If you have found yourself doubting the results of a report of query generated by your CMMS, then it is important to look into the possible reasons for the given results. These usually range from pressing the wrong key to misreading the numbers. The absence of certain information or having unrealistic expectations may also contribute to the outcome of your CMMS report. Generally, in most cases the quality of data is usually the main culprit. If this is the reason for your case, then you need to reconsider the worth of your CMMS.

When you have a CMMS in place, then your operations will be dependent on the information that is provided by the system, such as asset availability, budget variances, energy consumption, performance, work backlog and payroll hours, among others. As such, data quality must be uncompromised to make the right decisions and ensure smooth operations. Even then, maintenance managers have over the years discovered that the quality of data entered into the CMMS may be lacking.

Here is some insight on data quality and how it can be manipulated:

Definition of Data Quality

Overall, data quality refers to the information that satisfies the expectations of the consumers as well as the source. Various terms have been put forward in an attempt to describe data quality. Below are some of these terms:

• Integrity is a guarantee that data is preserved from its source to the intended consumer without any form of distortion either from malicious or accidental intervention. For instance, when the receiver of parts scans a barcode that is read incorrectly due to damage on the scanner, hacking the CMMS and falsifying the number of spare parts that was received.

• Validity confirms that the data actually conforms to the rules of business like range, logic and format. A good example is when a meter reading is entered to determine whether a PM is due, then it must have four numeric digits of greater value than what was last entered but must not exceed a 1,000 miles.

• Accuracy is the degree of correctness as well as conformity of the data to the standard given. For instance, a technician working on a job should not take more than 3 hours, so when he enters 5 hours for the work, it may be deliberate or due to human error.

• Precision is the level granularity of the data. That is, when maintenance inspection is performed and requires measuring the quantity of oil left and the technician records ½ as opposed to 0.4532.

• Credibility is where the data is reasonable and believable. For example, when technicians possess varying levels of diagnostic capability and experience in determination of the root cause of failure, then data gathered from one source may be considered more credible than another source.

• Timeliness on the other hand holds that data may be perfect, but it is of no value if it is not current or is not delivered at the right time. For example, when information is entered at the end of the week as opposed to at the beginning of each job, then the history reports that are generated in the course of the week will not be a true reflection of the reality.

• Completeness becomes an issue when users decide to enter less data or use fewer fields in the CMMS with the view of cutting costs or saving time, but end up compromising on decision-making ability.

• Conciseness and higher data quality means greater brevity as well as being succinct when entering data.

• Redundancy refers to making numerous entries of the same data and this is common when dealing with a large database.

• Consistency is the level of repeatability of data. That is, can a person enter the same data under different circumstances in future?

• Objectivity means biases may be introduced in the data during the process of entering, approving and manipulating CMMS data, effectively generating distortions that are recognizable.

• Utility refers to the usefulness and applicability of the data that is collected. Generally, not all reports have the same applicability and utility.

• Accessibility means the CMMS data needs to be available and is easily obtainable by those who are authorized to retrieve it.

• Usability is a crucial aspect of data quality that refers to ease of use as well as intuitiveness of the data that includes ease of remembering and learning.

• Traceability is also critical especially when there is a need to know the source of the data and determine its credibility in order to obtain more information.

• Flexibility means there is no risk of compromising data quality when the CMMS database or a process supporting it is changed.

Overall, the aim of having a CMMS is to not only improve the utilization of the company’s assets but also the performance, reduce capital assets and operating costs related to assets. It is also instrumental in extending asset life and improving the Return on Assets (ROA).