It seems that everyone in the data industry is talking about Data Trust as essential for effectively developing AI and analytics products. We all understand that poor or inaccurate data leads to poor or inaccurate analytics and AI models. So, let’s get to work monitoring, testing, and fixing all the data we have.
Wait, that’s impossible. Doing this would require the data team to focus solely on cleaning data instead of building new data products.
It feels like we’re at a dead end.
This reminds me of the long-standing idea in the data industry of documenting data assets. It sounded like a great idea, but it failed because data teams are primarily there to build new products, not to maintain existing documentation. So, what should we do?
The Concept of Data Debt
The key to tackling this issue is to clear your Data Debt. But what is Data Debt?
Many tables, jobs, and dashboards that were developed in the past are no longer in use. Based on our extensive work with clients across various industries, Forrester has found that 70% of all data within organizations goes unused. We have found the typical reasons for this usually include:
These might include:
- Dashboards that haven’t been viewed in months
- Data pipelines feeding into obsolete data products
- Tables storing information for discontinued products or services
- Duplicate datasets created for one-off analyses and never cleaned up
The real issue is that these assets continue to be maintained, monitored, and even “improved” as part of broader data quality initiatives. This means that a significant portion of your data team’s effort – potentially up to 30% – is being expended on assets that provide little to no value to your organization.
The Hidden Costs of Data Debt
The implications of carrying substantial Data Debt extend far beyond just wasted effort.
Consider these hidden costs:
- Increased complexity: Unnecessary data assets make your data ecosystem more complex, making it harder to navigate and understand.
- Reduced agility: Time spent maintaining obsolete assets is time not spent on innovative new projects.
- Higher infrastructure costs: Storing and processing unnecessary data inflates your cloud computing bills.
- Decreased data team morale: Constantly working on low-value tasks can lead to frustration and burnout among your data professionals.
- Misplaced trust: Ironically, by maintaining these unnecessary assets, we may inadvertently be placing trust in the wrong data, leading to misguided decisions.
The Path Forward to Ensuring Data Trust: Clearing Data Debt
Before we can meaningfully address Data Trust, we must first clear our Data Debt. This isn’t a one-time project but rather an ongoing process that should become part of your organization’s data DNA.
Here’s seven steps you can undertake to eliminate your Data Debt:
- Conduct a comprehensive data asset audit: Identify all your data assets, including tables, pipelines, dashboards, and reports.
- Implement usage tracking: Use tools to monitor which assets are actually being used, how often, and by whom.
- Prioritize assets for review: Focus on those with low usage or those that haven’t been updated in a long time.
- Engage stakeholders: Consult with business users to understand if seemingly unused assets still hold value.
- Develop a cleansing process: Create a standardized approach for safely decommissioning unnecessary assets.
- Foster a culture of data minimalism: Encourage your team to regularly question the ongoing value of data assets they manage.
- Automate where possible: Implement tools that can automatically flag potentially obsolete assets for review.
Seemore Data: From Data Debt to Data Trust
At Seemore Data, we’ve developed sophisticated tools and methodologies to help organizations quickly identify and eliminate their Data Debt. Our platform provides visibility into data lineage and usage patterns, helps prioritize assets for review, and actively suggests step-by-step optimization strategies.
But more than just tools, we offer a partnership to help instill a culture of continuous data optimization. We believe that maintaining a lean, high-value data ecosystem is an ongoing process, not a one-time cleanup effort.
Only after addressing your Data Debt can you meaningfully engage with the concept of Data Trust. By focusing your resources on the data that truly matters, you can:
- Improve the overall quality of your most important data assets
- Free up resources for innovative new data projects
- Increase the agility and effectiveness of your data team
- Build genuine trust in your data among stakeholders
In the end, the path to true Data Trust doesn’t start with trying to fix everything. It starts with having the courage to let go of what no longer serves your organization. By clearing your Data Debt, you’re not just cleaning house — you’re paving the way for a more trustworthy, efficient, and valuable data ecosystem.
At Seemore Data, we’re committed to helping organizations navigate this journey. Because in the world of data, less truly can be more.
Are you interested in continuing this discussion directly with Ariel? You can message him at ariel@seemoredata.io to delve deeper into how to ensure Data Debt is not standing in the way of achieving Data Trust in your organization.
