Data quality issues never seem to go away. Some data specialists view the ultimate fix as a simple one: validate the data when it’s first entered into your business systems. In reality, this approach isn’t as simple as it seems. End-users always find workarounds that skewer the best intentions of system designers. When you compound them across multiple systems over several years, the impact on your business can be costly.

Learn how to stop running from chronic data quality issues and bring your data—and your business—under control. 


The Source of Your Data Quality Issues 

Data quality problems often start at entry points, such as an online form or customer data inputs entered by operations teams. Each point gathers different details about an individual. But the risk of human error persists as indicated by misspelled names or mistyped information.  

Validation points, such as address lookup or prompts in a free-text field, help ensure the information is entered correctly. Even then, it’s human nature to look for shortcuts as workarounds despite their effect on upstream data use. It’s typical to find dummy text entered in mandatory fields to bypass the system, even from trained operations staff.  

When you finally bring the data together from multiple customer systems, you realize just how out of sync it is. The real challenge is that, when users take shortcuts, they don’t realize the impact their actions might have on downstream data consumers.


Business and Regulatory Drivers for Data Remediation 

A critical success factor of data quality management is how much you can automate to save on manual efforts. Another factor is whether you need to conduct a major rectification exercise driven by the regulator. 

Regulatory drivers enforce these exercises to ensure companies have a more accurate view of their finances and control over their business. For example, in the banking and finance industries, companies must comply with Know Your Customer (KYC) regulations and be able to define the legal entities that make up their multinational customers.  

The regulatory factors push companies into compliance by having audit points they must meet. Complying with these regulations comes at a significant cost when you have several teams manually trying to reconcile records. And if you wait until the last minute to reconcile your data, it’s more costly because of the extra resources and work required to meet the deadline.  


The Challenge with Traditional Data Quality Approaches 

Data quality management refers to an array of solutions to profile, clean, and organize data. It’s often a “necessary evil” to cleanse data for reporting and application use. It normally involves pulling data into a separate environment to analyze, join, and sort it in a way that’s specific to the goal. 

A major challenge with data quality is that the job feels never-ending and ultimately overwhelming. It’s like trying to clean up the mess as you see people littering ahead of you. Even once your data is resolved, over time, the efforts become undone again by normal human behavior.



Quality scoring on the single view across all of your data gives you the levers to continually monitor and improve quality. Over time, the amount of serious data quality issues decreases, so only minor issues remain.  


Take the First Step with Quantexa 

As the first step toward data quality, connect your customer records into a single customer view. It starts with the Quantexa platform, which has Dynamic Entity Resolution at its core. 

  1. You build a single view of customer records, making connections even when individual source records are incomplete.  
  2. By using rules, you create a candidate record that contains all the attributes, scores the data, and enables your teams to make decisions about a particular record.  
  3. With the analytics framework and data exploration features from Quantexa, your team can quickly locate conflicts in the data and automatically handle those issues or flag them for human interaction. 

Poor data quality doesn’t have to be an excuse for not meeting requirements or taking on new data projects to serve your customers or drive your business. With the Quantexa platform, the big impact is on the control and continuous improvement over your data so you can work simultaneously on other data projects. By improving the quality of your data, you improve its impact on your business and the impact of your business on your customers.

The Quantexa platform can make a big impact on poor quality data.  

Find out how you can overcome your data challenges with entity resolution.

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