Top 3 Reasons “Bad” Data Can Kill Your Business

4 minutes

I was driving down the highway, headed to the airport for an overdue vacation (postponed from the early pandemic), when the “oil change needed” message displayed on my car’s dashboard. The car was running fine, so I continued on my way for a much-needed break.

I realized the importance of this little alarm because it signaled to change my oil soon, when I returned from my trip. Still, I thought, what if cars did not have this warning? There’d be no indication of a problem until it was too late and the engine was in much greater distress — possibly forcing me to stop the car and miss my flight. In the meantime, the car was fine, and I was free to enjoy my vacation.

Like a warning light, data is equally essential for our maintenance operations. It keeps us informed, helps us make better decisions and ultimately enables us to run better operations. Computerized Maintenance Management Systems (CMMS) automate a variety of maintenance activities, including:

  • work order management
  • spare parts
  • tracking
  • improvements to assets/facilities, safety and other tasks

Each area creates data that helps to improve current and future maintenance operations and decision-making. But not all data is created equal. The three critical factors of data quality, quantity and timeliness may mean the difference between the success and failure of our business.


Lack of quality data hurts decision making

Without quality data, speed and quantity are meaningless – you can’t make good decisions if that data is not valid and trustworthy. One of the best ways to improve data-driven decisions is to ensure that your work orders get updated with quality information. For CMMS, much of the analytics starts with work order data, which is typically at the heart of our maintenance operations.

When I review CMMS data, I often find work orders with inadequate data input that look like this:


Description automatically generated

You might have hundreds, thousands or even tens of thousands of work orders in your operations. But imagine the immense improvement in reviving analysis from these work orders if they looked more like what’s below!


Description automatically generated

These four fields are just the start to improving your operations. Better quality starts by simply ensuring that good data entry — capturing as much data as possible without overburdening the team — is performed. This requires solid training, standard best practices and regular enforcement. The payoff is worth it; the quality of your CMMS data goes up dramatically, and you can make better decisions. Without quality data, your decision-making ability, and your business, can suffer.



Description automatically generated with medium confidence

Figure above shows the effects of downtime across assets.

Lack of data quantity limits analysis

Maintenance operations improve with data-driven decisions from 1) a variety of sources, and 2) over historical time frames and sources. Often, there is a debate as to whether it makes sense to add a work order to your CMMS. Some arguments might be, “it is so minor, and I already fixed it; why bother?”. In other cases, the team is overwhelmed and can’t find time to go back and enter a work order after the fact.

For me, the best test is: if we looked back at the history of that asset, would that work order help explain anything?

Small, quick issues might be an excellent indicator of how a fix of this size in retrospect set the positive course for that asset’s future — a lesson that can benefit similar assets and situations. In my opinion, this is a subtle yet highly-valuable bit of information that should be part of that asset’s history.

Not taking time to enter major maintenance issues is a bad habit and should be avoided at all costs. It’s comparable to your doctor not documenting a major surgery or illness in your medical records — how can you expect to be properly evaluated in the future?

I understand that paperwork can be a pain, but the payoff is worth it. Having a complete record with enough information gives the maintenance team an excellent resource to see what’s been done historically, which is especially helpful in diagnosing current issues. Plus, that data is critical in showing trends and analytics that can prevent future issues.


Description automatically generated

Figure above showing how IoT technology data can help illustrate trends in your assets.


Lack of data timeliness slows response

How quickly you retrieve data can streamline response and operational improvements. Some examples:

  • Electronically entered work requests can appear on work orders faster. This is superior to paper forms, emails that need to be re-entered, or worse yet, ignored issues.
  • Using your CMMS's ability to move work orders through the approval, notification and escalation process in an intelligent and dynamic manner will shorten response and ultimately find the best person/contractor to work the issue.
  • Using modern technology such as the Internet of Things (IoT) to electronically have your assets available to inform maintenance of issues can be a fantastic way to get a more timely response.

“Mean Time to Response” (MTtR) is an excellent metric to measure how long it takes from when an issue is detected to how quickly maintenance can respond. The shorter the time, the quicker the fix and the less of an impact.


Graphical user interface, application, website

Description automatically generatedA screenshot of a clock

Description automatically generated with low confidence

Figures above show an example of MTtR in our Asset Essentials product dashboard.