How to Tackle Common Data Quality Challenges and Fix Them?

Poor data quality can lead to costly mistakes and missed opportunities. This blog post will show you how to identify and fix the most common data quality issues, so you can make better decisions with your data.

In the fast-paced business world, organizations of all sizes are betting on the AI and modern technologies to get the most out of their data assets. Even though this approach may sound more straightforward, data quality consistently needs to be checked and reassured to get the best out of it. Often, organizations end up struggling with incomplete or inaccurate data, security issues, or hidden data, which can derail their objectives. Here are some of examples how data quality might affect an organization's growth:

  • Misspelled customer names result in lost revenue opportunities from communication breakdown.
  • Inaccurate information on regional preferences may prevent organizations from venturing into new business markets.
  • Outdated emergency contact numbers can prevent obtaining urgent medical care consent.

When an organization's dataquality is compromised it creates bottlenecks for revenue generation andoperational costs - inwardly affecting the overall performance and revenue. Besides this, here are some interesting Gartner insights tobe aware of:

Gartner predicts that 70% of organizations will rigorously track dataquality levels via metrics, improving it by 60% to significantly reduceoperational risks and costs.

Gartner analysts predict that by 2024, approximately 50% organizationswill embrace contemporary Data Quality (DQ) solutions to enhance their supportfor digital business endeavors.

1. Inaccurate data

In the healthcare sector, data accuracy plays a crucial role in seamless functioning and success. The COVID-19 crisis highlighted key points about why, how, and what needs improvement to address real-world challenges. Inaccurate data does not support growth, smooth functioning, or even appropriate response when needed. When the customer data is not accurate, personalized customer experiences disenchant, and marketing campaigns fail miserably.  

A drill down into the issues may point out several factors such as data decay, human errors, and data drift. According to Gartner, month-on-month roughly 3% of data gets decayed globally, and this is not to be taken lightly. As a result, automated data management alone cannot guarantee data accuracy. Dedicated data quality tools are essential for maintaining data integrity.


The DO - data observability platform offers predictive, continuous, and self-service data quality assurance. It detects and resolves issues early to ensure trust in analytics.

2. Duplicate data

Currently, organizations deal with data from three sources - streaming data, cloud data lakes, and local databases which pose a tough challenge. On top of it, application and system silos may add up to this challenge. Therefore, organizations may witness overlaps and duplications in these data sources. For example, the duplication can occur in contact details; thereby, leading to ineffective marketing campaigns. Moreover, duplicate data increases the likelihood of producing unbalanced analytical results and introduce bias into ML - Machine Learning models during training.


Managing data quality with the help of rule-based techniques can help organizations avoid duplicate and overlapping records. On the other hand, with our data observability solution, the rules are automatically created and improved continuously by learning from the data.  

3. Hidden data

Many organizations fail to utilize all their data, resulting in data becoming trapped in silos or being abandoned. For instance, a marketing team that has plans to run campaigns may not benefit from data available from customer service teams. They need to create more accurate and complete customer profiles. Hidden data signifies missed out opportunities to improve services, design innovative products, and optimize processes.  


If hidden data is a concern for your organization's data quality, trust our observability solution to automatically discover hidden relationships, such as cross-column anomalies and unknown unknowns.  

4. Excessive amount of data

While data-driven analytics has many benefits, an excess of data does not necessarily indicate poor data quality. However, the truth is it often does. It's easy to get lost in a sea of data when searching for information relevant to your analytical projects. Approximately, 75% of the time data analysts, data scientists, and business users spend their time identifying the correct data and preparing it.


When the tug-of-war is with the volumes and variety of data - pouring in from multiple sources, we have the perfect solution that best fits the need. Our Data Observability Solution can seamlessly scale up and maintain continuous data quality across multiple sources, without moving or extracting any data. You don't need to worry about dealing with too much data as the solution offers fully automatic profiling, outlier detection, schema change detection, and pattern analysis.  

5. Inconsistent data  

Data inconsistencies are common in organizations that work with multiple data sources. These inconsistencies can arise in units, formats, or as spelling errors, and sometimes result from migration errors. Data reconciliation is the only solution, and it must be performed regularly without fail to ensure trusted data powers their analytics; otherwise, it may lead to a loss of data value.


The observability solution automatically profiles datasets and identifies any quality issues whenever data changes.  

6.Data Downtime

Without reliable data it is impossible for enterprises to make data-driven decisions and operations. Lack of proper data will also not help going forward confidently during events like infrastructure upgrades, reorganizations, M&A, and migrations. All this leads to poor analytical results and a spike in customer complaints.

Therefore, it is essential to keep a track on the data downtime and take immediate actions to minimize the downtime through automated solutions.


Having SLAs and holding people accountable is important, but to ensure constant access to trusted data, you need a comprehensive approach. The Data Observability solution can help you overcome handling unstructured data, invalid data, redundancy in data, and data transformation errors.

Final Thoughts

Ensuring high-quality data is a crucial aspect of the data lifecycle, yet it is not always an easy task for organizations. Quick-fix solutions often fall short in addressing data quality issues. To effectively tackle these issues at their source, the best approach is to prioritize data quality within the organizational data strategy. To achieve this, it is necessary to involve and enable all stakeholders to contribute to data quality.

With our data observability solution, you can acquire in-depth details about data quality and visibility to resolve real-time data issues and reduce downtime. You can always have confidence that the data is accurate and trustworthy for analysis, decision-making, and downstream processes. For information on how to implement data observability practices and data governance strategies, please feel free to reach out to our experts.

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