You prevent reports from changing or disappearing after a contact center migration by making a full inventory of all existing reports, KPI definitions, and data sources before the migration, and by making clear agreements on how historical data is transferred and validated. Without that preparation, you run the risk that management information will be incorrect, missing, or calculated differently than expected after go-live.
This applies to any organization switching to a new contact center platform, regardless of the scale of the migration. The questions below provide you with a concrete framework, from risk assessment to post-go-live validation.
Which reports are most at risk during a contact center migration?
Reports most at risk during a contact center migration are those dependent on platform-specific data structures, such as wait time reports, service level reports, and agent performance measurements. These are often deeply intertwined with the internal logic of the old system and are not automatically translated correctly to a new platform.
Specifically, these are the categories you should give extra attention to:
- Real-time dashboards that pull live data from the old system and require a new data source after migration
- Historical trend reports where comparability over time is essential for capacity planning or quality monitoring
- Combined reports that merge data from multiple systems, such as CRM, telephony, and chat
- Manual reports that employees compile themselves based on exports, as these are often poorly documented
- Compliance reports used for audits or regulators where discrepancies have direct consequences
Reports that live purely in a BI tool or external data warehouse are less vulnerable, provided the underlying data feed is correctly continued after migration. But even then: check whether field names, aggregation levels, and time zone configurations match what the new platform provides.
Why do KPI definitions change after a platform switch?
KPI definitions change after a platform switch because each contact center platform has its own way of calculating metrics. What one system calls a “handled call,” another only counts once the after-call work is completed. These technical differences are not visible in the report names, but they are in the figures.
A few common causes of KPI drift after migration:
- Different definition of service level: some platforms measure time to the first ringtone, others only from the moment an agent becomes available
- Deviating calculation of Average Handle Time (AHT): including or excluding after-call work, queue wait time, or transfer time
- How abandoned calls are counted: hanging up immediately versus hanging up after a certain number of seconds
- Time zone settings: reports running on UTC in the new system while the old system used local time
The result is that you see lower or higher KPI scores after the migration without the actual performance having changed. This undermines trust in data and makes management more difficult. Therefore, before the migration, record how each KPI is calculated in the current system and test that against the definition in the new platform.
How do you make a full inventory of existing reports?
You make a full inventory of existing reports by systematically collecting all reports used by or for the contact center, including informal exports and manual overviews. Involve not only the administrators but also the daily users, as they know which reports are actually used and which haven’t been opened for years.
Follow these steps:
- Collect all reports from the current system, including scheduled email reports, dashboards, and ad-hoc queries
- Add manual reports that employees keep themselves in Excel or other tools
- Document per report: name, owner, frequency, users, data source, KPI definitions, and purpose
- Prioritize based on usage and impact: which reports are business-critical, which are rarely consulted?
- Mark dependencies: which reports are input for other processes, decisions, or external parties?
A practical tool is a reporting register in a simple spreadsheet. That register will later become the basis for your migration planning and validation checklist. Keep it manageable: focus first on the reports used daily or weekly.
What needs to be arranged before historical data is migrated?
Before historical data is migrated, you need to know which data you want to keep, in what format the new platform expects that data, and how you will check the integrity of the transferred data. Without that preparation, you migrate data that is not readable, comparable, or complete in the new system.
Ensure the following matters are arranged:
- Data retention policy: how many months or years of historical data do you want to take with you, and what is legally required?
- Data format and field mapping: can the fields and values from the old system be translated to the structure of the new platform?
- Archiving strategy for non-migratable data: not all historical data fits in the new system; determine what will be archived and how it remains accessible
- Privacy check: does the data migration comply with GDPR, and is there data that may not be transferred?
- Test migration: perform a trial migration with a limited dataset before transferring the full historical data
A common mistake is to arrange historical data only at the last minute. Include this in the project planning as a separate work package with its own lead time, as data quality and privacy checks take more time than expected.
How do you validate reports after the new system goes live?
After go-live, you validate reports by comparing the outcomes of the new system in parallel with the outcomes of the old system or a reliable reference for an agreed period. Only when the deviations fall within an acceptable margin do you switch completely to the new reports.
A workable approach for validation:
- Set a parallel period of at least two weeks during which both systems are active and reports are kept side-by-side
- Define acceptable deviations per KPI before you start, so you can objectively assess whether a difference is an error or an expected result of a different definition
- Test on multiple levels: totals at organizational level, per team, per channel, and per time unit
- Involve end users in the validation, as they recognize deviations that are technically difficult to detect
- Document deviations and their cause, even if they are acceptable, so you can later explain why figures before and after the migration differ
Also ensure you request formal approval from the owners of business-critical reports before closing down the old system. This prevents later discussions about whether the data is correct.
When is it better to rebuild reports than to migrate them?
It is better to rebuild reports than to migrate them when the existing reports have grown around limitations of the old system, no longer align with current management needs, or have become so complex that migration poses more risk than a fresh start. Migration is not an end in itself if the result is a digital copy of an outdated report.
Situations where rebuilding is the better choice:
- The report contains workarounds or correction factors that compensated for shortcomings of the old platform
- No one remembers why certain calculations were set up that way, and there is no documentation
- The new platform offers native reporting capabilities that make the existing report redundant
- The report is rarely used and the build time is lower than the migration time
- The management information need has changed and the existing report no longer aligns with it
Use the inventory you made earlier to make a conscious choice per report: migrate, rebuild, or discontinue. This three-way split makes the scope manageable and prevents you from putting energy into preserving reports that no one needs anymore.
How Pegamento helps preserve reports after a contact center migration
We guide organizations through the entire migration process, from the initial inventory to post-go-live validation. In doing so, we ensure that reports and historical data are not lost or changed unnoticed. What we specifically arrange for you:
- Report inventory and prioritization: we map out all existing reports, including informal exports and manual overviews, and determine with you what will be migrated, rebuilt, or phased out
- KPI mapping: we document how each KPI is defined in the current system and test that against the definitions in the new platform, so you continue to compare apples to apples after the migration
- Data migration management: we handle the technical and organizational preparation for historical data migration, including privacy checks and archiving strategy
- Parallel validation: we guide the parallel period after go-live and assess with you whether reports are functioning correctly before the old system is closed
- Single point of contact for the total package: from contact center technology to omnichannel communication, you don’t have to switch between multiple suppliers
We work with a smart combination of proven modules—no costly custom work, but tailored solutions with standard building blocks that align with your processes and systems. This way, you maintain control over your data, your reports, and your management information, even after a major platform switch.
Want to know how we can guide your migration without data loss or reporting issues? Contact us and we’ll be happy to think along with you.
Frequently Asked Questions
How long does a typical parallel validation period last and what if the old system is already closed?
A parallel validation period ideally lasts two to four weeks, depending on the complexity of your reports and the number of channels you manage. If the old system is already closed before validation has taken place, you can fall back on exported snapshots or archived data as a reference — provided you secured those before the migration. This is exactly why recording a baseline measurement before go-live must be a mandatory part of your migration plan, not an optional step.
What do you do if end users complain after the migration that the figures 'aren't right', but you can't find a technical error?
This is often a signal of KPI drift: the figures are technically correct but calculated differently than users are accustomed to. The solution is not to recalculate, but to explain — document per KPI what has changed in the definition and actively communicate this to all users. Also, create a transition document with a comparison of the old and new calculation methods, so team leaders and managers have the context to interpret reports correctly.
What information should you record at a minimum per report in a reporting register?
Record at least the following information per report: name and description, owner and user group, reporting frequency, data source(s), KPIs used with their definitions, the purpose of the report, and any dependencies with other processes or systems. Also add a column for the migration decision — migrate, rebuild, or discontinue — so the register serves directly as a planning tool during the migration process.
How do you handle reports that combine data from the new contact center platform and from systems that are not being migrated, such as a CRM?
Combined reports require a separate approach: map out per report which data feed comes from which system, and test each feed individually after the migration before validating the combined output. Pay specific attention to field names, unique keys used for the link (such as customer ID or call ID), and time zone configurations, as small deviations in these cause incorrect merges that are difficult to detect in the final report.
Is it wise to revise the reporting structure immediately during the migration, or is it better to migrate one-to-one first?
The recommended approach is to migrate business-critical reports one-to-one first and only optimize afterward — simultaneously changing the platform and reporting structure increases the risk of errors and makes validation significantly more difficult. Use the inventory to make a clear distinction: what must work immediately after go-live, and what can be improved or redesigned in a second phase. This phased approach keeps the migration manageable and data trust intact.
Which GDPR points of attention are specifically relevant when migrating historical contact center data?
When migrating historical contact center data, you must take into account retention periods per data type — call recordings often may not be kept longer than strictly necessary — and the question of whether personal data may be transferred at all to a new platform with potentially a different processing location. Also check whether the new supplier has a valid processing agreement and whether the data migration itself, including any interim storage, complies with GDPR. Involve your Data Protection Officer (DPO) early on, so a privacy check does not cause delays just before go-live.
How do you involve team leaders and operational managers in the migration of reports without overloading them with technical details?
Involve team leaders and managers at the level relevant to them: have them validate the inventory by confirming which reports they actually use for management, and explicitly ask for their approval during post-go-live validation. Translate technical findings — such as a changed AHT definition — into operational impact: what does this mean for targets and how should they read the figures from now on? This way, they become owners of the reports in the new system instead of skeptical users who distrust the data.
