asset management

Asset Management Fundamentals: Topic 5 - Asset Knowledge - Data Collection and Management

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Topic 5 - Asset Knowledge - Data Collection and Management

Table of Contents Preview ...................................................................................................................................... 2

Learning Objectives ................................................................................................................ 2 Introduction ............................................................................................................................... 2 Information Required and Prioritising ...................................................................................... 4 Level of Data Detail .................................................................................................................. 5 Asset Register Structure Hierarchy and Numbering ............................................................... 6 Planning the Data Collection Process ..................................................................................... 8 Data Capture Options ..............................................................................................................10 Sources of Data .......................................................................................................................11 Cost Factors ............................................................................................................................12 Pilot Programs .........................................................................................................................13 Data Accuracy and Confidence ...............................................................................................13 Specialised Services for Data Collection ................................................................................14 New Assets Data Input (As Constructed) ...............................................................................15 On Going Maintenance of the Data ........................................................................................16 Summary ..................................................................................................................................17 Review Questions and Sample Answer Summary .................................................................18 References ...............................................................................................................................23

Readings ...............................................................................................................................23 Activities ................................................................................................................................24

Asset Management Fundamentals: Topic 5 - Asset Knowledge - Data Collection and Management

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Preview Learning Objectives The learning objectives of this topic are as follows:

• Analyse the importance of carefully planning the data collection process based on identified information requirements, structure of the data framework and the use of pilot test programs to ensure data collection is done efficiently at the lowest possible cost to the organisation.

• Analyse appropriate asset register structures and hierarchy and numbering systems that can be employed.

• Investigate how data can be collected as part of normal day to day business of the organisation and issues of data accuracy and levels of confidence in the data collected.

• Discuss the level of detail of data that needs to be collected for various asset management tasks, prioritising the data collection process and possible sources of that data.

• Address the importance of collecting data on new assets as they are created or donated for input to the asset management systems and various tools that are available to facilitate such.

• Investigate various specialist services that are available for data collection for particular asset classes.

• Analyse how to best manage the ongoing maintenance of the data collected and the importance of security to ensure the ongoing integrity of the data and maintaining its currency.

Introduction To be effective, asset management involves firstly knowing what assets are owned, their value, age, condition and performance. We saw in previous topics, the way in which services required to be delivered, are determined. We now start to focus on the assets themselves and what information we need to assess how well they can deliver these services By its very nature, asset management with all of its associated condition assessment and performance monitoring involves the collection, storage and analysis of an enormous amount of data. Management of how that data is collected, gathered, stored and integrated is critical to minimising the costs and resource requirements of any asset owning organisation. The use of new technology to assist in this regard should always be considered. It is critical that this phase is very well planned and managed, with priority given to collecting data which the organization needs to support the appropriate level of financial reporting, performance measurement and technical asset management issues. Data collection can typically be the most expensive part of the whole asset management process and so needs to be carefully planned to minimise those costs. The Pareto principle can typically be applied here, where 80% of the necessary data can be collected for 20 to 50% of the total cost of 100% complete data. Data collection can often be most economically collected as part of day-to-day operation and maintenance activities.

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When a specific data collection activity is identified and planned, it is often beneficial to run a pilot program first to gain a better appreciation by all concerned as to the resources that are going to be required, the associated cost and most importantly, to test that the outcomes actually meet the needs of the organisation. In all cases it is essential that the adopted data collection program is fully documented with standards and quality procedures spelt out to ensure that data collected is the right data, and that asset managers can have confidence in the quality and timeliness of data available for analysis. The ongoing management and maintenance of asset data must be given consideration at the outset of data capture activities. Data integrity is also of paramount importance and protocols need to be established at the outset, that specify who has access to be able to modify or update data. These processes should generate an auditable trail so that the organisation can be satisfied that protocols are being adhered to. This does not mean that access to data should be denied to anyone who has a legitimate use of that data, but the ability to modify the data needs to be controlled. The requirements and capabilities of information systems, the opportunities for ongoing improvement in data accuracy, and the upload of data relating to new assets as they are commissioned, are all key issues associated with data management. Thought also needs to be given at the outset when designing data systems, about the likely eventual need to transition to perhaps a new more sophisticated system. The ability to easily migrate data across to such new systems is critical to the future success of any such upgrade. A staged approach is often best when tackling data collection, starting with minimum data required for legislative compliance and to meet reporting requirements. Organisations should first try to use existing data wherever possible. This could be in the form of hard copy plans, spreadsheets, and various databases including the financial asset register. If this data is insufficient to address the business needs, then identify which data is missing. Be careful to only identify data that is needed for end reporting. It is tempting to collect much more data than is needed – we should avoid such temptation. There are quite a number of specialist services available for collecting specific types of data and some of these involve very specialised and expensive equipment. Such equipment could not logically be justified for ownership by each asset organisation, so it makes good sense to utilise these specialist services – as long as their output meets the actual business needs of the organisation. A good example of such service is the pavement measuring tools such as the vehicle mounted electronic pavement monitoring survey devices offered by a number of commercial companies, for providing condition data on the existing road networks. Similarly, there are services employing closed circuit television (CCTV) for gathering condition data on buried assets such as stormwater, sewer pipelines etc. So, as can be seen, the data collection and management phase is one that needs careful consideration and can typically be one of the most resource intensive and costly parts of the whole asset management process. For these reasons, it is incumbent on asset managers to fully appreciate the ramifications of what they propose for data collection and be able to justify the effort involved.

Asset Management Fundamentals: Topic 5 - Asset Knowledge - Data Collection and Management

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Information Required and Prioritising When considering what information is needed by the organisation to effectively manage the assets, it is worth again reinforcing the benefits of following a staged approach. Start with at least the minimum information requirements, (to meet legislative, reporting requirements) and then add to that, over time as the asset management processes mature. Things like asset values, remaining life and their replacement or renewal costs are probably the most important. Next we would look to information that might help improve maintenance, and then finally information that will improve risk management and optimised decision-making. It is up to each organisation, however, to determine its needs. So a good asset database, (which can be as simple as a spreadsheet), but more commonly will be more sophisticated software, is the starting point. Staff need to be able to identify assets and their components, their location and associated data as outlined above. Reading 5.1 Refer to Table 2.4.1 and case study 2.28 in the IIMM pages 2/56 and 2/57 for examples of typical data requirements that one would expect to find in an asset management system depending on its level of maturity. Again, these requirements need to be tempered by determining what the organisation needs to meet its particular objectives. Case Study 29 provides an example of this. Refer to case study 2.29 on page 2/50 of the IIMM for an example of a risk based approach to setting priorities for data collection.

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Level of Data Detail We highlighted earlier, the importance of collecting only the data that will be needed to support the organisation’s business needs and decision making processes. Of course there will likely be a number of such business needs including some legislative reporting requirements, such as required by various Acts and drivers like the Accounting Standards. Government reporting requirements and reporting to customers/ratepayers are also drivers. When deciding on the level of data to be collected, consider the following:

• the purpose for which the data is required • availability of resources, e.g. skill levels, equipment • accessibility and quality of existing data • data management issues, e.g. data maintenance costs • the required completeness and accuracy of the data • the asset condition and criticality • data collection techniques • whether the extra detail will make a material difference.

(IIMM 2015 p 2/51) Data needs may vary for each asset class depending on risk issues and their particular level of sophistication on the asset management journey. As highlighted earlier, it is also worth employing the continuous improvement approach with “minimum” level data collected initially and then more data can be added at a later date as resources become available and needs change. When considering the business needs, the following might give some prompts for what data to collect, how much and accuracy etc.:

• maintenance (planned and unplanned) management systems needs. • asset accounting outputs and reporting needs. • prediction modelling. • risk management. • optimised decision support systems. • GIS input.

It is also important to consider asset groups across the organisation, so that there is appropriate consistency in how each group is treated. Reading 5.2 Refer to Figure 2.4.2 of the IIMM on p 2/52 for an example of possible level of detail for data collection requirements to satisfy a variety of parameters, what each of these typically address and the data fields that one would expect to see data collection for each.

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Asset Register Structure Hierarchy and Numbering Data needs to be stored in a logical framework and hence asset hierarchies have been developed to make the task more manageable. These typically apply by splitting the assets into their various classes or broad asset types (roads, water supply etc.) and then further breaking them down into facilities, assets, components, sub-components etc. Again, we should carefully assess the business needs of the organisation when deciding on the hierarchy or structure of the data framework. Typical hierarchies can be based on asset type or asset function or a combination. The following figure 2.4.3 from the IIMM demonstrates such.

(IIMM 2015 P 2/52)

Key issues to consider, in terms of complexity of the hierarchy include those as listed in the IIMM, and are as follows:

• The different types of assets to be managed and the structure; • Information required to manage the assets at the various levels; • The ability to report at appropriate levels to stakeholders; • The component level at which maintenance activities and costs area to be assigned; • The asset hierarchy capabilities of the AM information system (different systems can

support a different number of levels); and • Specific network requirement such as linking of assets (ie connectivity of pipe sections

for hydraulic modeling). (IIMM 2015 p 2/62)

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Information to be derived from the data will vary depending on the users needs. Operation and maintenance staff will typically be concerned at the component or sub-component level. Data will likely need to be aggregated for analysis at the overall asset class level for long term strategic planning and financial reporting. It is important that asset hierarchies for various asset groups across the organisation are developed and adopted on a corporate wide basis to ensure consistency. Some examples of how asset data structures may be set up in various hierarchies can be found in the IIMM at table 2.4.3. Reading 5.3 Refer to Section 2.4.6 of the IIMM pp 2/63-65 to see a number of examples of the way in which various asset types can be broken down into a number of discrete asset headings with a number of service areas for each that can then be further broken down into various components. Asset identification, has in the past, been an issue of major concern with the numbering systems employed, critical to allow sorting and analysis of data and retrieval. So called “intelligent” numbering systems were advocated to allow identification of type, location, and a range of other attributes through uniqueness of the number. Fortunately, with the advent of GIS and better database technology, this issue has been largely overcome and now it is possible to easily retrieve data, search and analyse data etc. without having to rely on the numbering system to identify the assets. It is of course important to ensure the data systems integrate with the GIS and other organizational software. It is also important that the systems can accommodate possible future upgrades. It is still important, however, that an asset identification system is able to provide a unique identifier for each asset – for assigning and retrieving information. As detailed in the IIMM, an identification system should:

• be appropriate for the asset hierarchy and software systems to be used; • have simple rules for assigning numbers; • allow for the accommodation of newly created assets; • avoid unnecessary complexity; and • allow existing numbering systems to be incorporated (where possible).

(IIMM 2015 p 2/65) As with the asset hierarchy, the numbering system should be consistent across the organisation to enable linking of data. This obviously becomes potentially more of a challenge where there are different asset management software systems in place for different asset groups. Numbering systems generally follow three major categories, with the preferred system being determined by organisational needs. The three categories are:

• Unintelligent (random sequential numbers). • Semi-intelligent (asset identification that may indicate the type of asset, department, or

responsible organisation and may identify an asset’s approximate location but then uses unintelligent sequential numbers for the balance of the number).

• Fully intelligent (the asset identification will be structured to indicate the type of asset, the location, and other items that can be identified through the uniqueness of the number).

(IIMM 2015 p 2/65)

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As highlighted above, the need to employ intelligent numbering systems is now less frequent due to the power of GIS linked systems but there will still be cases such as complex facilities (power stations or the like) when some form of intelligent numbering will still be required. As detailed in the IIMM, some good points to note if setting up an intelligent numbering system, are as follows:

• emphasis should be placed on information being input into the various fields as opposed to asset identification. For example, asset description should be consistent across an asset group, e.g. when searching for a pump, the word pump should be a standard descriptor across the asset group;

• care should be taken when implementing sequential numbers to allow for future expansion of the data set and the addition of new assets;

• consult with other organisations that have used these asset identification processes, to learn from their experience;

• systems adopted must be capable of being used for any chosen software; • avoid the use of alpha-characters in the asset identification that have the potential for

being confused as numbers, e.g. O and I; and • integration with existing systems (GIS, financial) will require resources for data matching

and data scrubbing. This can be an extensive exercise. Planning the Data Collection Process As we have covered previously, Information Systems for asset management are developed from the data gathered on the particular asset classes. The data on its own does not provide the answers needed. It is how that data is used through analysis and to create “information” that is then of value for decision making. Obviously, the data that needs to be collected is driven by the information system requirements. There will be a lot of data input at the time of creation of the asset and then further data needed during the life of the asset. We discuss these phases in more detail later in this Topic. Hence, when beginning the planning of a data collection program, we must first have a clear understanding of what outcomes we require from the information that will be derived from the data collected. We repeat here the diagram from the IIMM – Figure 2.4.4 to remind you of the basic steps involved.

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Data Collection Strategy Flowchart

(IIMM 2015 p 2/66) Remember that data collection will potentially be the biggest single demand on resources and hence costs, for the organisation. Accordingly, when planning for such, we should look to solutions that will minimise the costs. This can be assisted by:

• Collecting only the data that is absolutely needed (the correct data). • Do so as cost effectively as possible • Record the accuracy level of the data collected. • Ensure all of the necessary data is collected (completeness).

Costs can be saved by establishing programs that involve data collection being done as part of normal routine maintenance and operational activities. This raises the issue of which options to consider for optimal data collection. Developing a business case for the data collection program makes good sense to provide some rigour to be balancing act needed to ensure the cost associated with the level of data to be collected, is appropriate.

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Reading 5.4 Refer to case study 2.31 of the IIMM on page 2/67 for an example of developing a business case for data collection. It is obviously important to note that a significant amount of data must be “collected” on creation of the asset and held in asset registers for use by a variety of interested stakeholders across the organisation. This will typically include financial data of interest to the accountants for financial reporting, operation and maintenance staff utilising data on the way in which infrastructure assets should be operated and maintained optimally, and of course the asset managers who have responsibility for the whole of life cycle performance, with concerns about useful life, optimal intervention actions for renewals/upgrades etc. So initial data collected will include construction dates, costs, projected useful life, materials involved, design plans, as constructed detail and so the list goes on. When it comes to further collection of data during the life and operation of the assets, there is a need to focus more on the performance of the assets, their condition, how well they are meeting service delivery requirements etc. and information to assist modelling of how they might be deteriorating over time. All of this is geared towards helping decision making about optimal intervention for renewal or upgrading of the assets.

Data Capture Options The data collection program should consider a range of data capture options to determine the most cost efficient approach. These options need to balance time, cost, and data confidence as follows.

• Time – consider the timeframe of the project knowing when data is required or more importantly, when the resulting information and reports are needed.

• Cost – consider the internal and external costs associated with setting up the data capture project, assessing the assets, managing the data, maintaining the data and producing the required reports.

• Data Confidence – typically a subjective understanding of the data quality but can be quantitative, based on accuracy, completeness and reliability.

Manual hardcopy data collection forms can be used to collect data. However, they are prone to data entry errors and the inefficiencies of double handling data as well as being more time consuming compared to electronic options. Data collection using hard copy forms should be considered if there are only a small number of assets involved.

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Electronic data collection is the best approach to ensure cost and time efficiencies including data accuracy and ongoing data maintenance. Later sections describe various options that allow a Mobile application which is directly linked with software applications to provide a data collection tool that is integrated with analysis and reporting. There are examples proving that following good process with effective software on PDAs, you will reduce your survey and data entry time by a factor of two. (IPWEA 2009 P32)

Sources of Data Data can be sourced in a variety of ways and again we re-iterate the aim to minimise costs by utilising existing resources wherever possible. Often there is a wealth of data held in existing documentation that can be sourced and prove to be of value for asset management purposes. Typical examples are historical records of design plans, specifications, cost estimates, dates of construction etc. Also, if prepared, “as constructed” field books and the like can be used. Translating this data into GIS layers or into relational databases can then allow better use of the data. When planning a data collection exercise, it is worth spending the effort to firstly carry out a desktop survey to review all available existing data. The assets themselves, in the field, are of course another major source of data when it comes to carrying out condition assessments or undertaking modelling to determine deterioration profiles. Fortunately, advances in technology are improving the cost effectiveness of gathering field data. As detailed in the IIMM, the use of laptops, PDAs, tablet devices and palm held data loggers etc. have greatly improved the cost and accuracy of such data collection. Most AM information systems are now either web-based or can be web-enabled so that with use of wireless technology, staff can remotely access the main asset register back in the office. An alternative is that field computers hold a copy of the main asset register and receive regular updates from the main register and send back updated data. CCTV is another major source of data for buried pipe network assets. Field computers allow rapid data input and uploading into the main asset register. The process applicable is:

• download a sub-set of the asset register applicable to the site or district • using pen-based or data loggers or map-based technology, update the asset attributes,

asset condition or maintenance work order • return data-set to main office (can be via telecommunications) • validate the data collected on-site • upload data to the main asset register and update all appropriate changes • update the audit trail during the up-load process.

While field-based computers have proven successful, with improved data accuracy, however organisations need to pilot the computers to determine their suitability for the environment in which they operate. Once it has been shown that there are benefits in using the computers, the

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computers and training can be implemented across the organisation. Other important considerations include:

• durability of the hardware, screen size etc; • ease of use; • validation of all field data entry; and • an audit trail to track the details of all changes.

(IIMM 2015 p 2/70) Other cost effective methods include factoring data collection into:

• the planned and routine maintenance activities, • when gathering data on asset condition etc. of the assets being maintained. • when commissioning or upgrading assets; and • in combination with other asset groups or external utility operators.

Do not forget to also utilise digital photos as part of the process. Linked into the GIS, such photo data can greatly assist in informing users. (A picture is worth a thousand words). Reading 5.5 Refer to Table 2.4.4 of the IIMM on p 2/67 for some examples of typical sources of data and to Case Study 2.32 on p 2/68 of the IIMM to review how to use existing data, to improve asset management knowledge. Cost Factors We have mentioned already that data collection for asset management can easily become the most expensive part of the whole exercise. Hence the importance of continually seeking to undertake these activities at lowest cost by collecting only that data that is going to be needed for the business as identified through the Data Collection Strategy Flowchart that we covered earlier. Software solutions for large computerised asset management systems can make data management far easier, however, they can also be a trap in that they can demand significant amounts of data on which they rely, to be able to operate. Be aware of these issues and the resourcing/cost implications before committing to such. As an alternative, fairly simple spreadsheets and databases, these days, have enormous power to manage and manipulate data. The main factors influencing the cost of data collection include:

• Availability of data. • Skills of staff. • Software availability • Time availability • Level of detail. • Accuracy required.

Refer to Table 2.4.5 of the IIMM on p 2/70 for some suggested ways of cutting costs for data collection. (IIMM 2015 p 2/70)

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Pilot Programs The data collection and supporting processes should only be implemented after successful completion of a pilot study. Implementation of a pilot study is a cost-effective way to test assumptions of the proposed data collection and its outcomes, before committing a full approach across the entire portfolio. The pilot study should start on a small portion of the portfolio where all the activities within the proposed activities can be tested and fine-tuned. It needs to involve a representative cross section of the staff who will be ultimately involved and staff at all levels in the organisation. It should involve all stages of data collection, entry and audit. It should review outcomes, include the ability to produce meaningful reports and identify opportunities for improvement. T The methodology can then be fine-tuned or overhauled as necessary. The outcome of the pilot study will confirm the benefits and costs of the overall data capture project. Knowing the costs to implement the process across the whole portfolio may affect the final methodology. The strategy must then balance the budget with its ability to provide the necessary data confidence to satisfy the organisation’s needs. The benefits of a pilot study can be summarised as follows:

1. Ensures the appropriate level of detail is captured while achieving an immediate result. 2. Confidence that the chosen system can cope with the data, analysis and reporting

required. 3. The projected cost of the full data capture project is known and verified as affordable. 4. Obtain buy-in from the organisation.

The implementation of the data collection process then becomes a managed project once stakeholders have signed off the outcomes of the Pilot. (IPWEA 2009 P33)

Data Accuracy and Confidence Because of the human element in collecting and inputting data, unfortunately there will always be some degree of error potentially inherent. The fact that various assessors are used over time can add to that dilemma. In order to have confidence in the data being used, it is important to record the level of accuracy (confidence) in the data being input. Systems should have an inbuilt means of recording how the data has been collected, by whom, and the level of accuracy etc. for ongoing audit purposes. Whilst being collected, data accuracy should be monitored by random sampling and checking to ensure consistent results are being achieved. It also important that the processes employed are well documented so all involved are clear about what is expected and importantly, the same process can be applied in the future to ensure consistency over time. As detailed in the IIMM, there are some basic checks recommended to address data capture accuracy. These are as follows:

• audit: random audit of data accuracy (minimum 5% sample) at each stage of collection and entry;

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• connectivity: check network nodes are interconnected logically; and • logic: sort and print out data and look for abnormalities.

(IIMM 2015 p 2/71)

Reading 5.6 Refer to the Table 2.4.6 on Page 2/71 of the IIMM to see some examples of data accuracy and confidence grading systems in common use.

Specialised Services for Data Collection We alluded to this in the Introduction to this Topic, and this can be a very effective way of gathering specialised data for a particular asset class or for a particular asset management analysis task. These specialist services, because of their economy of scale, can employ specialised equipment and technicians that would otherwise not be available to most organisations. This can accordingly be a very cost effective way of gathering say condition data for a particular asset class, quickly and to a high degree of accuracy with targeted outcomes specified for the use of the specialised data so collected. Some examples of such specialised services include:

• Pavement condition surveys to feed into a pavement management system for optimising intervention works such as overlays, reseals, reconstruction etc. These might typically involve roughness measures to derive a pavement condition index and some structural assessment through deflection testing etc.

• CCTV services or Quickview type camera technology for pipeline condition assessment for buried assets such as sewer lines, stormwater drainage lines etc.

• Buildings condition assessment – certain firms specialise in providing inspection services and reports of building depreciated replacement cost, valuations, and maintenance plans derived from these condition assessments.

Activity 5.1 Undertake an internet search for some of these specialised service providers to see what is available in this field. Try searching for “cctv pipeline inspection” and go to the website for the arrb group at www.arrb.com.au and click on “road survey equipment” to see the variety of applications available. Reading 5.7 Refer to case study 2.34 in the. IIMM on p 2/73 for a good example of the use of specialised equipment for road pavement data collection surveys.

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New Assets Data Input (As Constructed) We have focussed much here on collecting data in relation to already existing assets. An area often overlooked or paid inadequate attention, is the recording of data on new assets as they are created (or donated) and ensuring this data is immediately included on asset registers and on other asset management systems. The major issue here is to collect all the necessary data for those assets the organisation may have built itself or acquired under contracts to build or purchase. In any developing community, there will also be a considerable amount of infrastructure created as part of that land development process and then “donated” to the Council for ongoing maintenance and asset management. It is important to obtain up to date “as constructed” information on all of these assets as soon as they are commissioned for use and ensuring all of the related data is translated into the mainstream systems of the organisation, for ongoing management purposes. There should be a consistent format or specification of all of the data that will be required so that developer’s consultants and surveyors can comply uniformly and have certainty about what a Council is requiring of them in terms of data to be submitted. There has been excellent work done over a number of years by a group of South East Queensland Councils who are in one of the highest growth areas in Australia, in developing a product referred to as ADAC (Asset Design and As Constructed) to address just such an issue.

The ADAC product is developed and maintained by a consortium of Local Government agencies in Queensland in conjunction with the Institute of Public Works Engineering Australasia - Queensland Division (IPWEAQ). The process is used to facilitate the collection and lodgement of detailed information on contributed civil infrastructure and associated assets provided by the private sector to Councils.

The consortium has developed a standard which defines: • the information required for each asset type; • the terminology to describe this information; and • allowable values.

From this, a program has been developed for the commonly used AutoCAD engineering software, to facilitate the collection of this information, and define the format in which the data is stored. The product is offered free of charge by participating ADAC Member Councils to their private sector partners involved in the property development industry to ensure a uniform approach is adopted. The product comprises a suite of AutoCAD drawing routines, relevant asset standards, documentation and product support that can be downloaded from the ADAC site following registration of customer details. The product can of course also be used by a Council for inputting data from their own projects.

Reading 5.8 Refer to the case study 2.33 in the IIMM on page 2/69 to see how one Council in south-east Queensland has been utilising ADAC to develop standards for the electronic receipt of “as constructed” data. Activity 5.2 Go to the ADAC website at www.engicom.com.au/adac to explore the potential and benefits of this resource.

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On Going Maintenance of the Data Once data has been collected and input, the systems will move into the full operational phase. All staff members and contractors will be responsible for the inputting of data in relation to their work activities. Data should be managed and maintained, with clear accountability given to an appropriate person for management and security of the information. With respect to long-term strategic planning, wherever possible, the same staff should be responsible for assessing the condition of assets, the remaining residual life and the rehabilitation or renewal work that could be required in the future. In any event these processes should all be well documented to ensure there is consistency over time even if other personnel are used. Once in operation, it is important to review the overall program and determine exactly what has been achieved, and to what level of sophistication and complexity the system has been implemented. It is through the operation of the system, using the data obtained in the initial data collection exercise, that validation of the quality and an appreciation of the functionality of this initial data can be gained. It is important to revisit the outcomes that were established at the outset as being the objectives to be met, to test and see what has actually been achieved. Another target is to ensure that the systems remain effective and relevant to the organisation, with regular reviews to determine the efficiency and effectiveness of the system. This can help to determine if any enhancements are required, to either the data that are available or to the computer systems or software systems themselves. This can ensure that they are more applicable to the needs of the workforce, the business units or the corporate organisation for these systems. This can be seen as a long-term role for staff involved in asset management. The currency of data is critical to effective asset management. The resourcing of the ongoing data management, including quality checking/auditing, should be recognised in future AM planning costs. (IIMM 2015 p 2/71 and 2/72)

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Summary In this topic, we have set out the challenge of managing the significant amounts of data that will typically be involved in the asset management process. The challenge is to utilise that data in a carefully planned and structured way to convert it into “knowledge” that will be of benefit to the organisation in the decision making processes. Also addressed is the use of the data for the necessary reporting of information to the many stakeholders that have an interest in how well the organisation is managing its infrastructure asset management responsibilities. We have seen how costly the data collection and management process can be in terms of dollars and human resources for the establishment of systems and their demand for data. Hence we need to re-iterate again the importance of establishing an information strategy at the outset so that the data gathering is driven purely by the business needs of the organisation. Now these business needs will of course include a range of requirements for reporting information as well. This topic explains the benefits of using a pilot program to test the proposed data collection exercise and importantly, its outcomes, before committing to the cost and effort of the full blown exercise. Another important concept is that of documenting the process for quality assurance purposes and to be able to audit the implementation. Planning of data collection with an eye to the future is also emphasised to allow for ease of transitioning data across into new upgraded systems as and when required. The importance of tapping into existing data to make sure it is fully utilised is highlighted, before embarking on collecting new data. The advent of new technology that has made the data collection and management process so much easier and more effective, has been covered. Particularly, GIS is mentioned, that aids geographic location of assets and makes asset numbering/identification so much simpler. Also the use of field computers/data loggers to improve efficiency and accuracy of data collection, is covered. Also addressed is the availability of specialist services to collect specific data sets for particular asset classes. Reference is made to the importance of collecting data on new assets as they are created or donated for input into the asset management systems as soon as they are commissioned for use. Again, resources available to assist in this process are identified. The value in starting with a simple core approach to better understand the data needs and what information can be derived before embarking on costly software for full asset management systems with all their attendant data demands, is again emphasised. Possible sources of data and possible ways to minimise the costs associated with data collection are addressed. Finally we looked at the important issues of ongoing maintenance of the data once it is in the various systems, the importance of maintaining currency of the data, securing it against unauthorised users possibly contaminating the data and setting up audit trails for such control. In the next topic. we continue with data collection by way of assessing the condition of assets as another critical factor in the required knowledge about the assets, to feed into the asset management planning process.

Asset Management Fundamentals: Topic 5 - Asset Knowledge - Data Collection and Management

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Review Questions and Sample Answer Summary 1. What are the issues that should be addressed as part of the initial planning of the data

collection process and explain the benefits of undertaking a pilot program? Answer Summary dot points; • Note that data collection will potentially be the biggest single demand on resources and

hence costs, for the organisation. Comment on the need to design the data collection based on a business case model with clear outcomes defined that accord with the business needs only and tailor the data collected to meet those outcomes. The main principles are;

o Collecting only the data that is absolutely needed (the correct data). o Do so as cost effectively as possible o Record the accuracy level of the data collected. o Ensure all of the necessary data is collected (completeness).

• As part of assessing the business needs of the organisation, consider also the hierarchy or structure of the data framework that needs to be set up, with due consideration of the following:

o What information will be required to manage the assets o Which assets are to be managed o Accuracy required of the data and information o Component level at which assets are required to be valued o Resources available to collect and manage data and information o What tactics are to be employed to achieve service delivery, e.g. outsourcing o What are the current key business drivers, e.g. maintaining service delivery in a

high growth environment and/or aging infrastructure o The hierarchy should include the capability of modelling networks and routes.

• Consider the various options that could be considered for data collection such as use of in-house staff, purpose engaged contract staff, external contractors, Specialist services, etc.

• Decide on the alternatives of manual hardcopy data collection forms being used to collect data with associated pitfalls of data entry errors and the inefficiencies of double handling data as well as being more time consuming compared to electronic options.

• Note that electronic data collection is the best approach to ensure cost and time efficiencies including data accuracy and ongoing data maintenance. These also allow an in-field Mobile application which is directly linked with software applications to provide a data collection tool that is integrated with analysis and reporting.

• Comment on the need as part of the planning phase, to weigh up issues of: o Time – consider the timeframe of the project knowing when data is required or

more importantly, when the resulting information and reports are needed. o Cost – consider the internal and external costs associated with setting up the data

capture project, assessing the assets, managing the data, maintaining the data and producing the required reports.

o Data Confidence – typically a subjective understanding of the data quality but can be quantitative, based on accuracy, completeness and reliability.

• Discuss the benefit of a pilot study which should start on a small portion of the portfolio where all the activities within the proposed activities can be tested and fine-tuned. It needs to involve a representative cross section of the staff who will be ultimately involved and staff at all levels in the organisation. Note that the outcome of the pilot

Asset Management Fundamentals: Topic 5 - Asset Knowledge - Data Collection and Management

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study should confirm the benefits and costs of the overall data capture project. It must demonstrate that the expected outcomes are in fact achieved.

The benefits of a pilot study can be summarised as follows: o Ensures the appropriate level of detail is captured while achieving an immediate

result. o Confidence that the chosen system can cope with the data, analysis and reporting

required. o The projected cost of the full data capture project is known and verified as

affordable. o Obtain buy-in from the organisation.

2. Outline the actual steps in a typical data collection project, how to prioritise what data is to be collected, possible sources of data and how it may be possible to minimise associated costs.

Answer Summary dot points;- • Outline the 5 main steps in the data collection process as follows:

o SCOPE the project first, which involves:  Define clear project objectives aligned to the business plan  Confirm final asset numbering system and register  Identify key issues  Identify criteria for selecting best process

o RESEARCH what is required of the project:  Identify user data needs relating to:

• - detail • - accuracy • - timing

 Identify existing sources of data and accuracy  Identify data collection opportunities  Identify data collection methods

o ANALYSE how the data collection best tackled:  Identify data collection and entry options  Assess optimal program and methods for data collection and entry  Assess resource needs and budgets  Evaluate options against the assessment criteria  Select preferred option

o TRIAL the proposed method by way of pilot program:  Set up and run a pilot program  Validate the analysis  Verify that objectives are met  Confirm preferred option or review as necessary  Confirm budgets and programs

o IMPLEMENT the selected solution:  Fully document data collection, entry, updating and audit processes

and standards  Secure budgets  Allocate responsibilities  Train staff  Collect and enter data  Ongoing audit and review of process, quality and program

Asset Management Fundamentals: Topic 5 - Asset Knowledge - Data Collection and Management

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o Additional data to be collected can be prioritised to make the process more manageable and cost effective, by only addressing those assets that are reaching the end of their effective life and will require further attention in the immediate future, e.g. 5 to 10 years. This additional data includes:  additional attribute or physical characteristics data  condition data  data required to enable adequate predictive modelling  performance data  data that may be required to choose the optimal renewal option.

• In deciding on sources of data, often there is a wealth of data held in existing documentation. Typical examples are historical records of design plans, specifications, cost estimates, dates of construction, “as constructed” field books and the like.

• The assets themselves, in the field, are another major source of data for condition assessments or undertaking modelling to determine deterioration profiles. Advances in technology are improving the cost effectiveness of gathering field data. Field computers allow rapid data input and uploading into the main asset register. The process applicable is:

o download a sub-set of the asset register applicable to the site or district o using pen-based or data loggers or map-based technology, update the asset

attributes, asset condition or maintenance work order o return data-set to main office (can be via telecommunications) o validate the data collected on-site o upload data to the main asset register and update all appropriate changes o update the audit trail during the up-load process.

• Comment on how costs can be saved by establishing programs that involve data

collection being done as part of normal routine maintenance and operational activities. Note that software solutions for large computerised asset management systems can make data management far easier, however, they can also be a trap in that they can demand significant amounts of data. Be aware of these issues and the resourcing/cost implications before committing to such.

3. What level of data detail should be collected and how do we go about ensuring the

appropriate level of accuracy is achieved? How do we assign a degree of confidence to the data collected and what are the issues regarding on-going maintenance of the data and its security?

Answer Summary dot points;- • Highlight the importance of collecting only the data that will be needed to support the

organisation’s business needs and decision making processes. Note that such will include some legislative reporting requirements, such as required by various Acts and drivers like the Accounting Standards. Government reporting requirements and reporting to customers/ratepayers.

• When deciding on the level of data to be collected, consider the following: o the purpose for which the data is required o availability of resources, e.g. skill levels, equipment o accessibility and quality of existing data o data management issues, e.g. data maintenance costs o data collection techniques

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o whether the extra detail will make a material difference. • When considering the business needs, consider the following for what data to collect,

how much and accuracy etc.: o valuation data o maintenance (planned and unplanned) management systems needs. o asset accounting outputs and reporting needs. o Condition and performance monitoring o prediction modelling o risk management o lifecycle management data o optimised decision support systems o GIS input.

• In order to have confidence in the data being used, it is important to record the level of accuracy (confidence) in the data being input. Systems should have an inbuilt means of recording how the data has been collected, by whom, and the level of accuracy etc. for ongoing audit purposes. Whilst being collected, data accuracy should be monitored by random sampling and checking to ensure consistent results are being achieved. It also important that the processes employed are well documented so all involved are clear about what is expected and importantly, the same process can be applied in the future to ensure consistency over time. A grading system of 1 to 5 can be used to record accuracy of the data.

• On-going maintenance of the system is about ensuring that the systems remain effective and relevant to the organisation, with regular reviews to determine its efficiency and effectiveness. This can drive any enhancements to either the data that are available or to the computer systems or software systems themselves. This can ensure that they are more applicable to the needs of the workforce, the business units or the corporate organisation for these systems. This can be seen as a long- term role for staff involved in asset management.

• The currency and security of data is critical to effective asset management. The resourcing of the ongoing data management, including quality checking/auditing, should be recognised in future AM planning costs.

4. Comment on the possible use of specialist services for the collection of data and

possible benefits of such. Also comment on the importance of collecting data on new assets as they are commissioned and means of doing so. Answer Summary dot points; • Specialist services, because of their economy of scale in providing service to many

organisations, can employ specialised equipment and technicians that would otherwise not be available to most individual organisations.

• This can accordingly be a very cost effective way of gathering say condition data for a particular asset class, quickly and to a high degree of accuracy with targeted outcomes specified for the use of the specialised data so collected.

• Some examples of such specialised services include: o Pavement condition surveys to feed into a pavement management system for

optimising intervention works. o CCTV services for pipeline condition assessment for buried assets such as

sewer lines, stormwater drainage lines etc.

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o Buildings condition assessment – certain firms specialise in providing inspection services and reports of building condition assessment, depreciated replacement cost, valuations, and maintenance plans.

• For new assets, the need is to collect all the necessary data for those assets the organisation may have built itself or acquired under contracts or have had donated under development, for ongoing maintenance and asset management.

• It is important to obtain up to date “as constructed” information on all of these assets as soon as they are commissioned for use and ensuring all of the related data is translated into the mainstream systems of the organisation, for ongoing asset management purposes.

• There should be a consistent format or specification of all of the data that will be required so that developer’s consultants and surveyors can comply uniformly and have certainty about what a Council is requiring of them in terms of data to be submitted.

• Comment on systems such as ADAC as an already developed tool that provides such a specification.

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References ADAC 2011, “As Designed and As Constructed” IPWEAQ www.engicom.com.au/adac. ARRB 2009, “Road Survey Equipment” www.arrb.com.au IPWEA 2009, “Building Condition and Performance Assessment Guidelines” Practice Note 3

Institute of Public Works Engineering Australia, Sydney, 2009. IPWEA (2015) International Infrastructure Management Manual V 5.0 Readings Reading 5.1 Refer to Table 2.4.1 and case study 2.28 in the IIMM pages 2/56 and 2/57 for

examples of typical data requirements that one would expect to find in an asset management system depending on its level of maturity. Again, these requirements need to be tempered by determining what the organisation needs to meet its particular objectives. Case Study 29 provides an example of this.

Refer to case study 2.29 on page 2/50 of the IIMM for an example of a risk based approach to setting priorities for data collection

Reading 5.2 Refer to Figure 2.4.2 of the IIMM on p 2/52 for an example of possible level of detail for data collection requirements to satisfy a variety of parameters, what each of these typically address and the data fields that one would expect to see data collection for each.

Reading 5.3 Refer to Section 2.4.6 of the IIMM pp 2/63-65 to see a number of examples of the

way in which various asset types can be broken down into a number of discrete asset headings with a number of service areas for each that can then be further broken down into various components.

Reading 5.4 Refer to case study 2.31 of the IIMM on page 2/67 for an example of developing

a business case for data collection. Reading 5.5 Refer to Table 2.4.4 of the IIMM on p 2/67 for some examples of typical sources

of data and to Case Study 2.32 on p 2/68 of the IIMM to review how to use existing data, to improve asset management knowledge.

Reading 5.6 Refer to the Table 2.4.6 on Page 2/71 of the IIMM to see some examples of data

accuracy and confidence grading systems in common use. Reading 5.7 Refer to case study 2.34 in the. IIMM on p 2/73 for a good example of the use of

specialised equipment for road pavement data collection surveys.

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Reading 5.8 Refer to the case study 2.33 in the IIMM on page 2/69 to see how one Council in south-east Queensland has been utilising ADAC to develop standards for the electronic receipt of “as constructed” data.

Reading 5.9 Refer to case study 35 in the IIMM on page 2/61 for an example of how one organisation has documented data capture processes to maintain data quality

Activities Activity 5.1 Undertake an internet search for some of these specialised service providers to

see what is available in this field. Try searching for “cctv pipeline inspection” and go to the website for the arrb group at www.arrb.com.au and click on “road survey equipment” to see the variety of applications available.

Activity 5.2 Go to the ADAC website at www.engicom.com.au/adac to explore the potential

and benefits of this resource.