There’s a word (actually two) for the time and money bad data is costing your data-driven company: data debt. Often misunderstood as another form of technical debt, data debt is, in fact, an entirely different beast, threatening to undermine all the trust you’ve put in the data-driven decisions that guide your business. And, as bad as it seems, and as Chad Sanderson very well said in one of his recent LinkedIn posts…

Data without quality isn't trustworthy.

The good news is that, the sooner an organization takes control of data quality, the better equipped it will be to reduce or even avoid data debt.

Data Debt vs. Technical Debt

To truly understand the gravity of data debt, it's essential to differentiate it from technical debt.

While many people see data debt as another form of technical debt, the truth is that data debt is far worse than technical debt.

Technical debt usually arises from quick fixes in software development, resulting in code that may not be optimized which eventually turns into scalability issues in the long run. Despite these inconveniences, technical debt generally does not compromise the core functionality of the application while, on the other way around, data debt strikes at the very heart of their users’ trust.

Quote Chad Sanderson

Unveiling the Real Costs of Data Debt

Data debt is a silent menace that plagues organizations in several ways. While we’ve already talked about the fatal consequences once distrust drips in your data, the direct and indirect costs and consequences can be equally damaging:

Loss of Productivity

Many product managers and data analysts spend a substantial amount of their working hours fixing bad data instead of leveraging it to drive their products forward. Indeed, Forrester estimates that the sprawling mess of data, coupled with the daunting task of cleaning it, can take up more than 40% of a data analyst’s time.

Related to this previous point, The New York Times has also noted that this usually leads to what data scientists call ‘data wrangling’, ‘data munging’, and ‘data janitor’ work, which forces them to spend from 50% to 80% of their time collecting and preparing unruly digital data before it can be used for strategic decision-making.

All in all, the time and effort spent rectifying data issues, coupled with the consequences of making decisions based on untrustworthy data, result in diverting resources away from more productive tasks, which ultimately limits an organization's ability to invest in innovation.

Data Debt: Its Root Causes

All the short-term data decisions you’re currently making or you’ve made in the past will make your future data much harder to understand, leverage, and, ultimately, trust. That is why understanding the causes of data debt is crucial for preventing its proliferation:

Lack of Data Governance

One of the primary causes of data debt is the lack of data governance. Data governance involves establishing policies and procedures for effective data management, encompassing data quality, data security, and data privacy. Without proper data governance, data becomes inconsistent, unreliable, and unprotected against ineffective data management and non-compliance.

Messy Analytics Tracking

Inaccurate and inconsistent analytics tracking is a significant contributor to data debt. Incomplete or incorrect tracking can lead to a jumbled mix of various event names and data elements, which necessitates both time and financial resources to decipher in order to align it to effectively analyze it.

Outdated Data Structures

Another cause that leads to data debt lies in the way data does not evolve at the same pace software products do. Yet, as we have mentioned before, all the short-term data decisions you make now will make your future data much harder to understand, leverage, and trust.

Data Silos

Data silos can also contribute to data debt by hindering data inconsistencies and inaccuracies that eventually become impossible to spot as not all members involved in the data collection process are able to see them and, thus, be on the same page unless they are on the same team.

Clean Analytics Tracking: The way to reduce and avoid data debt

Fortunately, the antidote to control or even avoid data debt lies in clean analytics tracking. Clean analytics tracking involves actively measuring the metrics that are relevant to your business, auditing data sources, and ensuring correct analytics implementations.

To get rid of your data debt and build trust in your information, the logic behind your analytics tracking needs to be checked out. That means creating a single source of data truth to be provided with a roadmap for every member involved in the data collection process to ensure the data of your digital analytics, acquisition, pixels, and campaigns are accurately collected, responsibly managed, and integrated efficiently across teams and platforms.

Trackingplan is a fully automated data QA and observability solution for your digital analytics created to ensure your data never breaks and always arrives to your specifications by automatically documenting all the data that your apps and websites are sending to third-party integrations like Google Analytics, Segment, or MixPanel.

This creates a single source of truth where all teams involved in first-party data collection can collaborate, and automatically receive notifications when things change or break to easily debug any problem by being provided with the root cause of the problems affecting your data integrity, even before they go into production.

You can try it out yourself here or ask for a demo at