How to gain confidence in data quality

July 21, 2021
Use these tips to check your CMMS data integrity, so they lead to sound operational decisions.

In today’s new world of big data, the internet of things, and Industry 4.0, data analytics is a hot topic. There is tremendous activity in the manufacturing sector focused on harnessing all the operational data that is being collected to improve bottom-line business performance. The ultimate goal, of course, is to derive actionable insights from the data, insights that drive manual or automated responses.

Tactics and Practices

This article is part of our monthly Tactics and Practices column. Read more Tactics and Practices.

To support this, many analytical programs/software have reached the market to execute and visualize the analytical results. Data analytics is exciting and promising—if you’re confident in the quality of your data.

Validating data sources

When planning to analyze your business data, you need to respect the saying “Gold in, gold out.” Always validate the integrity of the data source prior to making decisions based upon its analysis. For example, if you are looking at a basic Pareto chart to identify the primary contributors to a metric, you need to ensure that the data is comprehensive before making decisions based upon the analysis. The example in Figure 1 would not meet the acceptance threshold.

Have you validated the integrity of the data source prior to making decisions? Is the data comprehensive and is it accurate? Consider these areas when evaluating the quality of your data.

How your CMMS is configured can also impact data integrity. Consider carefully how your CMMS is designed in these three areas:

Mandatory data fields. As mentioned earlier, configuring certain CMMS data fields to be mandatory for data entry can lead to the use of the “easiest entry” phenomenon. Using the first or last item on the list can result in corrupt data.

Default values. When default values are used in data fields, those fields are often overlooked during data entry. While this may seem to expedite data entry, it’s a recipe for corrupt information that could impact data analytics. If the data is almost always the default entry, then question whether the field should be used at all.

Showing the plan at the time of actual data entry. In many CMMS systems, it is common for the planned data (such as labor hours) to be included in the work order header (or even defaulted on the time entry screen). This is like identifying the answer on a multiple choice test and then asking the user to simply select it. While the planner/scheduler should know the expected work duration of a job, and issue it accordingly, the value does not need to be on the order issued to the craftsperson. By not providing this value, you can expect a more un-biased, actual data entry.

Good data = good decisions

In summary, to make good decisions based upon data, you need to be using good data. At a minimum, there should be some level of validation prior to data being used for decision making. The quality / integrity of the data is just as much a factor of data entry discipline as it is system configuration for intuitive and efficient use. When creating any data visualization, a representation of the data quality should always be a part, when possible, of how the results are provided to the decision makers.

This story originally appeared in the July 2021 issue of Plant Services. Subscribe to Plant Services here.

About the Author: Rich Jansen

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