Set Data Retention Rules for Product Analytics and Logs Without Losing Insight

We drew the line at 90 days for raw event-level logs and kept indefinite retention only for aggregated, anonymized usage patterns. Anything tied to an identifiable customer record past that window gets rolled up into cohort-level metrics—no individual click trails, no raw session replays sitting around for a year just in case someone wants to dig through them later.

The rule of thumb that made this easy to enforce: if a support or product decision hasn't needed the raw data within 90 days, it's not going to need it at 180. We tested that assumption against a full quarter of tickets before committing to the policy, and it held. Product learning didn't suffer either, because trend data and feature adoption curves survive the rollup fine. You only lose the ability to reconstruct one specific user's exact click path from six months back, which almost nobody was actually using for anything besides curiosity.

The privacy upside compounds too. Shorter retention windows mean a smaller blast radius if there's ever a breach, and they make data deletion requests trivial instead of a fire drill across a dozen log tables.

We delete all customer data that's more than one full year old, full stop. For ongoing clients, we'll offer them a copy of the data before deleting it. Older data is really just a liability. We have to pay to store it, we virtually never have reason to access it, and it's a major cybersecurity risk.

One of the best rules I've seen for putting lines on retention schedules is forcing very tight alignment between the primary deletion schedules and the deletion schedules of infrastructure backups. Frequently, someone will decide that customer logs of product behavior should be deleted after 90 days to minimize privacy exposure, and then someone in infrastructure will back up the system and retain the backups for a year.

A retention schedule only limits a company when legal/engineering are tightly aligned in enforcing the same schedules for backups. The first step in reconciling a conflict like this is to actually put those boundaries in place—the legal side argues that you should retain longer for defensibility, the engineering side argues that you should retain shorter for system performance—and then you define tight data buckets and then define what schedules apply.

Another thing that's incredibly important is defining very precise restore procedures that respect the deletion of data in the normal course of business. When people restore systems after an event, it's common that there will be backups hanging around that don't have the right deletion schedule and will restore customer data that shouldn't be held anymore.

Defining where the backup schedules differ from the active system schedules is necessary; otherwise, you'll get customer data back in the system.

Split the question in two, because most teams answer it as one. At VolRadar, an options and volatility analytics platform, the data that makes the product work is end-of-day market data — public, non-personal, and worth keeping permanently, since an IV Rank percentile means nothing without years of history behind it. User-level logs are the opposite: they answer a question once, then only accumulate risk. So the retention rule I work to is that market history is permanent, and user-level detail lives only as long as the specific question it was collected to answer. Most "we need more logs" requests are really one aggregate somebody could have computed and stored instead.

Retention lines hold when they are set per data type rather than per system, and that one change is what fixed this for most of the teams I work with. Analytics and logs grow because nobody owns the delete. I act as named DPO for tech companies across the EU, UK and US, and the rule I give them is to aggregate early and keep the raw event only as long as you would genuinely re-run an analysis on it.

In practice that splits into three buckets: raw event logs carrying an identifier, which are the risky ones and which almost nobody queries past 90 days; aggregated metrics with no identifier, which you can keep indefinitely because they stopped being personal data the moment you dropped the join key; and security or audit logs, which have their own justification and their own longer clock.

The decision that made the difference was asking product teams to name the last time they had actually queried raw events older than the line they were defending. Usually nobody could. Retention arguments are mostly about the fear of not having something rather than about an analysis anyone ran, and once people have to name the query, the number comes down on its own.

Then write the clock into the pipeline instead of a policy document, so deletion happens whether or not anyone remembers, and pseudonymize at ingest so the useful shape of the data survives the delete and the person does not.

A five-year log store is one breach away from being a five-year disclosure, and under GDPR you have to justify every month of it in writing to a regulator who will ask what you used it for.

My rule of thumb is to keep the events that still help explain customer behavior, not every event just because it exists.

In product analytics, more data can actually make things harder if you're storing huge volumes of low-value interactions. What matters is whether an event helps answer a useful question later: what did the customer view, what did they add to cart, where did they drop out, what did they buy, did they return it?

At Stormly, we focus on retaining the behavioral events that preserve that story. That gives us enough history to understand changes over time without treating every click, request, or technical log as equally valuable.

For me, the retention line is simple: if an event no longer improves the quality of a future decision, there's little reason to keep it.

Maurice Sikkink
CTO & Co-Founder, Stormly
https://www.stormly.com

I keep only the customer data needed to produce the product signals we actually use and avoid persisting raw logs when they can be re-fetched. That practice came from replacing a roughly $300-per-month prompt-tracking subscription with an internal tool that cost about $20 to operate; because our team knew how to access the relevant APIs, we stored minimal derived signals and accessed raw prompts as needed. Rule of thumb: if you can rehydrate data from a trusted API, do not keep the raw record longer than the window needed to compute its signal. This preserved product learning while cutting the amount of sensitive data we host and lowering privacy exposure.