Housing organizations collect huge amounts of data about tenants, properties, repairs, and business operations. This data comes from formal systems (Housing Management, Repairs, Finance, HR), emails, spreadsheets, and documents. Making sure all this data is correct, up-to-date, and useful is a constant challenge.
This guide explains nine common data problems with examples from tenant records, property management, and repair jobs. While we suggest ideal fixes, we know that many organizations can't change their main computer systems. That's where a Data Quality System like Project Overwatch helps - it can spot and track these problems even when your main systems can't be updated to prevent the issues occuring in the first place.
Sometimes important information is missing from records. This could be:
Ideal fix: Set up your computer systems to require important information before saving a record. Train staff to check for missing details and give them context as to why the information is needed.
When you can't change your main system: A Data Quality System can scan your data on a regular basis, create reports of missing information, and send alerts to the right teams for follow-up. Speed is important because other processes may depend on this information to work correctly.
The same information might be stored differently across multiple systems:
Why this happens: Information gets entered at different times but there is no automatic update between systems, or the information is captured for one purpose and then used for something else without checking it is still correct (e.g. temporary phone numbers for a one-off repair).
Ideal fix: Use one main database where possible. Set up automatic data sharing between systems. Bring data into a data warehouse for reconcilation and push updates back to source systems.
When systems can't be integrated: A Data Quality System can compare information across different systems, highlight conflicts, and track which discrepancies have been resolved.
Sometimes individual pieces of information look OK, but they don't work together:
Ideal fix: Set up system checks that compare related information when data is entered.
When validation rules can't be added: A Data Quality System can run these checks regularly on existing data, flag conflicts for review, and track resolution time and trends.
Some information might be technically possible but very unusual:
Ideal fix: Build review workflows into your main systems with supervisor approval for unusual cases.
When workflow changes aren't possible: A Data Quality System can flag unusual cases for review, allow staff to mark them as "confirmed correct," and suppress future alerts for verified exceptions.
Data records connect to each other. When you change one record, it might make another record incorrect or break a business rule:
Ideal fix: Set up automatic rules that update related records when changes are made.
When automatic rules can't be implemented: A Data Quality System can identify broken relationships between records and create cleanup reports for manual review.
Some information has dates attached. What was correct yesterday might be wrong today:
Ideal fix: Set up automatic status updates and deadline alerts in your main systems.
When automatic updates aren't available: A Data Quality System can run frequent checks for date-related problems and send alerts to the right teams when action is needed.
Life moves on and we don't always get to hear about it. There are many examples of data that is collected once but needs a periodic review to ensure it is still correct. Often your systems will have a capture or change date but won't be able to tell you the last time that informations was verified if it didn't change:
Ideal fix: Schedule regular check-ins and set automatic review reminders in your systems.
When regular reviews can't be automated: A Data Quality System can identify records that haven't been updated for set periods and generate review lists for different teams. It can act as an external control to log that the review has been completed and suppress the warning for a period of time.
Laws and the the business environment change over time. Information you used to collect might now be:
Ideal fix: Build compliance checks into your systems that automatically flag or remove outdated data.
When systems can't be updated: A Data Quality System can identify data that may no longer comply with current regulations and create action plans for review and cleanup across multiple systems.
Some information is only needed for a specific task. Once that task is done, the information should be deleted:
Ideal fix: Set up automatic deletion schedules and workflows in your main systems.
When automatic cleanup isn't possible: A Data Quality System can identify temporary information that should be removed, create deletion schedules, and track compliance with data retention policies.
Remember: Many organizations can't significantly modify their main housing, repair, or financial systems. A dedicated Data Quality System like Project Overwatch works alongside your existing systems to identify, track, and help resolve these common data problems without requiring expensive system changes.