6 Steps to Building an EAM Analytics Program for Maintenance and Asset Management

A thorough and well-designed analytics program holds many benefits for maintenance and asset management. The precise benefits sought vary from industry to industry and organization, but some of the most common include identifying bad actors, detecting exceptions, and enhancing transparent communications. Analytics in asset management are often used to drive continuous improvement as well.

Accessing any of these benefits relies on having an EAM analytics program in place – and not just any analytics program. If you want to get the right benefits from it, your analytics program must be suited to your needs.

In this blog, we’ll go over the basics of how to build an analytics program for your maintenance organization. This guide is more of a starting point in your analytics journey than a complete how-to that will enable you to build an analytics strategy overnight.  

At its root, it’s powered by mathematics and algorithms that usually require years of education to fully grasp. The impacts of analytics are enormously far reaching, with the potential to transform every corner of the operation. For example, a properly executed analytics program can serve as an RCM tool, increasing the effectiveness of your reliability centered maintenance.

Building an analytics strategy isn’t a job that can be done alone or quickly. You will certainly need buy-in from management, maintenance, and likely IT as well.

You may find that you need to loop back to earlier steps to refine your ideas. Each section builds on the previous one, but the steps below should be treated as signposts rather than a direct, one-way path.

1. Determine the Goals of Your Analytics and Maintenance KPIs

First, you must outline why you want insight into your analytics and what problems the information will help you solve. You must also decide what the long-term goals of these efforts will be, and make sure they’re in alignment with the overall goals of the organization.

Goals in perfect alignment with the organization’s overall mission and vision are more likely to be achieved and will also help encourage buy-in from other stakeholders.

Be as precise as you can when setting these goals. “Reduce failures” is a noble goal, but “reduce failures by 50 percent within one year” is more precise.

Other than making sure your goals are in alignment with the organization, there are no fixed rules for what makes a good goal. Even something as benign sounding as “Reduce unintended outages by 25 percent next quarter to increase production” is not a good goal if the company doesn’t want or require more production next quarter.

Further reading:

      • How Well Do You Understand the Key Metrics for Reliability and Maintenance?

      • Maintenance KPIs: Let Your Goals Guide Your Choices

2. Get Buy-In from Stakeholders

Ensuring that all stakeholders agree that maintenance analytics would provide value is an essential first step. Your organization almost certainly has rules in place that must be followed before initiating any project that will require capital or labor.

Even in the unlikely event that you’re allowed to run the maintenance department as your own personal fiefdom, we would still recommend discussing the idea with senior management and the IT department. Nobody likes being blindsided. Plus, members of those two groups can be your strongest allies when it comes to implementing an analytics solution.

How you present the idea will likely mean the difference between success and failure. It is important to fine-tune your pitch to the interests of the diverse groups that you’re speaking with. Spend some time doing your research and put together a short, compelling presentation on why your organization needs maintenance analytics.

A list of maintenance department woes may elicit sympathy from your colleagues, but it is unlikely to move them to action. Whenever possible, show how maintenance analytics will help them personally. When that’s not possible, you must show how it will help the organization as a whole. Note that for senior executives, these may be the same thing. For more information on exactly how to do this, please see Selling Maintenance Software to Your Executive Team.

3. Is It Better to Buy or Build an EAM Analytics Solution?

Technician reviewing EAM analytics on a mobile device

Can your IT department build the solution that you require, one that will satisfy all your analytics goals? Probably, if they’re given the budget to do so. Whether or not they should is a very different question. Your in-house IT resources might be better spent on other projects. There’s also the question of whether they have any concrete experience with building analytics solutions.

An analytics solution that is built by your IT department has the advantage of being built to your exact specifications. However, this inherent customization can come with its own downside. Your needs may change, and the IT department may not have the resources to adapt the solution they’ve built. We come down very heavily on the “buy” side of this debate. A solution used by thousands of people in diverse industries will be more robust, useful, and flexible than a custom-built solution. Users have given these solutions the ultimate test by using them in the field, day after day and year after year. In this time, the solutions have only improved and become both more flexible and easier to use.

This question is not simply about the software you use to wrangle or display the data. You will need some form of data warehouse, and you may need a master data solution as well. Your organization may already have a data warehouse. In this case, it’s wise to consider a data mart as well. A data warehouse holds all the information across an enterprise. The data mart, however, is a part of the data warehouse dedicated to a department, line, or team. It may be wise to install a maintenance data mart if possible, thereby sequestering the data needed for maintenance analytics.

Goals may be altered to line up with what IT can do/what your favorite software provides, and you will certainly need buy-in before making any purchases or giving the IT department a gigantic pile of new work.

In the end, there are advantages and disadvantages to both sides of the debate. You must choose which path is best for you to follow. Prometheus Group’s reporting and analytics solutions come with built-in reports and maintenance KPIs, can be easily configured to your exact needs, and offer seamless integration with your EAM, ERP, or CMMS.

4. Clean and Organize Your Data

In general, the more data you have, the better. This may be true even if the data is of relatively low quality. However, the best analytics are drawn from substantial amounts of data that are both high quality and complete.

Data cleaning can be done manually, but it is more common to use scripts. Long story short, if the amount of data is small enough that you can clean it up manually, it may not be enough to provide confident answers through analytics.

The exact cleansing methods will depend on where most of the errors are thought to reside, but some of the more common fixes include removing extra spaces, removing duplicates, spell checking, and changing the text case to upper or lower, as appropriate.

Having clean master data allows you to better track your metrics. Prometheus Master Data-as-a-Service guarantees you’re operating on clean data and a standardized taxonomy. Cleansing and sustainment of the data are offered as a subscription service, ensuring the data stays clean.

5. Ensure the Data Stays Clean

Technician checking warehouse inventory data on a mobile device

Once the data is clean, you must keep it that way. In addition to historical data, you’ll want to track and analyze the new, incoming data as you go forward. Every day, work orders are being executed, equipment is inspected, the results logged, and assets are generating their own operating and sensor recordings. Your data had errors before. How do you ensure the newly generated data is error free?

The short answer is that there is no guarantee. The longer answer is that you must put safeguards in place to help ensure the data entered is accurate and can be understood by your analytics solution.

One thing that can help is to design the entry fields in the database in such a way that they simply will not accept data that doesn’t meet its parameters, or give it rules on how to convert the data to the right format.

For example, let’s say that every asset in your organization has a six-digit code that signifies that asset and that asset alone. You could easily set up the system so that it rejects any code that has more or fewer than six digits.

Let’s further propose that not only does each asset have its own six-digit code, but that each character in that sequence has a precise meaning. For example, say the very first digit tells you the location within your organization (0 for Site A, 1 for Site B, and so on). It is possible to build your solution so that it recognizes this and will spit back any code that has the wrong facility number. These are of course only examples. Exactly how it is setup is up to you and your organization, but it’s important to consider all the possible ways someone could enter incorrect data, and then try to prevent those instances from occurring.

Note that these safeguards depend on data being entered in the system in the first place. Empowering teams with the tools to hold themselves accountable is critical if you find you’re struggling with inputting accurate data and maintaining data accuracy.

Mobility is one of the best tools that you can provide to help with this.  Everyone in your department likely sees the value of maintenance data; what typically holds them back is the time and effort involved in manual data entry. Replacing a paper system with a mobile solution makes this much faster and easier, and all data can be entered as it’s recorded, rather than hours or days later.

6. Refine the Processes and Goals of Your Analytics

Now you will start to see the results of all your work. At this point, the most important thing to do is to continue to refine your analytics so they stay true to your overall goals.

Don’t be afraid to change your strategy as you proceed. After a few months of tracking certain analytics, you may determine that the information being captured is not leading to the actions you wanted to see. It’s possible that this is a personnel problem, but you must be willing to consider that the analytics being captured aren’t the right ones needed to drive those behaviors.

In some ways, you can treat your analytics as you would any maintenance project. The goals must be defined, approval and buy-in arranged, the program rolled out, and finally, the program subjected to continuous improvement to ensure that it achieves the best results.  

Prometheus Group has Reporting & Analytics solutions and expertise to help you achieve the best results with maintenance analytics. Contact us for more information on how we can help you unlock the power of your data.