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7 Common Issues Affecting Data Quality

by SwoopTalent, on May 10, 2018

Today's data-dependent businesses are significantly impacted by the quality of their data. Every forecast and business decision is driven by data, so it's easy to see how bad data could negatively impact the bottom line. Here are seven common data issues to watch for in your business.

1. Data Duplication

Data that is gathered from multiple sources (which happens more all the time) has a much higher chance of duplicate records than single sourced data. Record duplication is one of the significant problems that data-aware businesses face, and it can impact the bottom line due to skewed projections and duplicate marketing efforts.

2. Excessive Data

Believe it or not, it is possible to have too much data. Data that should not have been collected serves to confuse entity relationships and slow down data mining efforts. Excessive data can cause valid data relationships to be overlooked.

3. Poorly Organized Data

The wrong table structures can make data mining an arduous process. The data organization should reflect the relative importance of the data to your business, not necessarily the business organization itself. 

4. Stale Data

Customer Data becomes stale every day due to natural and expected changes, such as address changes, new phones, and new email accounts. This means that at any moment a substantial portion of your business data is going to be obsolete. The best way of combating this loss of data is to have a means of refreshing it, such as an email campaign or a customer prompt when they log into your site.

Another option is to have a Last-Update field and use it to ignore portions of data during analysis that may be suspect.

5. Weak Data Security

Anytime customer data is stored or handled, precautions should be in place to prevent fraud and theft. There is an implied layer of trust between the customer and your data systems, and failing to uphold that trust is a sure way to lose valuable customers.

Businesses need to develop a robust strategy and follow it when it comes to data security.

6. Poorly Collected Data

Avoiding incorrect and spam data is an essential part of a customer-facing interface. Sometimes using simple constraints, such as numbers only for a zip code, can go a long way to keeping the data quality high.

Many times bad data can be collected even though it was not a deliberate spam attempt. For example, many customers will fill out the name and address information without even looking at all of the field labels. A field not in the typical order for a common data set could end up collecting a lot of incorrect data.

7. Poorly Defined Data

Sometimes data gets crammed into other fields that it doesn't belong - perhaps in the name of expediency. This is sure to create problems when the data is analyzed later. Sometimes it just doesn't pay to be clever when solving a data collection issue. Sometimes you need to call in the professionals.

The importance of data quality can't be overstated in today's data-driven economy. Be sure to properly maintain your data as if your business is depending on it, because it truly is. SwoopTalent uses advanced AI algorithm solutions, providing you with 'best-of-breed' data solutions for your global talent needs. Contact us to see how we can help with your data quality.

Topics:talent insightsdatatalent data as a servicetalent datadata science

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