6 tips for managing data debt in your organization

Companies have started gathering massive amounts of data to offer a potential use case via natural language processing, computer vision, and machine learning algorithms.

That said, while companies collect data, they lack the infrastructure to store it without degradation. With every passing day, they incur a data debt they’ll have to pay sooner or later with interest when they come around to using that data for business prospects.

Let’s explore how to manage data debt in your organization to reduce potential costs in the future.

1. Prioritize Data Quality Management

2. Relate Data Debt to Business Impact and ROI

3. Track Ownership and Data Lineage

4. Catalog All Data

5. Collaborate With Upstream and Downstream Teams

6. Report on Progress and Drive Accountability

1. Prioritize data quality management

Implementing solid data governance policies that emphasize data quality is crucial for modern organizations. As a start, companies should consider:

• Setting clear standards: Establish guidelines for data entry, conduct regular audits, and use automated tools to assess data quality. 
• Promoting data literacy: According to a 2024 report by TDWI, 52% of respondents share that their company has no training related to data quality. By investing in data literacy initiatives, companies can get ahead of their competition. 
• Cultivating a data-centric culture: Instill the importance of high-quality data management into your brand ethos. This way, every employee recognizes their role in maintaining data integrity. 

2. Relate Data Debt to business impact and ROI

To prevent data debt from getting out of control, assess how data debt affects business operations and return on investment. 

To start, identify critical data assets that influence customer experience, revenue generation, and key processes. Similarly, quantify the costs of poor data quality, such as lost sales or operational inefficiencies. 

Once that’s done, develop a scoring system that weighs a data asset’s importance against its current quality to decide which data assets require immediate attention and resources. 

3. Track ownership and Data Lineage

Establishing clear ownership of data products and maintaining detailed data lineage records are crucial for responsible data management. By tracking your data's origin and transformation path, you can better understand the full impact of changes and ensure data integrity across your organization. 

To implement effective data ownership and provenance practices, focus on:

• Assigning data ownership: Create a comprehensive data ownership matrix, mapping each asset to its designated owner with clear responsibilities and authority.
• Implementing lineage tracking: Deploy a data lineage tool that integrates with your existing infrastructure to capture and update data flow information automatically.
• Enforcing governance policies: Establish a framework with clear data handling standards, regular audits of data pipelines, and integration of data journey analysis into change management procedures.

4. Catalog all data

Maintaining a thorough data catalog is crucial for organizations with complex data infrastructure. With it, they can document all their data assets, including their location, sensitivity, and ownership. 

To create a comprehensive catalog, start with a thorough inventory of all data sources across the organization. It’ll include structured data in databases and data warehouses and unstructured data in cloud storage, document repositories, and individual spreadsheets. 

Beyond that, for each data asset, note down the essential metadata, such as the data’s physical or virtual location, its sensitivity (public, internal, or restricted), and responsible team members. 

A data catalog includes information on each data asset. 

While time-consuming, the effort put into creating a well-structured data catalog pays significant dividends as it enhances data discovery, supports compliance, and encourages effective data usage across the organization. 

Alternatively, simplify documentation by using a platform that leverages generative AI to automate the discovery and documentation of data assets, ensuring they are kept up-to-date.

5. Collaborate with upstream and downstream teams

Encourage communication and collaboration among teams that manage source systems and those that use data products. This ensures alignment and minimizes the risk of disruptions when making changes. 

Beyond that, create a change notification system that alerts affected teams about planned data modifications two weeks in advance. Before you implement changes, work with the affected teams to assess the impact on their systems and identify potential issues early on.

6. Report on progress and drive accountability

Develop specific, measurable key performance indicators (KPIs) for data debt reduction. Examples include a percentage of datasets fully documented, the number of data errors identified and resolved, and improvement in data quality scores.

Create a dashboard that visualizes these key metrics over time. Share this dashboard with stakeholders across the organization to maintain transparency and focus on data debt management. 

Reducing Data Debt

In addition to implementing the six key initiatives to manage data debt, companies can also look into solutions for reducing the data debt altogether. For instance, data practitioners can look for tools to:

• Eliminate duplicate data in their system
• Centralize their data infrastructure
• Improve data quality on entry
• Enable collaboration between data teams
• Automate data transformation and cleansing processes

While that might appear to be a long list, Matillion offers all that and more with its Data Productivity Cloud, so you can focus on using data instead of cleaning it.