Focus collection efforts on data that supports operational and strategic decisions
Collecting information is expensive. All too often organizations expend a great deal of effort collecting data that is never used. Even worse, despite significant efforts, organizations find they may not have the information they actually need to make effective decisions. It is important to recognize the differences in data requirements between project and strategic levels. Project level decisions require granular data, whereas strategic decisions can be made with more generalized data. An organization with detailed measurements of dimensions and materials testing reports, but lacking the assets’ year constructed may find it difficult to forecast rehabilitation or replacement activities at a high level. Another organization with detailed age and maintenance rehabilitation information may find it easy to forecast their future rehabilitation and replacement needs, but find more difficulty in selecting projects. The International Infrastructure Management Manual (IIMM) recommends prioritizing initial data collection efforts on data that supports strategic level decision making.
Assign a static unique identifier to each of your assets
Although seemingly innocuous, an asset identifier is a fundamental building block of an asset register. The term ‘static’ is key and is what can accommodate potentially unforeseen uses, such as asset register growth, inclusion of other departments’ assets, linking to other systems, file management, and others.
Imagine the example of a city public works organization. Over the years, each department developed an asset register using the next available row number as the asset identifier. As use within each department grew, ancillary databases, spreadsheets, and files were made to store condition assessments, photographs, and maintenance history. As software changed over time, database upgrades and newer file formats updated row numbers by re-indexing. All of the related references in other databases, spreadsheets, and files also needed to be updated for consistency.
Another example of the potential pitfalls becomes clear when implementing an enterprise asset management system, where multiple departments’ assets are stored in a single database. Departments that use the same numbering format will be required to update asset identifiers to be unique. Again, any related references to other databases, spreadsheets, and files also need updating. A carefully considered asset identification scheme ensures that your asset register can accommodate expansion, persist linkages to other systems and processes, and withstand changes in technology. One recommendation is to use an asset category or class followed by a sequential number. For example, the street department may prefix all road assets with RDS, while the storm water department may use STW.
Know the core attributes that support asset management materials/type, location, condition, age, criticality, useful life, and economic value
The IIMM recommends a complete asset register including the data elements above as the initial priority. There are likely many sources of existing data; the challenge is often locating these sources and reconciling them to be logically consistent. These attributes support several strategic summaries and inferences critical to asset management. Age, useful life, and economic values can be used to estimate future budget needs as older assets require replacement or rehabilitation. Budget forecasts identify periods with large asset replacement or repair requirements as assets constructed in the same era reach the end of their lives simultaneously. The effect of these steep rises in budget needs are critical to communicating budget needs to stakeholders. Condition and criticality support strategic prioritization for project selection and type and location can be used to begin to form tactical level project plans. As organizations mature, additional data collection efforts can be added to refine decision making. Other data elements may include asset utilization, performance, capacity, data accuracy, and others. These support more granular level decision making and prioritization.
Streamline data collection using mobile devices and laptops
Collecting information in paper forms often doubles the effort of data collection, as it then gets transcribed into digital systems. Furthermore, transcription errors, non-standardized values, and lack of control over the data collected can present even greater risk and cost to organizations.
In the past, mobile devices were bulky and harder to use. As mobile technology has matured, nearly everyone has a smart phone in their pocket and can use a basic “app” with little or no instruction. Tablets are also blurring the lines between laptop and mobile device, adding another arrow to the digital quiver. While anecdotal evidence about efficiency gains is strong, several government studies have verified in documented, unbiased forums. Combined with extensive wireless networks, mobile devices speed up the collection of data as well as the speed at which that information is made available to decision makers allows for more nimble action. The benefit of correcting issues sooner, or preventing them in the first place, is a significant advantage over traditional paper-based data collection.
Choose realistic update frequencies based on budget constraints
Data has a half-life. As it ages, it is less likely to be an accurate assessment. On the other hand, collecting condition data can be expensive. In the absence of legislated inspection regimes, it can be difficult to find the right mix of timely information within budgetary means. Understanding the true cost of collecting condition data and weighing this against the benefit or more accurate forecasts is one way to begin exploring the right update cycle. Organizations that have identified criticality ratings can use these as a means to focus efforts where they’re most needed.
Ensure the data you collect, including contracted data, integrates seamlessly with enterprise information systems
All too often, organizations pay for a large data collection effort, then spend months processing the data to import it into the enterprise information systems where it’s used. Even worse, after such an expense, some agencies find what they paid for is at best a silo that can be used for a limited time and won’t be compatible with other systems, or even comparable to later vintages. Though tedious, established and documented data standards and policies can pay large dividends when organizing internal data collection efforts. Organizations with data standards can also easily add these to contract specifications and require compliance. This reduces or eliminates the need to spend extra effort processing data into a useful format.
Promote data transparency to stakeholders both inside and outside of your organization
There is often a fear that sharing information can cause unnecessary scrutiny and may open the gates to public requests that will result in a barrage of immaterial complaints. While this is certainly possible, there is a growing trend for organizations to be more transparent and responsive to citizen input.
Baltimore’s CitiStat is a widely recognized for its success in transparency through the use of data. One of the highlights from CitiStat was the implementation of a 311 non-emergency number to report potholes. Implementing this system and tracking the response time, the city was able to guarantee repairs within 48 hours. Since implementing the system, the number of reported potholes in Baltimore has actually decreased. The lesson from Baltimore’s case is that engaging with constituents can actually change the dynamic to be more constructive, focus needed attention, and generate trust.
The NAMS Group (NZ). International Infrastructure Management Manual (IIMM). Version 3 – 2006 (International Edition). Association of Local Government Engineering NZ Inc (INGENIUM) and the National Asset Management Steering (NAMS) Group. New Zealand. ISBN No: 0-473-10685-X.