Hiring & Talent Strategy

Why Poor Data Quality Is a Workforce Problem, Not a Tool Problem

Posted on by MRINetwork
Why Poor Data Quality Is a Workforce Problem, Not a Tool Problem

Data quality matters. 

When an organization can trust its data, it is far better equipped to succeed. From more informed decision-making and greater operational efficiency to improved regulatory compliance, a solid data foundation is imperative. It is the backbone of any agile company, supporting the structure for all business operations, strategic decisions, and growth initiatives.

While it’s easy for organizations and their teams to blame poor data quality on IT tools, it’s crucial that you dive a bit deeper. Often, poor data indicates workplace issues—errors, silos, and apathy—stemming from human behavior and organizational processes.

This is why companies must address common misconceptions and understand the true drivers of data quality. Here is what to consider. 

Workforce Capability Gaps: The Root Cause

When faced with poor data quality, know that it is rarely a technical glitch or a tool issue. Instead, it is often a symptom of workforce capability gaps, driven by a lack of necessary skills, training, or data management understanding within the organization. 

Here is how this gap contributes to poor data quality:

  • Insufficient data literacy and analytical skills among employees often stem from inadequate training and a lack of understanding, further exacerbated by a poorly developed data culture. 
  • Lack of comprehensive data governance frameworks and ownership, often made worse by unclear, poorly defined roles and responsibilities. 
  • Challenges in integrating data across departments and systems are linked to human error and limited use of data-driven tools. 

Impact of Poor Data Quality on Organizational Performance

Poor data quality can significantly degrade an organization’s performance for the following reasons. 

Inaccurate Decision-Making Leading to Strategic Missteps

If data quality is poor, reports will be inaccurate. When leaders rely on incomplete, duplicated, or incorrect data, they are relying on flawed insights. As a result, they are misguided. The strategic decisions they make can create a ripple effect and negatively impact the company. 

Operational Inefficiencies and Increased Costs

When workers are consistently needing to identify and fix errors, productivity dips — which can be costly. There are also concerns surrounding everyday operations. Poor data quality leads to inefficient business processes, including disruptions in supply chain management and slow, inaccurate customer service.

Poor data quality can lead to brand damage, loss of loyalty, or even legal battles. The latter is especially true when poor data quality increases risks of non-compliance (e.g., GDPR, CCPA). Companies can face fines and reputational damage. 

Being aware that there may be an issue with data quality is step one, and often the most vital. This awareness will enable you to take action by implementing some or all of the following strategies. 

  • Develop and enforce robust data governance policies, including a Single Source of Truth (SSOT) and assign data stewards. 
  • Invest in employee training and upskilling programs to help ensure data accuracy and completion at the point of creation. 
  • Foster a culture that values data accuracy and integrity, connecting training to business impact. Leadership roles will be important in modeling and reinforcing your data culture. 

Data Leadership Is a Fundamental Requirement

Prioritizing a data leadership strategy isn’t just a competitive advantage, it’s a requirement. Within that strategy, this year and beyond, you must address workforce capability gaps. 

Rapid technological advancements, such as AI and automation, and shifting business models are creating significant skill gaps for companies. 

Now is the time to act so that you can target the skill sets needed to advance. 

Start with a workforce gap analysis to assess your current team’s skills vs. your future goal. Then, invest in upskilling and reskilling programs, especially in areas that may improve costs and retention rates. 

Take Your Recruitment Process to the Next Level 

You’ll also need to make an ongoing list of roles to fill, especially as positions evolve. During the recruitment process, prioritize skills-based hiring, emphasizing potential and adaptability over rigid experience requirements to broaden the talent pool. Partner with expert recruiters, especially when you’re filling high-demand or niche positions, like MRINetwork.  For over six decades, the MRINetwork team has helped companies of all sizes build high-performing teams. Learn more about our approach today.

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