The importance of good hygiene is ingrained in us at an early age. And for a good reason. But the hygiene we’re talking about today has nothing to do with brushing your teeth. Today’s topic is data hygiene actually plays a pivotal part in an organization’s success.
As anyone who’s ever dabbled in data can attest, insightful to an organization what’s being reviewed is accurate. Focusing on data hygiene—more specifically, data cleaning—is the most effective way to ensure inbound data is both accurate and relevant. For those unfamiliar with data cleaning, we wanted to explore the subject further, providing a detailed explanation and some examples of how this process can help your business excel.
The first thing you should know is this concept goes by several names. You may also have heard it referred to as data cleansing or data scrubbing. For our purposes and to remain consistent, we’ll stick with data cleaning.
Data cleaning is an important step that takes place early in the data analytics process before evaluation or exploration occurs. Essentially, it’s the act of validating data for errors or erroneous data that snuck in and could taint the analysis. That encompasses inaccurate, duplicative, corrupt, improperly formatted, or incomplete data. Why is this important? In 2016, IBM estimated that poor-quality data cost U.S. businesses more than $3 trillion—a number that has likely risen in the years since.
When discussing the benefits of data cleaning, most of the advantages revolve around accurate data. Armed with accurate data, companies can feel much more confident about their operational decisions. Imagine a scenario where you’re considering eliminating a product your company manufactures or a service you provide. If in reviewing the data, your worst-performing good or service appeared as your best due to an influx of bad data, you’d likely make the wrong decision. In a company that practices sound data cleaning, that’s not a possibility, and leaders have accurate information to inform them of these types of decisions.
In an industry like field service management, where customers are the lifeblood of the business, we can’t stress enough the importance of data cleaning. Properly validating data can improve customer satisfaction and your brand’s relationships with customers. For example, incomplete data fed into a system with no cleaning could result in an incorrect address appearing on a work order. As a result, technicians may drive to the wrong location, which means a delayed, rescheduled, or even canceled appointment. However, it bears repeating that clean and accurate data eliminates that risk.
So far, we’ve discussed the importance of data cleaning in terms of operational excellence. But what about how data cleaning can help data collection? The benefit of data cleaning is that it gets eyes on data coming in from multiple sources, which should enable you to spot trends. For instance, if you notice that each time you’re cleaning data, one particular source is consistently pushing incorrect or incomplete data, that’s a good indication that there’s an error somewhere in the process. In time, and with more and more rounds of data cleaning, the goal is to minimize the number of mistakes that are caught and rectified.
Looking for new ways to collect and analyze data? The fully customizable field service management software solution from EnSight+ can do that and much more. Book your demo today to learn about our embedded modules like work order management and asset and inventory management that store critically important data in one single platform in the cloud.
All monitored by you, in real time. Are you ready to take control?