The five most common data quality issues and how to overcome them

With the emergence of data socialization, many organizations gather, exchange, and make data accessible to all workers in an effective manner. 

Although most companies benefit from the extensive use of such information resources at their workers' hands, some face problems with the accuracy of the data they use.

As most organizations nowadays also look at implementing artificial intelligence systems or connecting their business via the Internet of Things, this becomes especially important.

Data quality issues can stem from duplicate data, unstructured data, incomplete data, different data formats, or the difficulty accessing the data. In this article, we will discuss the most common quality issues with data and how to overcome these. 

Duplicate data

Multiple copies of the same records take a toll on computing and storing, but may also produce skewed or incorrect insights when undetected. One of the critical problems could be human error — someone simply entering data multiple times by accident — or an algorithm that went wrong. 

The solution suggested for this problem is called "data deduplication." It is a combination of human intuition, data analysis, and algorithms to detect possible duplicates based on chance scores and common sense to determine where records look like a near match.

Unstructured data

Many times, if data has not been entered correctly in the system, or some files may have been corrupted, the remaining data has many missing variables. For example, if the address does not contain a zipcode at all, the remaining details might be of little interest, because it will be challenging to determine the geographical dimension.

With a data integration tool, you can help convert unstructured data to structured data. And also, move data from various formats into one consistent form.

Security issues

In addition to industry and regulatory standards such as HIPAA or PCI Data Security Standards (PCI DSS), data security and compliance requirements come from different sources and include organizational requirements. Failure to comply with these rules can result in hefty fines and, perhaps, even more expensive, loss of customer loyalty. Guidelines provided by regulations such as HIPAA and PCI also present a compelling argument for a robust data quality management system.

Consolidating the management of privacy and security enforcement as part of an overall data governance program gives a significant advantage. This may include integrated data management and auditor-validated data quality control procedures, giving business leaders and IT confidence that their company meets critical privacy requirements and protections against possible data leaks. By protecting customer data integrity with a unified data quality program, customers are encouraged to build strong and lasting connections to the brand.

Hidden data

Most companies are using only about 20% of their data when making business intelligence decisions, leaving 80% to sit in a metaphorical dumpster. Hidden data are most beneficial in regards to customer behavior. Customers interact with companies today in a variety of mediums, from in-person to over the phone to online. Data can be invaluable on when, how, and why customers interface with a company, but it is rarely utilized.

Capturing hidden data with a tool like the Datumize Data Collector (DDC) can give many more insights into the hidden data you now have obtained.  

Inaccurate data

Finally, there's no point in running big data analytics or making contact with customers based on data that is just plain wrong. Data can quickly become inaccurate. By not gathering all the hidden data, your data is not complete and limits you from making decisions based on complete and accurate data sets. The more obvious way for inaccurate data is data in systems filled with human mistakes, like a type or wrong information provided by the customer or inputting details in the wrong field. 

These can be among the toughest data quality issues to be found, mainly if the encoding is still appropriate- for example, entering an inaccurate, but legitimate, social security number can go unnoticed by a database that only checks in isolation the veracity of the information.

There's no cure for human error, but ensuring you have clear procedures that are followed consistently is a good start. Automation tools to reduce the amount of manual work when moving data between systems is also hugely useful in reducing the risk of mistakes by tired or bored workers.