Infrastructure leaders’ asset information governance questions answered
Introduction
At the Electricity Engineers’ Association Conference held during June 2023 in Ōtautahi Christchurch, Asset Dynamics’ Jules Congalton spoke about the critical role of information governance for infrastructure asset management.
In this article we answer five insightful questions that were posed by infrastructure leaders following this talk:
What is the typical level of maturity of information governance in NZ infrastructure organisations?
Is there value in standardising asset data models for infrastructure?
Are there standard asset data models for infrastructure?
Does the size of the organisation affect the implementation of information governance?
What technical challenges exist when establishing information governance?
Question 1: What is the typical level of maturity of asset information governance in New Zealand infrastructure organisations?
There are a range of standards, frameworks, and criteria relevant for assessing information governance maturity in an asset management context. These include:
The New Zealand Electricity Engineers’ Association (EEA) Asset Information Maturity Framework
ISO 8000 Part 62
The Asset Management Maturity Assessment Tool (AMMAT)
The Institute of Asset Management’s Self-Assessment Methodology (SAM+)
The International Infrastructure Management Manual (IIMM)
Maturity assessment is a valuable exercise as it provides an objective perspective on current organisational capability, and a benchmark against which improvements can be tracked. Such assessments also create focus on critical risks that need to be managed so that data is available to processes at the required level of quality.
Asset Dynamics regularly completes asset information maturity assessments using the EEA Asset Information Maturity Framework. This maturity assessment tool was originally developed for use in the electricity supply sector; however, it is based upon guidance from the Global Forum on Maintenance and Asset Management (GFMAM) and therefore is broadly applicable to any infrastructure organisation.
In alignment with the GFMAM Asset Management Landscape, the EEA Asset Information Maturity Framework considers four subjects within the Asset Information subject group:
Asset Information Strategy
Asset Information Standards
Asset Information Systems
Data and Information Management
The organisation’s maturity within each subject is assessed against a five-point maturity scale that ranges from Level 1 – Aware to Level 5 – Optimised.
The following chart shows the consolidated results of asset information maturity assessments of New Zealand infrastructure organisations completed by Asset Dynamics using the EEA Asset Information Maturity Framework.
On average, the highest level of maturity is in Data and Information Management. This reflects the fact that most organisations have established processes that enable asset management data and information to be collected and stored.
However, this is often undermined by immaturity in Asset Information Strategy and Standards, which results in a gap between what is being collected and what the organisation requires to operate effectively, and a lack of standard criteria against which data management practices can be assessed and controlled.
Question 2: Is there value in standardising asset data models for infrastructure?
Standardising infrastructure asset data models offers a range of benefits for the communities served by infrastructure.
Data Sharing and Collaboration: When infrastructure organisations share data with external stakeholders, such as government agencies, contractors, or partners, standardised data models simplify the exchange of information. This promotes collaboration and transparency.
Knowledge Transfer: Standardised data models make it easier for new employees to understand and work with asset data. It reduces the learning curve and accelerates knowledge transfer within the organisation.
Data Quality and Consistency: Standardised data models promote data quality and consistency. When everyone within an organisation and an industry uses the same data model, it reduces the likelihood of errors, duplicated data, and inconsistencies in asset information.
Interoperability: Standardised asset data models enable different systems and software applications to communicate and share data seamlessly. This interoperability is crucial for infrastructure organisations that rely on various tools and technologies for asset management, maintenance, and reporting.
Cost Efficiency: Standardisation can lead to cost savings by streamlining data management processes. It reduces the need for custom data integration solutions, simplifies data migration, and minimises the resources required for data maintenance.
Enhanced Decision-Making: Standardised data models provide a unified and clear view of asset information. This allows for better data analysis and informed decision-making, which is critical for infrastructure organisations to allocate resources efficiently and plan for maintenance and upgrades effectively.
Regulatory Compliance: Many infrastructure organisations are subject to regulatory requirements regarding asset management and reporting. Standardised data models can facilitate compliance by ensuring that data is organised and formatted according to regulatory standards.
Benchmarking and Performance Metrics: Standardised data allows organisations to benchmark their performance against industry standards or peer organisations. This can lead to insights for improvement and optimisation.
Are there standard infrastructure asset data models?
Standard asset data models have steadily developed within specific infrastructure sectors and some cross sector standards also exist. We identify key standard asset data models that are in use in New Zealand below.
Sector Specific Standard Asset Data Models
The New Zealand transport sector is adopting the Asset Management Data Standard (AMDS). This standard offers a consistent, integrated approach to data structures and asset management for land transport assets.
As part of New Zealand’s Water Services Reform asset data standards have been developed covering water asset hierarchies, asset attributes and standard field values.
The International Electrotechnical Commission (IEC) Common Information Model (CIM) is used by electricity utilities internationally as a data definition for an interface for data exchanged between systems. When this is done well it can be implemented via a messaging hub so that multiple systems can consume this data.
The New Zealand EEA AIMF includes guidance on definitions for master data management.
Cross Sector Standard Asset Data Models
Building Information Modelling (BIM) provides a standard asset data model for buildings and facilities and is seeing increasing use across a range of infrastructure sectors. The Australasian BIM Advisor Board provides a useful set of asset data fields for asset managers.
The Institute of Asset Management provides subject specific guidance of asset information management which is relevant to any organisation managing physical assets. While this document does not specify standard asset data models, it provides valuable guidance on principles and approach.
For some organisations there is no suitable standard data model, or the standard model may not meet the organisation’s requirements. In these situations, the following process is recommended to enhance the asset data model:
Define the asset management framework (objectives, structure, processes, decisions).
Based on those processes, define the asset hierarchy required and asset information required. This is the business view of the data, i.e., standard terminology, etc.
Map that to the logical master for each set of data (e.g., location information is mastered in GIS, equipment records and work history is mastered in the EAMS or CMMS, and energisation state is mastered in operational systems).
Assess the extent to which the data meets requirements.
Question 4: Does the size of the organisation affect the implementation of information governance?
The primary role of asset information governance is to align the management of information with the organisation’s asset management objectives. This is the degree to which the various aspects of information management work together cohesively to manage information related risks and opportunities, including in relation to safety, compliance, customer satisfaction and cost. An asset information strategy captures the asset information components of the organisation’s asset management objectives and enables an information governance group to ensure alignment.
What is required to achieve this alignment can vary significantly between smaller and larger organisations due to differences in their structures, communication, and performance measurement approaches.
Small organisations often have simpler, flatter structures with fewer layers of management. This often means more direct communication and quicker decision-making. Alignment is typically easier to achieve because there are fewer people and departments to coordinate.
In contrast, large organisations tend to have complex hierarchical structures with multiple departments, divisions, and layers of management. As a result, a more structured and systematic approach is required to realising value from information assets. Representatives from teams that depend on information to operate effectively must meet to agree priorities and plan improvements. This may include stakeholders from asset planning, work management, field services, finance and IT.
In small organisations, communication tends to be informal and face-to-face. Employees have more direct access to leadership, making it easier to align goals and expectations. However, large organisations often rely on formal communication channels, which can lead to information silos and slower transmission of information. Ensuring that messages are consistent and reach all levels of the organisation can be a significant alignment challenge.
When measuring performance, small organisations tend to have simpler and more easily measurable performance metrics. Alignment around these metrics can be more straightforward. With larger organisations, there are often multiple, complex performance metrics across various departments. Ensuring alignment around these metrics can be more challenging.
While the goal of information governance is to align information with asset management objectives in both small and large organisations the challenges and approaches to achieving it can differ significantly based on their size, structure, and culture. Small organisations often have an advantage in terms of agility and direct communication, while large organisations tend to need to implement more formal processes and strategies to maintain alignment across their diverse operations.
Question 5: What technical challenges exist when establishing asset information governance?
The key technical tasks to implementing asset information governance are identifying the information required for each asset management process and understanding what data the organisation holds.
Once these steps are taken, the quality of asset information can be assessed against requirements and any risks and opportunities can be identified. Assessing the quality of data against organisational requirements is key to implementing asset information governance.
The challenges to identifying, locating, and cataloguing data held can arise from a number of sources. One of these is data silos where data is stored in department-specific or application-specific silos. These silos can make it difficult to locate data because it may not be readily accessible to users outside of those departments or applications.
Another challenge is a lack of data classification. Without proper data classification and metadata tagging, it can be challenging to identify and categorise data accurately. This lack of metadata makes it harder to locate specific data and understand what the data represents. This includes understanding the relationship between asset data sets such as financial data, work orders, asset condition data, defects, and asset performance data.
Data duplication also occurs in some organisations where data exist in various locations and systems, making it unclear which version is the most current or authoritative. This can lead to confusion when trying to locate the most accurate data.
To overcome these technical challenges when implementing asset information governance, the recommended approach is to start with the known sets of data which are deemed to be critical to achieving the organisation’s asset management objectives. There is generally significant value in implementing information governance in phases, based around an area of business or a specific asset management objective or strategy. This allows the assessment of which parts of the required data is available and fit for purpose and where there are gaps.