Data Debt: The Silent Killer of Data-Driven Organizations

Image by Isaac — Big Data Debt

Have you heard of data debt? It’s a term used to describe the hidden costs and inefficiencies that arise when organizations accumulate technical debt related to their data management practices. Similar to technical debt in software development, data debt results from shortcuts and compromises made in data management infrastructure to meet short-term goals. Data debt can arise from a variety of sources, including outdated data structures, poorly documented data sources, inefficient data processing pipelines, and bad optimized data storage practices.

Data debt arises when organizations take shortcuts or make compromises in their data infrastructure in order to meet short-term goals or respond to changing business requirements. For example, a company may adopt a new data pipeline or database schema quickly in order to meet a specific business need, without fully documenting or testing the system. Over time, these shortcuts and compromises can accumulate, leading to a situation where the data infrastructure becomes difficult to maintain, modify, and scale.

Michel Tricot, cofounder and CEO of Airbyte, says, “Debt is not bad. However, debt needs to be repaid, which should be the focus because important decisions will be made with the data.”

Dont Accumulate Data Debt

There are several factors that contribute to data debt. These include:

1. Lack of documentation: When data infrastructure is not properly documented, it can be difficult for new team members to understand how data is processed, transformed, and stored. This can lead to misunderstandings and errors, as well as making it difficult to make changes or updates to the system.
2. Complex data pipelines: Data pipelines that are overly complex, with many inter dependencies and custom code, can be difficult to understand and maintain. This can lead to errors and inconsistencies, as well as making it difficult to scale the system.
3. Poor data quality: When data is not properly validated, cleaned, or transformed, it can lead to inaccuracies and inconsistencies in reporting and analysis. This can undermine confidence in the data, as well as making it difficult to make decisions based on the data.
4. Lack of ownership: When ownership of data infrastructure is unclear, it can be difficult to make changes or updates without stepping on someone else’s toes. This can lead to conflicts and delays, as well as making it difficult to maintain the system over time.
5. Technical debt in underlying systems: Finally, data debt can be exacerbated by technical debt in underlying systems, such as databases or cloud infrastructure. For example, if a company is using a database that is poorly designed or not optimized for their use case, it can lead to performance issues and other problems that make it difficult to scale the data infrastructure.

Tough to clean your data debt

Data debt can have several negative consequences for a company, including:

1. Increased risk of errors: Data debt can make it difficult to ensure data accuracy and consistency, leading to errors and inconsistencies in reporting and analysis.
2. Reduced agility: Data debt can make it difficult to make changes to data pipelines or database schemas, reducing the agility of the data team.
3. Decreased productivity: The lack of documentation and ownership in data systems can result in wasted time and effort as team members struggle to understand how data is processed and stored.

So,how we can address ? Is there any way where can tackle data debt ? Of course, there are.

Reducing data debt requires a disciplined and proactive approach to data management. This can involve a variety of practices, including:

Assessing Data Debt: Understanding the Purpose and Value of Your Data

Before addressing data debt, it is important to assess whether you have it in the first place. Not all data can be considered debt. To determine whether you have data debt, you need to understand how your data contributes to your organization’s overall goals. This involves assessing what data you have, how it is collected, who uses it, how they use it, and what actions they take based on the data. It is crucial to evaluate whether the collected data is being used effectively or if it is just sitting idle. If you discover that a particular data source is not serving any useful purpose, it can be considered data debt and should be removed to minimize the overall data debt

Measuring Data Debt: Steps to Gauge Severity, Impact, and Cost of Data Debt

Once you have identified the data debt in your organization, it is important to evaluate it thoroughly. Evaluating data debt involves understanding the severity of the debt, its impact on the organization, and the cost of addressing it. Here are some steps you can take to evaluate your data debt:

1. Identify the severity of the debt: You need to determine the severity of the data debt by evaluating the impact it has on your organization. This involves looking at how the debt affects data quality, efficiency, productivity, and profitability.
2. Determine the impact on the organization: Understanding the impact of the data debt on your organization can help you prioritize which debt to address first. Consider the impact of the data debt on various departments, teams, and processes within your organization.
3. Assess the cost of addressing the debt: Assessing the cost of addressing the data debt involves understanding the resources required to remediate the issue. This includes the cost of labor, tools, and technology required to fix the problem.
4. Prioritize the data debt: Once you have evaluated the severity, impact, and cost of the data debt, you can prioritize which debt to address first. It is important to focus on addressing the most critical and impactful data debt first, to ensure the greatest return on investment.

Adopting a Data Modeling Tool

A data modeling tool such as dbt can help modularize and document data pipelines and database schemas. dbt offers a modular approach to data modeling, allowing users to break down complex pipelines into smaller, more manageable units. With dbt, companies can also document their data pipelines, making it easier to understand the data flow and ensuring that changes are properly documented.

Implementing a Data Governance Framework

Implementing a data governance framework ensures ownership and accountability for data-related systems. Data governance policies and procedures can help ensure that data is managed consistently and effectively across the organization. This can include policies on data access, data security, and data quality assurance.

Joseph Rutakangwa, cofounder and CEO of Rwazi, says having data governance technologies in place can help. “Data catalogs, data lineage tools, and metadata management systems can help organizations manage and track data sources, data models, and data lineage, which can reduce the risk of data debt,” he says. “Data quality tools, such as data profiling and data cleansing tools, can help identify and address issues with data quality, which can help to prevent the introduction of poor-quality data into the data model and reduce the risk of data debt.” He also recommends, “Designate data stewardship roles, such as data architects, data analysts, and data engineers.” He says, “Assigning roles helps to maintain data models, ensure data is accurate, and address issues to minimize data debt.”

Sasha Grujicic, president of NowVertical, adds solutions such as ”standardizing data visualizations, removing unused reports, defining data definitions, implementing data catalogs that alert teams when things need documentation, and instituting data quality procedures.Organizations can identify and outline the proper data governance structure by adopting a top-down strategy and building a scalable system to support current and future inputs. For most companies, decreasing data debt will reduce risk, lower costs, increase productivity, and establish a foundation for growth for years to come”

Prioritizing Data Quality

Prioritizing data quality is essential in addressing data debt. Companies can invest in data validation and cleaning processes to ensure that data is accurate and complete. This can include automated tools for data cleaning and validation, as well as manual processes to review and correct data. By prioritizing data quality, companies can ensure that the data they rely on for decision-making is trustworthy and accurate.

Muralidharan K, cofounder and CEO of Saturam, building the data quality product qualdo.ai says “Enterprise decision with no data better than the decisions with the bad data. The identity of the enterprise is not shaped by the amount of data but by how much of the data that is understood and ready for use” stressing the importance of data quality.

Tricot, cofounder and CEO of Airbyte says, “Determine the level of trust you have in the data using cataloging tools and looking at how many data explorations and production reports rely on specific pieces of data.”

Although higher usage levels of data sources can suggest trust, it is not the sole indicator. It is crucial for dataops and governance teams to evaluate the quality of the data by measuring accuracy, completeness, consistency, timeliness, uniqueness, and validity metrics. Additionally, data leaders should conduct surveys of both leaders and users to develop a data satisfaction score, which would reflect their confidence in the data, reports, and predictions.

Creating a Culture of Documentation

Creating a culture of documentation is essential in reducing data debt. This means making documentation a core part of the data team’s workflow and providing tools and resources to make it easy to document data-related systems. This can include creating templates for documentation, setting expectations for documentation quality, and providing training and support to help teams document their work effectively.

In Conclusion, addressing data debt requires a disciplined approach to data management that prioritizes documentation, modularization, ownership, and quality. In order to remain competitive and make data-driven decisions, organizations must proactively address the issue of data debt.By adopting best practices in these areas, companies can reduce data debt and mitigate its negative impact on their operations and decision-making.

Lets not fall into data debt !!

Happy Data !!

-Thanks to dataquality.camp community where the topic of data debt was spoken and it triggered me to explore on data debt and how we can address