As an organization, you collect personal data for all kinds of purposes, customer service, invoicing, marketing, support. But how do you ensure that you don’t keep this data too long, collect more than necessary, or let it become outdated?
And what do you do when a single set of data is used for multiple purposes?
This blog explains what your organization needs to do to manage personal data responsibly, and the role your systems and internal processes play.
⏳ How to Monitor Data Retention Periods
Personal data may not be kept indefinitely. You must define in advance how long each type of data is retained, depending on its purpose. Once the purpose expires, the data must be deleted, either automatically or manually.
What can you do?
- Set clear retention periods for each data type (e.g., 7 years for invoicing, 2 years for marketing)
- Make sure your systems flag data that’s becoming outdated
- Schedule cleanup rounds (e.g., quarterly)
- Document everything in your processing policy or register
👉 Keep what’s necessary, not what’s convenient.
✂️ How to Apply Data Minimization
Data minimization means: only collect the personal data you truly need. Don’t ask for a full date of birth if an age category is sufficient. Don’t store ID copies without a legal reason.
What can you do?
- Ask per form or process: Do we really need this field?
- Remove fields or storage moments that lack a clear purpose
- Instruct employees clearly on what’s necessary (and what’s not)
- Restrict access to sensitive fields, only for those who need it
👉 Less data = less risk and more trust.
✅ How to Keep Data Accurate and Up to Date
Incorrect or outdated personal data can cause errors, complaints, or reputational damage. That’s why you must ensure data is accurate, complete, and current.
What can you do?
- Ask customers to verify their data periodically (e.g., when logging in)
- Use input validation: check for valid email formats, known postal codes
- Log who changed what, and when
- Correct errors as soon as they’re reported
👉 Clean data = better service and fewer mistakes.
🧩 What If One Dataset Serves Multiple Purposes?
Sometimes, a single dataset is used for different goals. For example:
- A customer makes a purchase (goal: invoicing)
- And subscribes to your newsletter (goal: marketing)
In such cases, you must:
- Define retention periods per purpose
- Track which data is used for which goal
- When one purpose expires (e.g., unsubscribing from the newsletter), only delete the related data, not the entire dataset if other purposes still apply
👉 Smart system use and good documentation are essential.
🛠️ Software and Structure: Essential for Personal Data Management
Your systems and internal processes must work together to support responsible data handling.
Your software should:
- Flag retention deadlines automatically
- Tag data by purpose
- Support access controls, data cleanup, and logging
Your internal organization (AO/IC) should:
- Document who monitors what
- Assign clear roles and responsibilities
- Periodically check whether policy is followed
👉 Without proper systems and structure, data management will fail.
✅ In Summary: What to Put in Place
| 🧩 What to manage | 💡 How to handle it |
|---|---|
| Retention periods | Define per purpose, enable system alerts |
| Data minimization | Only collect what’s truly necessary |
| Data accuracy and relevance | Use validation, logging, regular updates |
| Multiple data purposes | Tag by goal, don’t delete too early |
| Software + Internal governance | Assign roles, align systems with policies |
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