Thought Leadership

Don't kill your CMMS ROI with bad data: here's why quality is vital

One of the top reasons for a poorly implemented CMMS is bad data quality. Learn how to make the most of your CMMS investment.

The computerized maintenance management system (CMMS) market is growing quickly, with 2023 revenue predicted to be $1.6 billion and future expansion expected at a CAGR of 9.1% to $4.2 billion by 2033. Many companies are moving away from paper tickets, sticky notes, and spreadsheets to track asset maintenance and performance better in CMMS software.

While this is good news, many CMMS implementations fail to set up customers to achieve the ROI they expect. Some get suboptimal results, others are not even scratching the surface. There are many reasons for these gaps, but the most common is bad data.

Why is Data Critical in the CMMS Equation?

CMMS is a “garbage in/garbage out” kind of product, it’s only as good as the data in the system. You can get the best CMMS out there, but if is poor, you are not going to get the return you expect. Why?

  • Bad data = bad decisions
    A CMMS is used to schedule and track work orders and inspections, manage part inventories, and optimize asset performance. It’s used to decide: What maintenance do I plan for next month? When do I order spare parts? When do we prepare for third parties on site? With bad data, you are going to order the wrong spare parts, order them too late, or assign the wrong person to replace the part.
  • Poor data causes inefficiencies
    Unreliable data can lead to ineffective maintenance management. Over-maintaining assets wastes time, money, and spare parts, while under-maintaining machines increases the risk of downtime and shortens equipment lifespan.
  • Poor data causes unplanned downtime
    Faulty data leads to unreliable asset performance and increases the likelihood of equipment downtime. This can be extremely costly and risky as unplanned downtime can result in financial losses, employee injuries, and loss of customers due to frustration or dissatisfaction.

In the end, poor data quality defeats the purpose of investing in a CMMS. The goal of a CMMS is to enhance maintenance operations and achieve better ROI on asset performance. But with poor data, these objectives become difficult to achieve.

How Does Poor Data End Up In a CMMS?

To understand how bad data ends up in a CMMS, it's essential to understand the data entry points. Let’s look at a few scenarios.

Manual data generation and input

A big part of setting up a new computerized maintenance management system is producing maintenance plans and spare part lists. These are typically contained in equipment manuals supplied by the manufacturer. Getting data from these manuals into the system takes months of manual data entry.

Manual entry is the main source of poor data quality. We’ve heard Makini customers tell us about flying paper manuals to a data entry team who then spent months punching the data directly into the new CMMS! Inevitably, humans make mistakes.

According to CompareSoft’s CMMS Market Insights Report, nearly 28% of organizations seeking a new CMMS have no maintenance management system at all. This means that almost a third of implementations will be done from scratch with manual data generation.

Data migration from another system

Some facilities are already using one or multiple CMMS and are seeking to upgrade or consolidate into a unified system. This requires migrating equipment data, which is a complex and time-consuming process that may result in errors and inconsistencies too.

Out of organizations seeking new CMMS’, 14% use a bespoke internal system, 21% have an existing CMMS, and 9% rely on a combination of spreadsheets and an existing CMMS. All of these businesses are exposed to the risk of contaminating existing data during the migration process.

Data flow from other integrated software

By definition, a CMMS collects data from other operational and ERP systems involved in the asset maintenance ecosystem. These systems perform service request management, cost and budget management, purchase orders management, and so on. Inaccurate or incomplete data in these source systems can flow into a CMMS and lead to similar issues in those systems.

So what's a CMMS team to do?

Data quality problems from initial input of manufacturer manuals, cross-system migrations, or poor system integration must be identified and resolved within each channel. 

For example, errors due to manual data entry can be resolved through automation or direct access to manufacturer data, along with automated intelligence checks and flags to spot issues.

Errors due to data flowing from other integrated software can be avoided through reconciliation and correction between systems, and digging deeper into the data source of each of these systems.

Regardless of the source of data quality problems, system operators and project managers need automated solutions, or they will find themselves repeatedly fixing the same data problems.

Automate OEM Data Generation and Migration to Improve Data Quality

If you want to have quality data and a successful CMMS rollout, good process is foundational for data generation or data migration  Unfortunately, doing it well can take months and cost more than the CMMS software license itself! Automation and the tech are keys here.

Makini’s Integrated OEM LibraryTM automates the generation of maintenance plans and spare part lists right out of our extensive manufacturer libraries and pushes it into systems like UpKeep, IBM Maximo, SAP S/4HANA, and hundreds of others. All you need is an equipment list. This ensures data quality, saves months, and thousands of dollars in expense. Global industrial clients also migrate data from their older bespoke or outdated CMMS to a newer CMMS with Makini - without any compromise to data accuracy or integrity. Makini can also migrate existing CMMS' onto new deployments of UpKeep, IBM Maximo or hundreds of other CMMS or EAM platforms.

Feel free to contact us to learn more about how we can help you make the most of your CMMS investment.

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