Data quality is a value to measure the understanding and usefulness of a data set in an organization. Data can alter a decision, impact roadmaps and determine the requirements of any division in the said organization. Data quality is determined by various factors like accuracy, completeness, consistency, reliability, and timeliness.
But depending on the organization and its need, the standard for data quality varies. Not one size fits all. But it is vital to focus on data quality to find and report suspected failures, set metrics, govern, and provide security
5 Common data quality issues
Data duplication :
The data in an organization usually gets compromised when they have multiple system sources to manage data sets. It will lead to data silos because data controlled by one group may not be accessible to other groups. It is why some organizations provide unique handles to their customers. There will be multiple details that coincide and may erupt confusion. Overlapping the same data or exchanging the correct data with the wrong one can lead to data duplication. It will further cause damage to production, analytics, and decision-making.
Inconsistent formatting :
Another problem with having different sources to input and extract data is inconsistent and multiple formats. It is due to the lack of data homogeneity. It causes delays in queries because, with incorrect data, the system might misplace them in unrelated data sets leading to errors and duplication. It can be undone by following a standardization process with the help of Master data management tools like Prosol.
Inaccurate data :
At this point, we know having standard and accurate data is everything to manage an organization when they shift everything online. The only way to identify a customer is through the database. It will become tedious if the source doesn’t have the needed information about that customer in the right place. The only way to rectify it is by verifying the data with trusted sources.
Data transformation errors :
Over the years, data storing system has evolved with the need to acquire a variety of consolidated data based on different requirements. So, when data gets transferred from one format to another, errors might occur, misplacements will happen, and sometimes even the categories to which data should be updated get interchanged. The tool to convert has to be smart enough to identify requirements and categorize data effectively.
Incomplete information :
Let’s say a customer orders a product and the address they have provided misses their door or a plot number. Even if there is no problem with production and shipment, it will be hard to locate them. The transit charge turns out to be a waste. A complete set of information should be uploaded to the database to access and fulfill the service at every given point of the process to avoid problems.
Ways to avoid and solve the data quality issues
- The first step to avoiding data quality issues is fixing the error in the source system before collecting and consolidating them.
- Then the data can undergo a process of cleansing and standardization to eliminate duplication, errors, inaccuracies, and incompleteness.
- A vital step to avoid any issues is finding and fixing the errors before entering any data in the database. It will eliminate many complications in the future.
- But remember that data quality issues are inevitable. So, it’s necessary to accept that and work on a solution.
Thankfully, Prosol can do that job for you. Click here to learn how Prosol can help you identify errors in no time and rectify data quality issues.
