Data Debt—The Business Risk You Never Knew You Had

There's a good reason why data is often framed as the new oil; high-quality, accessible data is one of the most vital resources a business can leverage. Data acts as the fuel to help push businesses forward, enabling operational efficiency, strategic decision-making, and digital innovations like AI and machine learning. Still, data’s benefits only materialize when businesses are freed from the burden of data debt.

Data debt refers to the myriad issues of inadequate data management across an organization. These problems—poor data quality, lack of governance, or improper formatting—prevent businesses from realizing their data’s true potential. While data debt often leads to lost financial, operational, and productivity costs, it also creates major compliance and security issues that can jeopardize businesses.

As companies increasingly rely on data to enhance internal decision making, operational efficiency, productivity, and new revenue streams, ignoring data debt can no longer be avoided. Business leaders must understand how data debt arises, the consequences of inaction, and how to prevent it so they can maintain complete control and drive meaningful value.

How Businesses Acquire Data Debt

Companies in the late 1980s typically stored their data in centralized data warehouses. By the mid-2010s, demands for greater data storage led to the rise of data lakes, allowing companies to store large quantities of raw, unstructured data. Around the same time, cloud platforms emerged as a popular choice for data storage, offering businesses scalable management capabilities from virtually any connected location.

Related:How Technical Debt Can Impact Innovation and How to Fix It

Today, many companies use hybrid-cloud infrastructure to meet their data storage needs, leveraging a combination of on-premises storage, big data environments, and cloud-based systems. While a hybrid approach offers mobility, security, and financial benefits, the complexities of managing assets across multiple environments have escalated data debt for many businesses.

The fragmented nature of modern data storage, combined with the rapid accumulation of company data, has greatly contributed to the rise of data debt. Because data sets are frequently siloed in different locations, it’s often difficult for companies to follow the same data management practices and maintain proper governance and security.

Data debt can also grow from other factors, such as low data literacy among employees and organizational structure shifts. Employees who lack training in proper data management can lead to inefficient practices or methodologies that make it difficult to use data effectively. Companies that have undergone mergers and acquisitions usually must move and restructure their data assets, which often creates inconsistencies that increase data debt further.

Related:Data Skills Gap Is Hampering Productivity; Is Upskilling the Answer?

The Consequences of Data Debt

Companies that fail to address data debt are impacted in multiple ways. For instance, converting low-quality data into high-quality data requires significant effort from data engineers, data scientists, and business analysts, which can drain time, resources, and operational costs.

Any time spent cleaning, validating, and transforming data means the business is less focused on initiatives that drive progress. As discussed in the Matillion guide to data debt, a data scientist can spend nearly 80% of their time preparing data for analysis, leaving them with a small amount left to model and analyze it.

Data debt makes it harder for companies to acquire actionable insights that can improve decision-making. AI models or analytics using low-quality data make their outputs inaccurate and unreliable, preventing companies from gaining knowledge or ideas that can improve growth and performance–a major opportunity cost.

Data debt can also create serious issues for businesses operating in highly regulated industries, such as healthcare or finance. Companies that fail to properly store and manage personally identifiable information (PII) or other sensitive data may become non-compliant with industry regulations and face severe penalties or fines.

Related:The Rise of Generative AI Fuels Focus on Data Quality

Tackling Data Debt

While data debt can seem like a significant obstacle to overcome, companies can utilize the following tactics to free their operations from its grasp:

Prioritize quality and proper management

Establish management and governance policies that focus on data quality, such as standards for data entry, regularly scheduled audits, and continuous data monitoring. Additionally, ensure employees are upskilled in data literacy and make proper management a component of company culture.

Catalog data assets

Create a clear record of all data sets' locations, sensitivity levels, and ownership so they can be easily used and managed. For greater efficiency, use cataloging tools to automate processes and ensure records are continuously updated.

Evaluate the business impact

Understand and articulate the consequences of data debt to other company stakeholders so they can better understand its influence on operations and finances.

Emphasize collaboration

Ensure teams are aligned on data management policies and ownership. Make sure employees are communicating any changes to processes or storage and remain transparent about their work.

Monitor progress

Review and report the company’s progress in reducing data debt to keep owners accountable for their work. Use key performance indicators (KPIs) and metrics to understand if new processes are reducing data debt and improving quality.

Falling further into data debt is a serious business risk that can prevent companies from fully realizing the potential of their data. By implementing and investing in data productivity tools, businesses can create clean, structured, and well-maintained data, which will help gain valuable insights, maintain compliance, and capitalize on new revenue opportunities.

About the Author

Matthew Scullion is the CEO of Matillion.