness and decision making.

EIM is a process of aligning IT investment strategy with business processes and asset data. Very simply, EIM focus defines and optimizes the data management processes with clear accountability and timely performance metrics. EIM can be thought of as a manual or automated asset data synchronization process.

How to Start EIM
Getting started requires making sure everyone clearly understands the data management business process. From this point forward, timely and accurate data management should be part of individual performance and must roll up to department performance. Process optimization should take place, over time, with additional training and updated KPI metrics. Finally, a plan to take care of backlog of data problems must be developed. A major fielding exercise could be hard to justify. However, make data management part of day-to-day work processes. For example, as asset inspection occurs, GIS connectivity validation should take place if the data management tools are simple and straight forward. Field workforce needs to establish an ownership over the data management process and take pride in quality work as they perform work on the assets. Now that work includes EIM asset data quality. Back office support staff must likewise commit to timely processing turnaround (with KPI metrics) so the field staff can see the same level of commitment throughout the company and will ultimately see the value of their data quality efforts through performance improvement.

Emerging mobile workforce management applications are bridging the gap to enable EIM in the field. Ultimately detailed asset life-cycle application data analysis will be required to identify where the gaps in current processes and how to automate and streamline existing processes. Future papers will address best practices for this analysis.

Reference Model and Semantics
The energy industry’s collaborative business environment is providing EIM technology tools that will facilitate data and process integration (for example, enterprise application integration [EAI], Web services, UML, XML, business process modeling and Business Process Modeling Language), as well as foster the adoption of common semantics models and industry-specific vocabularies.

Common Information Model (CIM) is an International Electrotechnical Commission (IEC) standard (IEC TC57 standards 61970 and 61968) that is used for the integration of IT systems in the electric utility industry worldwide. Although CIM is an internationally adopted standard sanctioned by the IEC, the adoption rate has been lower in Europe and Asia/Pacific, where many utilities are less aware of CIM.

Activities have been aimed at creating common standards across energy domains, resulting in data models, common vocabulary and business processes that facilitate interoperability between line-of-business (LOB) applications and among energy enterprises. The CIM information is typically transported in the utility enterprise through an enterprise service bus architecture (ESB, e.g. WebShere, MQ, webMethods, Vitra, TIBO, etc.). MultiSpeak is a similar yet simplified model developed by the National Rural Electric Cooperative Association and is on a path to ultimately harmonize with CIM.

Utility enterprises aiming to achieve operational excellence must look for products that go beyond traditional integration technologies (such as application programming interfaces and EAI) or that are trying to incorporate business process integration by extending the functionality through inclusion on the data-model level. Many energy and utility IT and business leaders are developing their road maps to adopt the CIM and evaluate the impact on their core applications. In addition to vision, IT leaders should select software by the vendor’s ability to address product integration that uses CIM-enabling technology and architectural directions.

What Does EIM Look Like When Properly Implemented?
Figure 2 is an example of EIM based asset data synchronization for utility applications. In most cases T&D utility assets start in GIS, thus the critical part of any EIM strategy. For example, an estimator uses GIS to design the line extension in GIS that send a CIM-encoded message through the ESB to notify the asset services application that an asset has been created. Asset services validates that a valid asset type was created by a valid user and application and then sends the asset info to the applications that must know the addition or changes. If applications don’t support CIM interfaces directly, adaptors can be built to decode the CIM message structure as part of the system’s proprietary format.

All other applications that need the network asset data get it synchronized from the GIS model. Then users of the other applications must supplement the remaining attributes to complete any data requirements from the other specific applications. It does not make sense to include a lot of specific asset attribute data in the GIS model simply to pass to other applications but should actually be stored in the applications that need them. Asset Services provides the virtual asset register to point where the data of record is stored. CIM provides the reference model for Asset Services. CIM must often be extended for utility specific requirements. But this is a relatively painless process with the proper data modeling tools.

 EIM Based Asset Data Synchronization for Utility Applications
-> Figure 2: EIM Based Asset Data Synchronization for Utility Applications

In this case, asset services maintain the meta data to locate where appropriate information resides, identifies who has security to create, maintain and delete data from what applications. Asset services also provide keys to join data for advanced asset management analysis. Possibilities for asset management business intelligence become limitless. Here are a few examples:

– Transformer load management
– Regional reliability assessment
– Cost analysis by asset type or region
– Revenue analysis
– Failure rates by region
– Performance based maintenance planning
– Budget prioritization based on desired performance
– Asset risk assessment
– Remaining life analysis based on historical performance

Conclusion

In many utilities, the practice of managing information remains undisciplined and unfocused, as evidenced by complex integration (for example, high costs and resource lock-in associated with the development and maintenance of redundant and overlapping point-to-point interfaces), an unmanaged glut of information (such as an accelerating volume and velocity of information sources) and an inflexibility of system design, causing a delay in response to evolving business needs.

EIM solves many of the issues caused by decades of individualized utility application development and addresses the underlying complexities that impede cross-boundary attempts at innovation, agility and transformation. However, some utilities have yet to recognize the importance of managing asset information at the enterprise level and the need to apply the same emphasis as other strategic assets (such as people, real estate and finances). Utility IT, engineering and operations leaders have recognized the value of enterprise GIS as a critical entry point for T&D asset data management. Although adoption has been relatively slow, the drivers, issues and pressure to deliver EIM are growing as event undercover the problems with data quality.

Utilities need more than a bunch of cool technologies to drive performance. Asset data must be recognized as the fuel that empowers utility enterprise GIS and other IT applications. To truly drive business value from Enterprise GIS and other IT investments, information must be organized, structured and safeguarded to maximize its value, usefulness, accessibility and security. Utility IT, operations, and engineering leaders to recognize the need to manage asset information as currency — save, invest, spend, manage and account for it to at least the same level they manage the physical assets.

Bradley R. Williams, PE
Research Director, Energy & Utilities Industry Advisory Services – Gartner