By: Tom Christensen, Head of Thought Leadership - Nordics, LTIMindtree

It pays to be data driven. Research conducted by Forrester¹ reveals that companies advanced in leveraging data are eight times more likely to achieve 20% or more annual growth compared to organizations just beginning their data journeys. However, for many enterprises aiming to unlock the full potential of artificial intelligence and advanced analytics, an often-overlooked barrier exists—data debt challenges.

For others, it has become a matter of survival, as unresolved data issues threaten their ability to compete and comply with regulations. As a representative from the banking sector stated: ‘If we are not compliant, we risk losing our license to operate as a bank’.

It is therefore high time to bring together industry leaders for a roundtable discussion focused on identifying, addressing, and preventing data debt challenges. This blog explores the key takeaways, strategies, and debates from that evening.

What is data debt?

Data debt, much like technical debt, occurs when unresolved data issues—such as poor data quality, governance lapses, and security challenges accumulate over time. Left unchecked, these issues drain valuable resources, hamper innovation, erode trust in analytics, and impose compliance risks. For businesses eager to adopt AI or develop insights-driven strategies, data debt becomes an invisible tax that curtails progress.

The impact of data debt

During the roundtable discussion, participants largely agreed on the detrimental effects of data debt challenges, highlighting the following key issues it introduces to organizations:

• Slowed innovation: Data debt delays the development of new data-driven services, impacting product launches and the ability to leverage advanced technology like AI.
• Compliance risks: Poor governance and weak data governance frameworks increase the risk of breaching regulations such as GDPR, the EU AI Act, or other region-specific privacy laws.
• High operational costs: Data issues often lead to inefficient workflows and reliance on manual processes, which are both costly and resource-intensive.
• Reduced decision-making accuracy: Leaders cannot afford to rely on incomplete or low-quality data when making critical decisions.
• Strained IT resources: IT and data teams spend substantial time maintaining outdated data infrastructure instead of pursuing value-generating activities.

Addressing data debt challenges isn’t just about fixing current problems, it’s about creating a robust, scalable environment to handle the data influx of tomorrow’s AI-driven world.

Understanding organizational challenges

To gain insights into the delegates’ organizations, their situations, and maturity levels, we began the roundtable by inviting each participant to share their current status regarding data debt. This was paired with brief introductions, followed by a questionnaire. Below is a summary of the questionnaire results.

How has data debt impacted your organization’s ability to innovate, meet business objectives, or maintain compliance with data regulations?

The results highlight that the most significant impact of data debt challenges on organizations is slowed innovation and time-to-market. This issue directly affects their ability to launch new products or services efficiently. Other notable consequences include increased operational costs and strained IT and data team resources, suggesting that technical debt is overburdening both financial and human resources. Reduced decision-making accuracy and compliance challenges further emphasize its broad-reaching effects on business operations.

What strategies or frameworks have you implemented to improve data quality, security, and mitigate data debt?

Organizations predominantly prioritize adopting data governance frameworks as the primary strategy to address data quality and security issues linked to data debt. This approach emphasizes clear policies, roles, and accountability across data management processes. Additionally, investing in scalable and modern data infrastructure emerged as a key strategy, with a focus on automation and advanced tools to streamline processes. Regular auditing, prioritizing data quality workflows, and fostering cross-functional collaboration between IT, data, and business units are also frequently adopted practices.

How can organizations proactively prevent the accumulation of data debt as they scale, adopt new technologies, or work with increasingly complex data ecosystems?

To proactively prevent data debt, participants underscored the importance of regularly auditing and refactoring data systems to address technical debt early. Prioritizing the modernization of legacy systems ensures technological scalability. Implementing robust data governance frameworks and embedding data quality measures into workflows were also emphasized as critical steps. Further, fostering a culture of data literacy and collaboration is seen as vital for long-term success, enabling businesses to align goals across technical and operational teams. Together, these strategies aim to establish a sustainable framework for managing evolving data ecosystems effectively.

Approaches to mitigating data debt

The approach to addressing data debt was a top priority, and for that reason, we began by discussing both the technical and strategic aspects. Participants at the Stockholm roundtable shared the following insights:

Start with a business perspective

One delegate emphasized that addressing data debt starts with understanding business priorities. Identify the problem you aim to solve and ensure the right data is aligned to address it. This approach not only streamlines focus and ensures that data efforts directly support organizational goals, but also secures stakeholders buy-in.

Implementing data governance

Establishing robust data governance frameworks was unanimously seen as foundational. Governance frameworks define data ownership, policies, accountability, and audit trails, ensuring visibility and reliability throughout the data pipeline.

Case in point: Organisations like those described in Forrester’s analysis achieved better performance by centralising their governance structures and documenting pipelines comprehensively. Improved governance not only reduced errors but also built trust in analytics.

Recognising the continuous nature of data debt

Data debt accumulates over time, evolving like a snowball. Without ongoing management, the risk of inefficiencies, compliance issues, and low-quality insights grows. Continuous assessment, combined with proactive measures, allows businesses to mitigate data debt challenges before they hinders progress.

According to some of the delegates, three are two core areas to address data debt

• Data productivity: This forms the foundation for efficiency. Streamlined workflows ensure teams can easily access insights, enhancing operational agility. Instead of IT developing data-driven services, one delegate suggested that IT should focus on enabling service development by end-users within the organization.
• Data quality, consistency, reliability, and trust: Automated data validation and cleansing reduce inefficiencies and improve data readiness for operations and analytics. Leaders shared examples of how implementing tools for metadata management, data domains, and enrichment resulted in actionable, clean datasets.

Addressing these factors is essential for actionable data. These, coupled with strong people and robust processes ensure data can drive confident and accurate decision-making.

Quick Wins for immediate impact

Achieving quick wins is crucial for delivering immediate business value. These wins build trust among stakeholders and demonstrate the effectiveness of data debt mitigation strategies. Identifying and resolving high-priority issues quickly enables teams to showcase early successes and gain support for longer-term initiatives.

Fostering cross-functional collaboration

The discussion highlighted the importance of aligning IT, data teams, and business units to create sustainable solutions. A shared vision ensures data debt is addressed holistically, factoring in technical visibility, operational workflows, and business strategy. As one delegate stated, it all boils down to fixing the data debt.

Key question for reflection

A key point of discussion was where to begin tackling data debt. Should organizations focus on implementing technical tools, or should they first refine processes and invest in teams? Many agreed that a strategic combination of both yields the best outcomes.

Strategies for proactive prevention

While mitigation is essential, forward-thinking organizations are building systems and processes to prevent data debt challenges from arising. Key suggestions included:

• Embedding quality into workflows: Build validation and cleansing processes directly into data pipelines to ensure reliable data from the start
• Training teams in data literacy: Organizations such as the participants in Forrester’s observed studies emphasized scaling data literacy through internal training programs to ensure teams appreciate and uphold data governance
• Regular audits: Routine audits help identify and fix emerging weaknesses in data architecture
• Aligning technology to scale: Investing in infrastructure such as advanced cloud platforms and low-code tools ensures that data operations scale seamlessly with growth.

Next steps for leaders

According to the audience, tackling data debt is not just a technical challenge—it’s a strategic necessity that requires a blend of leadership, collaboration, and forward-thinking. Therefore, leaders must:

• Assess and prioritize critical areas of data debt
• Build cross-functional teams to aligntechnical and business goals
• Invest in scalable tools for automation, validation, and governance to reduce inefficiencies and support growth
• Strengthen data governance frameworks to ensure data quality, accountability, and security
• Foster a data-first culture through training best practices
• Monitor and adapt strategies to meet evolving technologies and regulations
• Leverage AI responsibly with high-quality, compliant data

These actionable steps empower leaders to reduce the burden of data debt and set the stage for sustained growth, innovation, and compliance. The key takeaway is clear: no action is no longer an option. Organizations that act boldly and invest strategically will not only address today’s challenges but also future-proof themselves for the rapidly evolving data-driven landscape.

Reference links:

¹ Forrester Study: The Total Economic Impact™ of Matillion

² Efficient Data Governance for Driving Effective ESG Reporting

³ Business Leaders and Data Experts Tackle ESG Data Challenges—ESG as a business driver, insights into ESG data strategy

⁴ Charting New Horizons: The Impact of the EU AI Act on Business