Key Takeaways:

• Data debt is a silent threat that undermines decision-making, customer satisfaction, productivity, and revenue—costing enterprises up to 35% across key success metrics.
• Poor data quality and governance practices are the root causes, often compounded by shortcuts, siloes, and deprioritized maintenance.
• To resolve data debt, organizations must treat data as a strategic asset, quantify its business impact, and focus on outcome-driven remediation.

Data’s current—and future—ability to drive business value cannot be overstated. Enterprise data can be transformed into insights that optimize operations and enhance decision-making processes.

However, many organizations already grapple with data debt: the compound results of poor or inefficient data management and a lack of data quality and integrity standards.

At best, data debt can hinder progress and efficiency; at worst, it can lead to lost opportunities and revenue. But how do you identify data debt in your organization, and what strategies should you employ to tackle it effectively?

What is data debt?

Data debt refers to the accumulated cost and negative impact of poor data management practices—such as bad data quality, lack of data governance strategy, and neglected maintenance—that hinder an organization’s ability to use data effectively.

Data debt builds up over time when quick fixes or shortcuts are used instead of investing in proper, sustainable data practices. It leads to issues like data silos, inaccurate reporting, customer dissatisfaction, reduced productivity, and lost revenue opportunities. The longer it goes unaddressed, the more costly and complex it becomes to resolve.

Not sure if you're at risk of data debt? Get the free checklist on the 9 warning signs of data debt 

What are the signs of data debt?

Data debt often manifests in several ways, such as data siloes, customer satisfaction, trust in your data, stagnant productivity or revenue, and compliance issues.

Hard-to-access Data Silos

Silos arise when information is contained within individual teams or departments, getting in the way of cross-functional access and sharing. Without a complete view of enterprise data to make informed decisions, teams can struggle with inaccurate reporting and forecasting, miss targeting the right customers at the right time, and fail to meet market demands.

Poor Customer Satisfaction

Inaccurate or outdated data can lead to errors, service delays, and loss of customer trust. Customer expectations are higher than ever. Any discrepancies can impact their interactions and satisfaction levels, leading to potential drops in revenue.

Low Confidence in Decisions

When your data can’t be trusted, neither can the decisions based upon it. Leaders and stewards may hesitate to act on flawed or incomplete information, fearing the repercussions of getting it wrong. And while this hesitation is understandable, it also grinds your progress and innovation to a halt.

Stagnant Productivity

Inefficient data systems can hamper productivity. Employees spend more time searching for information or rectifying errors instead of focusing on strategic higher-value tasks.

Revenue Plateaus

Data debt may lead to missed business opportunities, resulting in stagnant business growth and plateauing revenue. Inefficient data use prevents businesses from capitalizing on the latest market trends and customer demands.

Compliance Issues

Inadequate data management poses risks beyond operational inefficiencies and can lead to non-compliance with regulations and data protection laws, resulting in legal ramifications, financial penalties, and reputational damage.

Data debt burdens enterprises with a lost opportunity cost of 25% – 35% across almost every measurable success metric, including customer satisfaction (CSAT), decision-making, employee productivity, revenue impact, and compliance impact. It is in your enterprise’s best interests to address data debt before it compromises your ability to adapt and compete in a swiftly evolving market.

Learn about the full impact of data debt and how to combat it. Download the HFS research report produced in partnership with Syniti now.

What causes data debt?

The short answer: bad data.

Research suggests that 2 in 3 business leaders are dissatisfied with their enterprise data quality. More than 40% of organizational data has been deemed unusable. Low-quality, unreliable, inaccurate, and inconsistent data cannot be used or trusted for your key business initiatives to succeed.

Data debt only worsens due to data management inefficiencies, such as

• Shortcuts and quick fixes in place of regular maintenance
• Failing to establish a proper data foundation
• Postponing essential measures
• Ignoring or deprioritizing data governance and data quality

Putting off data management tasks turns small, fixable problems into multi-departmental crises. Enterprises that orchestrate and prioritize data management and governance tasks are better equipped to manage daily operations, identify gaps swiftly, and prevent minor issues from becoming major ones.

Not sure if you're at risk of data debt? Get the free checklist on the 9 warning signs of data debt 

How do you resolve data debt?

To eliminate data debt, identify the root causes of your bad data and mitigate them. Understanding these foundational issues is the first step in improving your data quality and business performance.

Three steps to transforming your data into a strategic asset.

1. Treat data as a core business issue
   Data is no longer just IT's problem. Enterprises must raise its profile and prioritize it as a core business matter. Establish data quality metrics, objectives, and accountability for managing data throughout the organization. Encourage cross-departmental engagement to align data objectives with business goals, enhancing the enterprise's ability to leverage data strategically.

2. Quantify the impact of bad data
   Understanding the negative effects of bad data is essential to reduce data debt and transform your data into a growth-driving, innovation-enabling asset. Use data analytics to determine where bad data is impacting operations and customer satisfaction. Then, use this information to prioritize your next remediation efforts.

3. Focus on outcomes
   Shift the focus from effort to outcomes. Prioritize initiatives that deliver tangible results, ensuring that data management efforts align with your organizational objectives and real-world benefits.

Save Yourself from Data Debt

Data debt may already be present in your organization, but it's never too late to take action. By championing a Data First approach, prioritizing data quality and governance, you can transform your enterprise's data into the powerful asset it was meant to be.

To learn more about the full impact of data debt and how to combat it, download the HFS research report produced in partnership with Syniti now.