Data has become the heart of all businesses across the world. Organizations heavily rely on data assets for the decision-making process but unfortunately “100% clean and accurate data” doesn’t exist. Data is impacted by numerous factors that deteriorate its quality. According to experts, the best way to fight data issues is to identify their root causes and introduce new processes to improve their quality. This article covers the common data quality issues faced by businesses and how can they fix them optimally. Before we dig deeper into this, Let us first understand why the knowledge of these issues is important and what impact it makes on business activities.
What is data quality? Data quality refers to the measurement of the current state of data against traits like completeness, accuracy, reliability, relevance, and timeliness. While the data quality issue indicates the presence of a defect that harms the above-mentioned traits. Data is beneficial only if its of high quality. Some of the consequences of poor-quality data are as follows:
Even with all the automation, data is still typed on various web interfaces. Hence, there is a high possibility of typographical mistakes leading to inaccurate data. This data entry can be done both by the customers and the employees. Customers may write the correct data into the wrong data field. Similarly, employees may make a mistake while handling or migrating the data. Experts recommend automating the process to minimize the involvement of data capture from humans. Some steps that may help in this regard are:
Nowadays, data comes from multiple channels giving rise to duplicate data when merged. It results in multiple variations of the same record providing skewed analytical results and incorrect insights. The budget is also wasted on these duplicate records. You can make use of Data Duplication Tools to find similar types of records and flag them as duplicates. Another technique that may help you out is standardizing your data fields and having some strict validation checks on data entry.
Mismatches in the same information across multiple data sources can lead to data inconsistencies. Consistency is important to correctly leverage the data. The inconsistencies may arise from different units and languages. For example, the distance may be expressed in km while m was required. It messes up all the operations of the business and needs to be addressed at the source so that the data pipelines provide trusted data. Therefore, need to make all the desired conversions before the migration and introduce the validity constraints. Constant monitoring of the data quality can also help you identify these inconsistencies.
Inaccurate data can seriously impact the decision-making holding the businesses to achieve their goals. It is tough to identify because the format, unit, and language are correct but there may be a spelling mistake or missing data making it inaccurate. Loss of data integrity and data drift (unexpected changes over time) are also indicative of data inaccuracy. We need to track them down in the early stages of the data lifecycle by employing various data management and data quality tools. These tools should be intelligent enough to spot these issues by automatically excluding incomplete entries and generating an alert.
In practice, many fields in the dataset may be calculated from the other fields to extract meaningful information. These are called computed fields. For instance, age is derived from the date of birth.
