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Understanding the causes of data debt is crucial for preventing its proliferation. Let’s have a look at them:
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Outdated Data Structures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.
Fortunately, the antidote to controlling or even avoiding data debt lies in clean analytics tracking. Learn more about how to reduce and avoid data debt in this blog post.