In the world of digital transformation, data is king. But migrating your valuable data from legacy systems to a new platform like ServiceNow can be a complex and daunting task. At Oblytech, we understand how much data integrity and clean integration matter. Our Data Migration & Integration services aim for a smooth and secure transition of your data to ServiceNow, helping your organization make full use of this platform.
We provide ongoing data management and support services to help keep your ServiceNow data accurate, up-to-date, and accessible.
For complex integration scenarios, we develop custom solutions to ensure smooth data exchange between ServiceNow and your legacy systems.
We integrate ServiceNow with your existing applications and systems, such as ERP, CRM, HR, and other critical business tools, to create a unified and streamlined IT ecosystem.
We use established tools and methodologies to migrate your data securely and efficiently to ServiceNow, planning the work to keep downtime low and business continuity in view.
We begin by thoroughly analyzing your existing data sources, identifying data quality issues, inconsistencies, and redundancies. We then develop a detailed migration plan, outlining the scope, timeline, and resources required for a smooth transition.
We clean, standardize, and reshape your data to ensure its accuracy, completeness, and compatibility with ServiceNow’s data model.
A data migration moves records from a source system into ServiceNow once, or in a planned set of loads. An integration keeps records moving between ServiceNow and other systems over time. Projects often describe both as data work and then discover that the decisions differ. A migration asks what the target should contain on the day it goes live and how the cut-over will be managed. An integration asks which system owns each piece of information, how changes are passed in each direction, and what happens when the other system is unavailable. Treating them as one workstream usually means the migration is designed around the integration’s needs, or the integration inherits a data set nobody checked.
This page is for the program lead, the data owner and the platform owner who must decide how information will move in and out of the platform. It explains the choices involved, the evidence that should exist before a load runs, and where the work tends to fail.
The first decision is whether the information needs to be moved once, kept in step, or both. Some data is historical and needs to arrive only once, with a clear record of what was loaded. Other data is operational and must stay current in both systems until one of them is retired. The table sets out the common cases and what each one usually needs.
| Information type | Usual pattern | What must be decided before the work starts | Main risk |
|---|---|---|---|
| Historical records kept for reference | One-off load into a read-friendly structure | Which fields are kept, and how old records are labeled | Old values displayed as current ones |
| Live reference data, such as locations or service catalogs | Ongoing synchronization in one direction | Which system is the source of truth for each attribute | Two systems editing the same field |
| Transactional records that are open at cut-over | Migration with a freeze and a reconciliation step | How open items are closed, moved or restarted | Work lost or duplicated in the gap |
| Records shared with a finance or HR system | Scheduled exchange with error handling | Who resolves a rejected record, and how quickly | Silent failures that surface weeks later |
The source-of-truth decision is the one most often left vague. Two systems that both appear to own a field are likely to disagree about it, and the disagreement is often found by a user at the worst moment.
A migration is not finished when the load completes. It is finished when someone can show that the target contains what it should, with the exceptions explained. A reconciliation plan defines the checks before the load runs, so that nobody is deciding afterwards what counts as success. The checks should answer three questions: did every intended record arrive, did its key fields arrive intact, and were any records rejected, transformed or deliberately left out, with the reason recorded for each.
In many migrations, much of the effort sits in transformation rather than in the movement itself. Source data uses codes, formats and conventions that the target does not share. Dates are stored differently, names are spelled inconsistently, and some fields carry more than one meaning. The transformation rules turn the source into the target’s model, and they are where data quality problems are either fixed or passed through.
Write the rules down as a mapping that a data owner can read, with an example for each rule that is not obvious. Keep the rules separate from the load scripts, so that a business person can review the decisions without reading code. A rule that cannot be explained to the data owner is probably a rule that will be wrong for some records, and those records will be found after go-live.
An integration fails in ways a migration does not. The connection may succeed while the data is wrong, or the other system may be down for hours without anyone noticing. A sound design answers four questions in advance. Who owns each side of the connection? What happens to a message that cannot be processed? How will someone find out that the connection has stopped delivering? And how will the two systems be brought back into agreement after an outage?
The answers should be written into the design, with names attached. The monitoring should alert a person, not only write to a log that nobody reads. Retry rules should be explicit about how many attempts are made and what happens after the last one. An integration without these answers works until the first unusual day and then becomes a source of disputes between teams.
Usually only what the business needs to keep working. Historical data that nobody searches is often better kept in an archive with a clear route to retrieve it, than loaded into the live platform where it will slow reporting and confuse users.
Choose a date when the volume of open work is low and the people who can resolve exceptions are available. A date chosen only for calendar convenience often lands on a period of peak activity, and the reconciliation then competes with normal work.
Each failed category needs an owner and a decision. Some records can be corrected and reloaded, some should be accepted as exceptions with a note, and some should be excluded deliberately. Leaving the failures in a log with no owner means they will be discovered by users.
Partly, using test data and a stand-in for the other system. The final test must use the real system, because the stand-in will not reproduce its timing, its error messages or its data quality.
A productive first conversation starts with a list of the source systems, a rough sense of the record types in each, and the date by which the target must be live. Add the name of the data owner for each source, and note which systems are expected to be retired. Those facts decide whether the work is a single migration, a set of integrations, or a sequence of both. The number of sources, the quality of the data and the number of decisions owners must make all change the effort.
Related pages cover the platform side of this work. The data migration, reconciliation and cut-over page goes deeper on the load and its checks, and the data governance and source authority page covers the ownership decisions. For the connection side, the enterprise platform integrations page and the integration monitoring and error ownership page deal with the connection side, including monitoring and who owns errors. If a particular source system is worrying you, you can discuss your data migration plan.
A ServiceNow data migration runs in a fixed order: profile the source, transform the data to the target model, load it into a non-production instance, reconcile the result, rehearse the whole run, then freeze the source and run the final load. The rehearsal is the step that shows whether the cut-over plan works. Teams that skip it usually find the problems on the night the live load is due.
Profiling comes first because the source is almost always messier than its owners expect. Counting records, finding blank mandatory fields and listing duplicate keys shows how much transformation work exists before any mapping is written. The output is a short list of defects, each with a named owner.
Most ServiceNow loads pass through a staging step before records reach the live tables. That gives you a place to see each row as it will be written, and to reject bad rows without touching production data. The mechanics are covered in how import sets stage records before they reach target tables. Keep the transform rules readable enough for the data owner to check line by line.
Rehearsals are where the sequence earns its keep. Run the full load against a copy of the target instance, reconcile it, fix what failed, and run it again. Each pass should produce the same counts and the same exception log. If the results change depending on who runs the load, the process is not yet repeatable. Timing matters as well: a rehearsal that runs longer than the cut-over window allows cannot serve as the plan.
The freeze is the step people underestimate. Once the final extract starts, edits made in the old system will not reach ServiceNow unless someone captures them. Agree the freeze time with the business, announce it, and lock edits on the source at that point rather than relying on people to stop. The exception work that follows is covered in how reconciliation and cutover are planned.
A rehearsal is ready to become the live run when these conditions hold:
Guides from the Oblytech ServiceNow library that explain the practice behind this page.