With data becoming a major buzzword, data quality has been a point of interest for most data specialists.
Superior-quality data is the ultimate driver of revenue for modern businesses. Good data can generate unprecedented lead conversion rates, account-based success, and winning deals.
In contrast, poor quality data can significantly drop the ROI of a company’s CRM and marketing automation investment.
With that being said, let us take you through the biggest reasons bad data is still an issue even in 2021.
Duplicates
Data duplication is a situation when an exact copy of a data point is created. To those unaware, this issue seems simple. However, data duplication is a widespread concern and can get pretty tricky to fix.
Thus, in healthcare, duplicate medical records are growing at a fast pace. This leads to patients often being mistreated.
We all know the number of risks it poses. But who is to blame for data duplication? There are a few guilty parties:
The human factor. You are likely dependent on your employees to fetch valuable data for you. We humans get tired quickly and cannot press on with the same task for a long time. As a result, fatigue makes your workers enter multiple copies of the same data piece.
Data duplication happens when you compile data from various websites. To keep search engines happy. listings may be slightly altered. Therefore you won’t be able to detect duplicates unless you turn to an advanced querying tool.
Duplicates are also common when you are fetching users' feedback. It’s almost the same as the first one.
Data duplicates are also common when you are asking for users feedback. Like your employees, users are mistake-prone, although the reasons may vary.
Inconsistent Formatting
Inconsistent data formatting is another issue that haunts most organizations. If the data is saved in inconsistent formats, the systems used to analyze the information may not interpret it as needed.
If the company collects the database of their consumers, then the format for basic data pieces should be specified. It may be especially challenging for systems to differentiate the US and European-style dates and phone numbers, especially when some have area codes and others don't.
Inaccurate Data
Finally, it is pointless to carry out data analysis or interact with users based on data that is just wrong.
If it weren’t a common pain, this issue wouldn’t make it to our list. Inaccurate data is generated for a number of reasons.
This could be the case of your customers providing erroneous information to a human operator. This could be details ending up in the wrong field.
Incorrect data is especially tricky to detect, since entering an incorrect but valid phone number that conforms to the general formatting is almost impossible to detect.
The Bottom Line
Human error cannot be cured. But if you embrace clear procedures that are followed consistently, your data analysis will be accurate and likely super effective at helping you produce the results you seek.
Also, automation tools help decrease the risks of mistakes by exhausted and bored workers. Do your data justice!