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Now that we are living in the world of data, everything can be represented by a set of numbers or clusters of information in the cloud. However, there are many cases where data may be muddled up, duplicated or corrupted that turns into junk data that prevents us from seeing what we want to see. With so many data clusters around the cloud, clean up is important to make sure data is clean and represents high quality information that helps with our work.
Data cleaning is an important part of a fleet manager’s job. When looking at huge sets of data everyday and making timely decisions based on the data, they have to make sure that the data is accurate. And by doing so, they need to correct inconsistencies either manually or through fleet management softwares.
There are multiple benefits to data cleaning. All of which makes the tedious task worth doing.
1) Removing inconsistencies and errors
Inconsistencies and errors may arise when different datasets are pulled into a cloud system. For example, individual data sets from ELDs in your fleet pulled into the fleet management software may show inconsistent numbers or incorrect data inputs.
2) Higher efficiency
Clear data means you can read the data without any need to double check or guess for consistency. Accurate information allows for accurate decisions with less time spent.
3) Higher satisfaction
Less errors mean customers are happy and everyone else on the team happy.
4) Better understanding of data
Clean data makes it easier to manipulate and learn how to interpret it.
Cleaning data can be done in a few simple steps.
1) Monitoring errors and inconsistencies.
Data errors usually occur with a trend as they usually come from the same source. This will help in identifying the root cause and help fix the problem of data errors. Tracing it back to the cause helps unclog data processing and streamline work in other departments.
2) Monitoring points of entry
Data coming in from one point of entry helps reduce the risk of duplicating the data. Multiple points of entry may lead to overlapping data that needs to be deleted after.
3) Validating data accuracy
Checking data for validity makes sure the data is accurate and is representing what it should represent. There are many data tools that use AI or machine learning that test for accuracy.
4) Deleting duplicate data
Using data cleaning tools help analyze raw data in bulk and delete duplicate data automatically.
Having a clean set of data is more than just having better organization and easier access. It also means the data can be used for deeper analysis and processing. With the help of Fleethunt Technologies ELD solutions and fleet management software, this data can be further used to develop business strategies or programs to increase driver retention and customer satisfaction. Learn more about fleet management in this article here.