All too often, asset intensive organizations spend a great deal of time and effort collecting data that is never used, or, even worse, they don’t collect the data they need to make effective decisions. Not all data is created equal. Sometimes, your collected data will permit you to see your organization’s efficacy, while other times you can use your data to create financial decisions.
If used effectively, your holistic data will allow you to optimize your processes while also helping you to make educated predictions about the future of the financial or personnel resource situations in your organization. Going forward, it is imperative that you establish the goal of your data collection at the outset.
Focus data collection efforts
The amount of data that you should collect and analyze will differ depending on what team is utilizing the data. Certainly, you don’t want to put excessive work on your team members to collect data that won’t be used or relevant to your organization. What can you do with the data you are collecting? Is there more data you need to collect, or less?
Once you identify the primary goal of collecting your data, you need to ensure that everything you collect is accurate and usable. It’s easy, even enticing, to take shortcuts in data collection, but those can be disastrous for your organization. Wrong data can indicate an improper asset replacement time, overpayments for projects or repairs or unnecessary downtime for public-facing assets.
As such, an enterprise automated data collection tool is the ideal resource for ensuring data accuracy and integrity. It reduces the error potential of human input, and also allows for digital trend recognition.
Do you know the differences between the data required for projects and data required for making strategic decisions? Project level decisions typically require granular data, while strategic decisions are made with more generalized data.
Example
Organization 1 keeps track of detailed measurements of dimensions and materials testing reports, but they fail to accurately track assets’ year of construction or procurement. While they collect an impressive amount of data, the data they lack will make it difficult to forecast replacement activities at a high level.
Organization 2 collects detailed age and maintenance data, so they find it easy to forecast their future replacement needs, but the data they lack makes it more difficult to select projects.
Both organizations can learn from each other. While Organization 1 collects detailed data on their assets, they overlook assets’ construction date. This can lead to ineffective maintenance and replacement schedules. Organization 2 tracks all maintenance and replacement data, but overlooks more in-depth asset data. This can lead to poor project selection in the future.
Today, all asset intensive organizations need to focus data collection efforts that support all levels of strategic decision-making. Take a look at the data you’re currently collecting: does it support your
organization in the long-term?
Understand core asset attributes
When developing an asset register, understanding all asset attributes should be a main priority. These attributes include:
- Asset Materials/Type
- Location
- Condition
- Age
- Criticality
- Useful Life
- Economic Value
There are many sources of existing data, so the challenge for most organizations is locating these sources and reconciling them for logical consistency.
The above attributes support several strategic summaries and inferences critical to modern asset management.
Age, useful life and economic value are used to estimate future budgetary needs, since older assets require replacement or rehabilitation. Budget forecasts identify periods with large asset replacement or maintenance requirements, as assets constructed around the same time reach end-of-life concurrently.
Asset condition and criticality support strategic prioritization for project selection, and asset type and location data are used to begin forming tactical level project plans.
As your organization matures, additional asset attributes can be added to refine decision-making even more. These additional attributes include asset utilization, data accuracy, capacity, performance and more. These attributes support more granular level decision-making and prioritization.
Streamline data collection with mobile technology
Collecting data on paper typically doubles the workload for staff, for it eventually requires transcription into digital systems. Manual data collection and entry also results in transcription errors, non-standardized values and an overall lack of control over the data. This presents great risks and costs to organizations.
Just a few short years ago, mobile technology for data collection was bulky and hard to use. Today, almost everyone owns a smartphone and can use basic apps with ease. Combined with extensive wireless networks, mobile devices speed up data collection, as well as the speed at which data is made available to key decision-makers within your organization. For example, AssetWorks Enterprise Asset Management (EAM) software allows for real-time integration with mobile modules, so issues reported in your community reach your maintenance staff quicker. This allows for correcting issues within your community sooner, or preventing them in the first place. This speed is a significant improvement over more traditional, paper-based data collection efforts.
Ensure data integration with enterprise asset management systems
All too often, organizations today pay for an impressive data collection effort, then spend months processing the data to import it into their enterprise asset management system. Even worse, after such an expense, some agencies find that the data collection they paid for isn’t even compatible with their enterprise system.
While it may seem tedious, established and documented data standards/policies can pay large dividends when organizing internal data collection efforts. Asset intensive organizations with set data standards can easily add these to contract specifications when investing in enterprise asset management software. This eliminates the need to spend extra efforts processing data into a useable format.
Using data to predict
Once you have accurate data and a clear utilization identified, can you see this data used for predictive budgeting or decision-making? If you can, you have achieved a high level of data science that you should be proud of! This can be highly effective for helping your organization in the long-run to predict possible scenarios with your assets and capital projects.
For example, what if you could compare levels of resource dependency and utilization to determine the effects of maintenance efforts before you had to enact them? What if you could enlighten your management and financial teams to help them see how funding or repairs would impact your community’s outlined initiatives?
In the modern world, public works directors, utility directors and anyone involved with replacing and maintaining assets can use powerful software like AssetWorks Enterprise Asset Management (EAM). For over 40 years, AssetWorks has provided software solutions that manage complex assets and infrastructure. Its mission is simple: to provide innovative and practical solutions to help organizations and the people they serve.
EAM handles the day-to-day tasks of modern asset intensive organizations, like work order management and real-time labor tracking for preventive maintenance. EAM can also manage inspection recording and future planning, like complete life-cycle cost analyses and capital budgeting.
