Data Retention Policy for Telemetry: How to Set One

A telemetry retention policy defines the lengths of time telemetry is kept, where it resides, and how it gets disposed of. Metrics, logs, traces, sensor data, and audit logs require different answers. Store everything and you waste money on storage costs and assume unnecessary risk. Store too little and you can't properly investigate incidents or satisfy auditors. Learn how to set retention periods, tier your storage and automate policies.

PODTECH Using Telemetry Data to Your Advantage

Telemetry retention helps teams care for critical infrastructure. PODTECH creates software solutions for data centers and intelligent building monitoring. Learn how your team can take advantage of telemetry retention to better manage data with PODTECH. www.podtech.com

TL;DR

Retain based on type of data, not source system. Operational statistics at high volume may retain for days at full resolution. Audit trails and compliance evidence may need to be kept for months or years.
Tier storage. Minimize the hot window for troubleshooting purposes. Move older data to lower-priced warm or cold storage tiers, aggregating it if possible.
Sample before you store. With tail-based sampling, only keep the traces you care about such as errors and slow requests, and drop the everyday noise.
Delete by design at ingestion. Segregate data, use immutable opaque IDs where possible, and automate disposal as well as deletion including from backups.
Write numbers, not intentions. If your policy states “as long as required” your teams will be guessing.

What a telemetry retention policy covers
Why there is no single right retention period
Classify telemetry before you set periods
Tiered storage: balance cost against visibility
Use sampling as a retention strategy
Audit records: where retention is not optional
Personal data in telemetry: keep it only as long as you can justify
How to write the policy: a seven-step process
A worked example: a mid-size data hall
Common mistakes
Our perspective
Build retention into your monitoring platform
FAQ
Sources

What a telemetry retention policy covers

Telemetry is machine data about the state of a system: metrics, logs, traces, events and, for physical infrastructure, sensor data. A retention policy defines how that stream should be handled:

What is collected and which category it belongs to.
How long each category stays searchable, and in which storage tier.
What happens at the end: deletion, aggregation into summaries, or archive.
Who owns the rule, and how exceptions such as legal holds are handled.

Generic retention guides address documents and email. Telemetry is different both in volume and velocity of value decay. That one-second temperature read may be mission critical during an incident and almost worthless half a year later. Who changed a set point may be important for years as an audit trail.

Why there is no single right retention period

Look at vendor defaults to see just how varied the answers can be. Datadog’s published defaults range from days to years depending on data type and product tier. This trend continues throughout the observability market. Microsoft logs real-time meeting telemetry for as little as 7 days on certain licenses, and longer for aggregated data. None of these retention periods is a standard you should replicate. They’re simply a reflection of each vendor’s storage costs.

Your own period should come from three questions:

Operational need: How far back do engineers look to diagnose a fault or compare against a baseline?
Obligation: What law, contract or standard dictates retention of evidence and for how long?
Liability: What would someone be able to do with this data if they got breached, and do you still need it?

Classify telemetry before you set periods

The Manning chapter on building policies for telemetry retention and aggregation hits the same theme repeated by most practitioners: retention should follow data type. Operational metrics can use short retention times such as days at full resolution, while audit logs or billing milestones can be kept for years.

A practical classification for a data centre or building estate looks like this.

Telemetry class	Examples	Typical purpose	Retention approach
High-frequency operational	Rack temperature, humidity, power draw, leak sensor polls	Live monitoring, fault diagnosis	Full resolution for a short hot window, then roll up
Aggregated trend	Hourly or daily averages, capacity and efficiency figures	Capacity planning, efficiency reporting	Months to years, low storage cost
Application traces and logs	Request traces, error logs	Debugging	Short hot window, sampled afterwards
Security and audit	Access events, configuration changes, alarm acknowledgements	Investigations, compliance	Defined by regulation and policy, usually years
Safety and incident records	Alarm history, near-miss events, inspection results	Regulatory proof, root-cause analysis	Tied to the applicable safety or contractual requirement

Legal, security and operations should all be at the table defining the periods. The most important thing is that there is an owner and class number for each period.

Tiered storage: balance cost against visibility

The usual structure has three layers:

Hot: searchable immediately. This data is used for things like live dashboards and troubleshooting. Practitioners typically store about 7 to 30 days worth here.
Warm or cold: less expensive and slower, intended for trend analysis and compliance. Trends can go back 12 months or more.
Aggregated: raw points are substituted with summaries, such as min, max and average for each interval. You lose granularity, but maintain the trend.

Two caveats: one, aggregation works in one direction. If you've rolled up one-second fire points into hourly averages, you can't go back and see a short spike later. Consider what resolution an investigation actually would require. Two, fetching data from cold storage takes time. If you need data to an auditor or incident team within hours, verify that your cold tier can respond that quickly.

Use sampling as a retention strategy

Sampling isn’t just a cost cheat code. It determines which data survives at all. With tail-based sampling in OpenTelemetry, the decision isn't made until after a trace finishes. This lets you keep every trace that has an error or high latency and throw away most of the healthy traces. The OpenTelemetry spec puts it like this: sampling is a lever that reduces the cost of collecting and processing telemetry.

Applied to retention policy, this means two rules can co-exist: retain routine traffic for a short time and at a low sampling rate, retain exceptions longer and in full. Write down both rules. When an auditor encounters a sampling rate with no documented reason, they will rightly wonder what got tossed.

Audit records: where retention is not optional

Security and audit telemetry is probably the type of telemetry most frequently regulated by external requirements. In US federal-aligned environments, NIST is the usual mapping. NIST SP 800-53 Control AU-11 requires organizations to store audit records for a certain period of time so that after-the-fact investigations and regulatory audits can be performed. Notice the wording “for a certain period of time”: they're not giving you that number. You have to decide it, justify it and document it.

AWS’ DevOps guidance makes a similar point about NIST 800-171: audit records must be created, protected and retained for the purpose of investigation and reporting. They also recommend automating retention by using lifecycle tools that log and audit disposal actions. Retention actions themselves should be traceable.

Where your industry is governed by specific regulations such as financial services, healthcare or fire and life safety codes, consult that regulator or standard and inquire of your counsel for that period. This article cannot provide that value for you.

Personal data in telemetry: keep it only as long as you can justify

Telemetry commonly includes personal data inadvertently: usernames from access logs, badge IDs, device IDs, IP addresses, location information from mobile apps, cameras or wearables on construction sites, and more. Storage limitation is probably your safest north star. “You,” says the UK regulator, “must not keep personal data longer than necessary.” You must be able to justify why you’re keeping it that long, and you must have a policy that defines your standard retention periods. That’s UK GDPR advice. The obligations vary from state to state, sector to sector in the US, so make sure you check what laws apply to you. But the lesson should still apply: scope the purpose, then scope the period.

Two design habits make deletion workable:

Use opaque identifiers that do not change. Many in the field advise having account and user IDs that are meaningless so you can delete at the subject level exactly. Log lines with free-text identifiers like names and email addresses make this nearly impossible.
Decide at ingest. KPMG’s retention and deletion guidance puts great emphasis on considering retention during design. Tag data by type and sensitivity at ingestion, so disposal can happen automatically and backups can be controlled in the same way.

How to write the policy: a seven-step process

Take inventory of your telemetry sources. This can consist of your BMS, PMS, NMS, sensors, application logs, security tools and third party platforms.
Place each source into one of the classes in the table above and indicate which contain personally identifiable or security sensitive information.
List out requirements. Document regulations, customer agreements, carrier mandates and internal audit needs by class.
Establish hot, warm and archive periods for each class based on the shortest period that meets both operational and obligation needs.
Define aggregation and sampling rules and the resolution kept at each stage.
Automate enforcement. TTLs, lifecycle rules, and scheduled jobs are better than manual housekeeping. Record each deletion.
Review regularly. Repeat this process when you add sensors, swap platforms, enter a new vertical or update your compliance process when regulations change.

Not sure what some of those terms mean? Check out our datacenter glossary for common definitions. BMS is defined here.

One thing the ICO’s guidance adds is on style. A well-written policy is prescriptive. It tells you what to keep and for how long, instead of repeating ambiguous legal wording that just confuses staff.

A worked example: a mid-size data hall

The numbers presented below are for example purposes only. They are not suggestions for your facility. Your legal, security and operations professionals determine your values.

Class	Hot	Warm / cold	Final action
Rack environmental sensors	14 days at full resolution	13 months, hourly aggregates	Delete raw after hot window; keep aggregates per policy
Power and cooling metrics	30 days	24 months, aggregated	Archive annual summaries
Access and badge logs	90 days	Per security policy	Delete or anonymise at end of period
Configuration change and alarm acknowledgement audit	90 days	Multi-year per audit requirement	Retain until the written period ends, then log the disposal

Notice what the table represents: each row has an actionable metric, long-term metric and a defined outcome. This is the absolute least that a policy can have for an auditor to test.

Common mistakes

Set and forget. If you create a policy at launch and never revisit it, the policy will quickly become out of sync with the system's real-world behavior.
Keeping raw data “just in case”. It increases cost and increases the potential impact of a breach. Outdated, sensitive information is a liability.
Forgetting backups. Erasing data from the live system while retaining it on backups for years violates the intent of the policy.
One period fits all. Too short for audit trails, too long for sensor jitter.
Missing documented destruction. If you can't prove deletion occurred, you can't prove compliance.
Overlooking physical-world telemetry. Building and safety systems data feeds incident investigations. Our resource covering NFPA compliant and NIST secure generator monitoring illustrates the importance of alarm and test records well beyond the day they were created.

Our perspective

We create software for data centres, buildings and construction sites. Retention decisions arise on virtually every project we work on. Our stance is straightforward: retention should be a design criterion, not a remediation afterthought.

The earliest and therefore cheapest time to determine how long you want readings to persist is before the first sensor is connected. That's when you have the luxury of picking the schema, identifiers, aggregation and storage tier together. Cleaning up afterwards, especially after years of data that wasn't classified on write, takes time and money. If your logs are stored by an older legacy system it can be worse because their logging systems commonly intermix operational data with security logs and personally identifiable information. For these reasons retention planning should be part of your integration and legacy system replacement projects, not after the fact.

We challenge our clients to consider telemetry across two time frames as well. Operations teams need fine detail for days worth of data. They want to know what a cooling loop was doing minute by minute before an alarm fired. Planning and compliance teams need coarse information that can cover years. A telemetry platform that meets both needs retains raw data for short periods, aggregates intentionally, and guards the audit trail. Our guide to telemetry tools explains how to assess platforms with that requirement in mind.

Finally write out the policy in numerals. If it can't be written as a lifecycle rule, then it's not a rule yet.

— Harry

Build retention into your monitoring platform

If retention rules are stored in a spreadsheet and telemetry comes from five siloed systems, you will not enforce those rules. PODTECH partners with data centre operators and infrastructure teams on telemetry, DCIM consulting and BMS and PMS integrations so that data collection, storage tiers and disposal are architected as a single system. View our datacenter services or read through a DCIM solutions case study to learn how we think about it.

FAQ

How long should telemetry data be retained?

It depends. Operational data is often maintained at highest resolution for days to weeks, then rolled up. Audit and compliance records are maintained for however long your contracts, policies or regulations require. Decide on a period for each bucket of data and document why.

What is the difference between hot and cold telemetry storage?

Hot storage is fast and searchable. This is where troubleshooting occurs while systems are live. Hot storage typically contains only the last few days or weeks. Cold storage is inexpensive and slow. Cold storage is used for older events that may be needed for trending and compliance, typically more than a year's worth of data. It's a balance of cost versus retrieval speed.

Does a retention policy have to include deletion?

Yes. If it only says what to retain, it is incomplete. It should say what happens at the end of each period: deletion, anonymisation or aggregation, and it should log that it did. For personal data you should be able to justify how long you retain it to regulators like the UK ICO.

How do I apply retention to data that contains personal information?

Label at ingestion, use aliases not names or email addresses, and store only for the shortest time you need for the declared reason. Review state, federal or sector regulations that apply to you and have your attorney or DPO validate the durations.