5 Types of Unclean Data and How to Clean Them

5 Types of Unclean Data and How to Clean Them

Data quality is no longer something that any organization can avoid or put on the back burner. With all data being considered as assets, it is critical to ensure data credibility at every stage of processing. Today we tell you about the errors that can creep in your data and how you can ensure that it does not affect your business.
1. Missing Information – You might often see that certain data has some information missing, i.e. that data is incomplete. A common example of this problem might be the fact that certain entries do not have their addresses or contact numbers listed. In such cases, either attempts have to be made to retrieve those data or those entries have to be deleted.


2. Information with errors – The next part of the above is erroneous data. Instead of information missing, in this case, there is some kind of faulty data. For example, there is an address which doesn’t direct to any place or may be a faulty contact number which has one or two digits less than the required one. In that case, it is mandatory to fix those errors. During input, the data should be checked to follow certain set standards. Also, efforts should be made to gather the correct data, if it wasn’t possible initially.


3. Integration of Data – Data collected from users is usually stored in separate places. For example, contact information, medical information and other information might be stored in different place. These data should be carefully integrated together. This helps in avoiding errors.


4. Resolving entities – Certain systems store certain type of data. After data integration, one needs to identify whether there are multiple entries of a single person. These have to be fixed or errors will occurs. In that case, those entities have to be resolved.


5. Analysing unresolved data – After the data is input, it needs to be analysed. The reason is that often abbreviations and similar stuff are used in the text, which cannot be read by the system. So another software, with a library of that kind, needs to convert that data into proper data

These are the most common ways in which we have seen data become unclean and all our clients are advised about them. If you have any other points in mind based on your experiences, please share them in the comment section below.


Further Reading –

Blog – Why Do Businesses Overlook The Importance Of Data Quality Improvement?

White Paper – Effective Plant Management – The Role of Sound Master Data

Case Study – Material Master Data Management for a leading Oil and Gas Company

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Vipul Aroh

Vipul Aroh

Vipul Aroh is a part of the Marketing team at Verdantis. Although relatively new to the field of master data management and data governance, he is fascinated by the topics and is becoming more passionate about them by the day. Vipul holds a Master’s in Business Administration from Sydneham Institute of Management, Mumbai.

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