In the realm of data engineering, the spotlight often falls on technological innovation and how to manage burgeoning datasets efficiently. Yet, a critical but less discussed aspect is 'data debt.'
What is Data Debt?
Data debt arises when shortcuts in data handling, such as collection, storage, and management, are taken. Driven by the urgency to produce results, compromises like embedding transformations directly into SQL, instead of utilising variables, are made. Although this might offer immediate time savings, it can lead to poorly structured datasets and overlooked documentation, causing long-term complications.
Real-World Impact
The repercussions of data debt are significant and widespread. While no one intentionally codes a server’s IP address only to change the production server pre-weekend with a Monday board meeting looming, such scenarios are not unheard of. This often results in teams spending excessive time deciphering value calculations and dependent data sources, causing project delays and data inaccuracies.
Strategies to Mitigate Data Debt
Documentation: While documenting data sources, transformations, and assumptions can seem burdensome, it's invaluable for managing complexity. Balancing the speed of development with adequate documentation is key. Encouraging new hires to contribute to documentation and code improvement can also be effective. This not only ensures the upkeep of your data processes but also fosters a learning environment.
Data Governance: Implementing a solid data governance framework is essential. This involves establishing clear policies and procedures for data management to minimise inconsistencies and inefficiencies, ultimately reducing data debt.
Regular Audits: Conducting regular audits is crucial. Surprisingly, some ETL/ELT processes yield inconsistent results upon repetition. Regular audits help identify data quality issues, code alterations, and state-dependent calculations, ensuring reliability and consistency in data processing.
Looking Ahead
As Joel Spolsky aptly put it, "code doesn't rust," but in our interconnected world, even code can 'rust.' Adapting swiftly is crucial, yet challenging when burdened with significant data debt.
One lingering question remains: Is managing data debt a technical challenge or a strategic business issue that requires proper budgeting and management?
Have you encountered similar challenges? How does your organisation address data debt?
