6 Data Cleaning Problems and their Solutions
We have already covered the various data cleaning tools and techniques for better revenue and decision in our previous blogs. Data cleaning or data cleansing is a solution for bad and dirty data problems that your database may include.
Bad data is the result of acquiring information from multiple sources, manually entering the records, inaccurate information, etc. These errors can be identified and eliminated from the database through data cleaning so that you can make the best possible business decision for your organization.
These data problems arise within the database and can be eradicated by cleaning the data. However, while you are in the middle of cleaning the data you may come across several problems where you will have to come up with proper and quick solutions to tackle such shortcomings. These errors or problems are inevitable but you have to go through them since cleaning the data is the main goal.
For an organization, it is very important that the business has the right data instead of a large data set. If you are looking to improve the quality of your data you should be aware of all the potential red flags that may emerge so that you can avoid the drawbacks. By doing so, you can enhance the analytical insights and the processes supporting decisions across the organization.
Let us take a look at some of the most common data cleaning problems that you may face and their solutions.
1. Data is not static
Arranging the data in such a way that it can be easily accessed by anyone who needs it, is a very important part of the data cleaning procedure. However, this is far from reality. Since data is collected from multiple sources the data is not arranged and is present in the database in a scattered manner.
Solution: The database warehouse should contain data in a unified manner. The warehouse should have a documented system which will prove to be helpful for the employees so that they can easily access the data from multiple sources. Moreover, data cleaning helps remove inaccurate and duplicate data thereby improving the data quality.
2. Incorrect data leads to bad decisions
You are bound to rely on different sources of data when you are operating your data. Your business decisions are thus based on such data. Therefore, if your data includes a lot of errors and inconsistencies, the decisions that you take based on them will be wrong and can sometimes prove to be fraught with danger for your business.
Solution: Cleaning your data for discrepancies is vital as it generates high-quality data that results in better business decisions. Do not process unclean data to the decision makers as it can be fatal for your business.
3. Wrong data affects client records
You are only bound to get complete client records when the names and the addresses in the database match. Names and addresses of clients can be a poor source of data and you might not the complete information always.
Solution: To avoid such mismatches, you should provide some external references that have the capability of verifying the data, correcting the inconsistencies and supplementing the data points.
4. Big data creates bigger problems
Owing to its traditional and relational databases, big data is, more often than not, a challenge for the IT department and marketers. Big data systems struggle with keeping the pace with the volume of data collected from multiple sources. When dirty data enters the big data systems, even if it is a minor error, it has a compound effect on the organization’s ERP, CRM, billing, etc., as the data is analyzed, compiled and filtered across the business channels.
Solution: The big data that is captured falls in structured, semi-structured and unstructured data category. If you understand the relevance and context of each data type, you can preserve the data quality. This way the data can show a complete picture of a customer that is presented in a more relevant manner.
5. Develop data cleaning framework in advance
Since data cleaning is an expensive and time-consuming job for your business, a lot of people don’t want to implement it in their business. Even if you might want to clean the data you may not have created a data cleaning framework in advance which might cause the process to be repetitive in nature.
Solution: Once you have cleaned the data you need to store this data in a secure location. You should keep an eye on the entire process to know which data underwent which process. Developing a data framework in advance will help you achieve this task.
6. Compliance issues
Different sources require data compliance and security requirements which may include corporate requirements apart from government and industry mandates. In case you fail to meet these rules you can be penalized with heavy fines or loss of customer loyalty.
Solution: Your business gets a significant advantage by consolidating security and privacy compliance management. This can include centralized data security and quality monitoring procedures so that your company meets important privacy standards and protects from the potential leak of data.
There are data cleaning problems and then there are solutions where you can get high-quality data. Missing information from the data is like a missing piece of a puzzle. If you omit it, your data will seem incomplete and if you try to squeeze in some other information, it will make your data irrelevant.