A data set is a collection of data that relates to a subject. In database terms, it will correspond to the contents of a single table. Each row is a member of the data set, and each column represents a specific variable that applies to the members.
For example, a list of students in a class will be the members of the data set. Columns can represent each of the tests they have taken during the semester. Enter the corresponding test scores for each student. The resulting table is a data set representing the students’ test scores for the semester.
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How to Maintain the Accuracy of a Data Set
Many businesses rely on data for most processes. Working on useful data depends on the nature of the information. Organizations must always work with data of the highest quality so they can make plausible and reliable analyses and inferences. Finding out that a database is erroneous toward the end of the analytic process can be problematic. Not only is it hard to pinpoint where the error began, but it can be too time-consuming and expensive to fix. As such, ensuring the accuracy of a data set is essential from the get-go. But how do you do just that? We listed some ways below.
Do Data Profiling
Often, the source of bad data starts from the actual receipt of incoming data. Organizations that work on data coming from another source, such as a department or a third-party application. Hence, there is no guarantee of data quality, and using a handy data profiling program can help sort through the information and classify it accordingly.
A data profiling tool helps data analysts check patterns, formats, consistencies, and abnormalities, and even completeness. Automating data profiling can help maintain data quality throughout.
Create a Data Pipeline Design
Another data set issue that most organizations have to deal with is duplicate data. Duplicate data refers to data set inputs obtained from the same source using a similar logic but by different people.
Working with duplicate data can lead to incorrect analyses. To avoid such a scenario, designing a data pipeline is a must. This pipeline must be carefully integrated into operations and should include a means for various teams to communicate effectively.
Establish Data Set Integrity
Organizations must always maintain data integrity. Checks should be put in-between each data gathering and processing step to ensure that all inputs are free of errors before these are passed on to relevant departments. It may be necessary to set up triggers when a step is not yet accomplished, so the data remains unusable until it is corrected.
Scalability issues should also be considered. As data sets grow and come from various sources, organizations must prepare to use additional databases to accommodate all information.
Build Data Control Teams
Organizations that rely on data should have a dedicated team responsible for data quality checks. One of their primary tasks is to analyze existing software and applications. There may be times when they also need to get feedback from the teams that use the collected data.
Teams can use modern artificial intelligence (AI) technologies to ensure that data accuracy is maintained when compiling data sets. These technologies can capture data, detect duplicate records, and identify anomalies that may affect the overall information quality automatically.
Maintaining quality data sets means taking full control over information through robust management as soon as the data sets are compiled. That ensures that no issues can hamper an organization’s operations.