Data quality issues - Top causes and consequences

Did you know that 80% of a data scientist's time goes into cleaning data as well as integrating and preparing it? That's because they know quality issues make business processes flawed and introduce inefficiencies. For instance, for every media dollar that marketers spend, 21 cents gets wasted due to subpar data. 

So, this write-up explores the reasons behind data quality issues and the effects of bad data. Understanding both causes and consequences will help you address and enhance organizational data quality and get the leadership on board.   

Data Quality Issues: What Are They? 

These issues crop up when you use data that is incomplete, inaccurate, inconsistent, or obsolete. Reasons might include duplicate records, missing values, data source disparity, etc. In any case, using data that's poor quality or doesn't reflect reality can impact sales, operations, decision-making, and other areas.  

Poor Data Quality: Common Causes  

Here are 5 common reasons why data quality issues occur: 

1. Ambiguous Ownership 

Unless every data source or domain has a specific owner, no one is accountable for the data generated. If a change is likely to impact a certain data source, you won't know whom to inform. Implementing initiatives for data quality improvement also becomes challenging. 

2. Isolated Operations 

In many organizations, teams work in siloes. Even if they partner up for certain data initiatives, the results might not be properly documented or shared. Hence, the same problems might arise in new data projects.  

3. Absence of Data Quality Programme 

A strategic approach is essential for overcoming data quality concerns. And your entire enterprise must be involved. Hence, your data quality management program should encompass rules, shared tools, and solutions for enablement. Also ensure reporting on metrics associated with data quality and improved data's effect on enterprise initiatives. 

4. Lack of Visibility 

Are your teams unaware of what data is available and how it moves through different systems? Inadequate visibility means teams neither trust the data nor understand its quality. Proper tooling can address this problem though, like a catalog covering the lineage and quality capabilities of data. 

5. Manual Data Management 

When done manually, data collection, validation, classification, cleansing, and problem correction are extremely time-consuming. Manual processes, unlike advanced tools, also make way for human errors and drain budgets. 

Consequences of Poor Data Quality 

Not resolving data quality issues can lead to:  

1. Overburdened Data Engineers 

In companies with complicated data pipelines, data engineers fix quality-related problems repeatedly and spend much time on unearthing root causes. In the meantime, more quality issues crop up. Sometimes, data engineers scrap and rework data entirely too. Hence, they are left with little time for coding or maintaining checks. 

2. Teams without Data Confidence 

If your teams think the data quality is subpar, they cannot do their job properly. And data trust issues usually show up through:

• Data scientists devoting excess time towards data cleansing and validation 
• Business leaders losing trust in reports
• Teams not wanting to use data obtained from other organizational units

3. Extended Lead Time 

Poor quality makes it difficult to access relevant data and generate reports quickly. Anyone who needs data must first determine its location and ownership and then wait for access approval. Even after access is granted, the individual must fix inaccurate data or hunt for new data. Either way, time is wasted. 

4. Failed Mergers and Acquisitions (M&As) 

M&As bank heavily on data and lack of proper integration often leads to failures. These signs indicate data quality issues are triggering suboptimal M&As:

• Extension of integration timeline 
• Integration or migration of fewer-than-expected systems 
• Inconsistent business language 
• No unified view of data domains 

5. Inefficient AI Models 

Artificial intelligence (AI) and machine learning (ML) models built on poor-quality data won't deliver as expected. Why? Data quality largely determines a model's deployment speed, performance, and long-term dependability. 

So, ideally, invest in the following: 

• Models for monitoring data drift (unexpected changes)
• Automated management of data quality
• Governance of data and AI

Otherwise, these issues are likely: 

• Frequent data drifts and lengthy investigations 
• Deployment of fewer than expected models 
• Unsatisfactory outcomes from AI projects 
• Complaints about data quality issues from data scientists and AI experts 

6. System Modernization Hiccups 

Modernization initiatives make the IT landscape and data flows simple, speed up data-related activities, and consolidate operations and billing. However, such projects cannot run smoothly with inaccurate, invalid, or inconsistent data. In fact, almost 90% of data integration projects exceed their budgets substantially or entirely fail due to poor-quality data. 

7. Inaccurate and Unreliable Reporting 

Accurate reports are necessary for better decision-making, efficiency, financial planning, performance, and compliance. After all, the average cost of non-compliance is almost $15 million.  

However, without good-quality data, you might experience: 

• Teams working overtime to compile reporting data manually 
• Teams manually aggregating spreadsheets in a data storage system
• Authorities rejecting reports often, followed by teams resolving data quality issues manually and preparing reports afresh

8. Poor Customer Acquisition and Retention 

Poor-quality data makes converting leads and retaining existing customers difficult. For instance, in case of duplicate records, a customer might receive offers or intimations multiple times, which can reduce satisfaction especially if they aren't interested. 

So, your customer data needs evaluation and improvement if: 

• It takes long to prepare campaign-related data  
• Marketing ROI is falling 
• Customers are frequently complaining about communication channels 
• Billing and reconciliation are delayed
• Marketing leaders are questioning analyses and reports 

Embrace Avant-Garde Data Quality Solutions for Your Business's Future

From isolated operations and undefined ownership to absence of visibility and manual management processes, there are multiple reasons why data quality issues happen. And they can impact your core business areas in different ways.  

That's why investing in advanced Data Quality Solutions is essential. Modern, flexible, and customizable data quality solutions enable businesses to profile, monitor, cleanse, standardize, verify, enrich, match, and consolidate data across systems. The result? Lower operational costs, more reliable insights, enhanced customer experiences, and a stronger, more resilient bottom