Quality and Innovation

Money loses value over time – and data depreciates too. This erosion of data value occurs through multiple channels, each diminishing the utility and reliability of information as time passes.

One of the most insidious ways data loses value is through the loss of context and institutional knowledge. As team members move on or memories fade, the subtle understanding of how data was collected, what various fields truly represent, and why certain methodological choices were made begins to evaporate. What seemed obvious when data was fresh becomes increasingly opaque, leaving future analysts to make educated guesses about critical details.

Data integrity also degrades over time. Storage systems fail, file formats become obsolete, and meanings transmogrify. While modern systems typically include safeguards, the probability of some form of corruption increases with each passing year. More importantly, the ability to verify data accuracy can often depend on now-unavailable source systems or contemporaneous records that don’t exist.

The relationship between data and decision making is time-sensitive. Business conditions evolve rapidly, and data from six months ago may reflect a market reality that no longer exists. Customer preferences shift, competitors enter and exit, and regulatory landscapes transform. The insights derived from older data can become not just less valuable, but potentially misleading if applied without careful consideration of changed circumstances.

Quick access to relevant data enables organizations to make decisions based on current conditions rather than historical snapshots. When data pipelines introduce significant delays, decision-makers must either act on stale information or proceed without data support. Neither option is ideal, and both can lead to suboptimal outcomes.

The temporal value decay of data underscores the importance of robust documentation, automated testing, and careful consideration of data retention policies. Organizations must balance the costs of maintaining historical data against its diminishing utility. While some data remains valuable for long-term trend analysis or compliance requirements, much of it becomes progressively less useful for operational decision making. Understanding this depreciation helps inform both technical architecture and business strategy.

What can you do about it?

• Make documentation mandatory: Don’t let anyone add or change data without explaining what it means. Think of it like labeling containers in your fridge – if you don’t write what’s inside and when it’s from, it becomes mystery food that nobody will trust.
• Do regular data checkups: Every few months, review your important data and ask basic questions like “Do we still know what this means?” and “Who understands this stuff?” It’s like checking your pantry for expired items before they become unusable.
• Label data with expiration dates: Decide how long different types of data stay useful. Some data (like sales numbers) might be crucial for a month but less important after a year. This helps you know what to focus on keeping fresh and what can be archived.

While it might be tempting to hold on to data, we also have to be practical: when are you really going to revisit that report from 1997? By being realistic and starting from the premise of depreciating data, we can save ourselves and others time, money, and stress for years to come.