Data quality is the absolute greatest impediment to revenue for a modern data-driven business that there is. Good data quality can lead to a drastic boost in the ability of a business to make sales. On the other hand, poor data quality can demolish any hopes a company has of making good returns on their investments.
So what exactly qualifies poor data quality?
We’ve drafted up a list of seven common data quality issues so you can have a better understanding of what you shouldn’t do when working in a data-driven environment.
1) Poor organization – When you’re not able to easily search through your data, you’ll find that it becomes significantly more difficult to make use of. With different organizational methods, there’s dozens of ways that data can be represented. With abbreviations, that number gets even bigger.
2) Too much data – 40 percent of people reported that there’s often too much data to properly work with in a database. While it might seem like “too much data” can never be a bad thing, more often than not, a good portion of the data just isn’t usable, which is going to mean that you’re spending more time digging through data that just isn’t usable looking for the one record that’s good to use.
3) Inconsistent data – When dealing with multiple data sources, inconsistency is a big indicator that there’s a data quality problem. In many circumstances, the same records might exist in both databases. Duplicate data is one of the biggest problems that exist for data-driven businesses and can bring down revenue faster than any other data issue.
4) Poor data security – 20 percent of people say that they would never consider doing business again with a company that failed to handle their data in a professional and secure manner. When working with customer data, there must always be precautions in place to make sure that it can’t be used for theft, fraud and spam, which will almost guarantee the loss of a future renewal.
5) Poorly defined data – Similarly to the problems that come with poor organization, sometimes data is poorly defined, which is going to create confusion around using data correctly. For example, data that’s sectioned into the wrong category, like a company account being filed as a single person’s contact is going to really mess things up in your database and make the whole thing more difficult to understand and sort through.
6) Incorrect data – Data decays at a rate of 2.2 percent per month. Therefore, it’s almost definitely going to be the case that some of your data is outdated, which is going to make it incorrect data. It’s a massive issue, as anywhere from 10 to 25 percent of data has errors within it.
7) Not being able to find the right data – People generally spend 30 percent of their time with data just looking for the right data that they need, not even using any of the valuable information inside. Even worse- in 40 percent of searches, people never even find the data that they were looking for in the first place.
Throughout all of these common data issues, the common theme is that in order to have your data in the best condition possible, organization is key. Likewise, the best way to keep your data in order is to implement a proactive data solution that can take care of all of the listed common data quality issues.
Luckily, the RingLead Data Management Solutions suite has all the tools that you’ll need in order to take care of all of these data quality problems. RingLead DMS is the only full stack data solution that can generate new leads all while enriching existing and incoming data and getting rid of all duplicate data in a database.
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