How to Avoid Common Challenges during Data Reconciliation and Migration

Organizations often have multiple business units, usually in different geographical locations, with each unit holding several applications. Each of these units sends and receives loads of data. Master data management applications consolidate a master record from several source systems. The solutions then offer the business end-users a single source of truth (a master record). MDM eliminates duplicate data and harmonizes the contents of master data, including descriptions, templates, manufacturer's names and part numbers in materials catalogues, first and last names, mailing addresses, phone numbers, and email addresses in counterparties catalogues. Additionally, MDM includes controls to prevent false reference data from being inserted into the system.

If your company is experiencing trouble with data centralization and migration, which is a common problem for growing enterprises, this article can serve as a starting point to find a successful solution.

Challenges in Data Migration

Enterprise data migration from one system to another is never easy, especially when you have to transfer millions of records between them. These are a few of the difficulties you can have during the procedure.

1. Complex Source Data

When source data is complex, which is usually the case, it presents one of the major difficulties in data transfer. To resolve any complexities, you'll typically need to prepare data migration tables that describe each data field and reflect the complexity of the future integration processes on the source data.

2. Data Loss or Corruption

Some of your data could get lost or corrupted during the migration process. Ensure you confirm that the source and migrated data match. This requires extensive data validation and testing. The process should include an assessment of events that could cause data corruption or loss.

3. Data Reconciliation

Typically the same data is scattered across several systems and business units, it is critical to cross-check each record against all systems, identify the master record and attach all the same records to a single record. This task requires special tools, proven approach and analytical skills, becoming a major challange to many organizations.

Why Data Reconciliation Is Important

In the master data management process, reconciliation plays a key role in the verification of data. This is critical before the data migration stage, since the organization must compare source data with target data to guarantee that the migration process is correct.

Lack of proper data reconciliation will cause

  • Missing records and values
  • Incorrect data
  • Duplications
  • Wrongly formatted data
  • Broken relationships within the database.

How to reconcile Materials catalogues

(1) Data mapping and reconciliation

  • Extract the defining attribute(s) in the records of two catalogues - catalog number and / or combinations of key characteristics
  • Standardize the selected attributes (replacement of characters with standard and other actions)
  • Compare selected and standardized attributes - if the attributes from different catalogues are equal, these records are the same and fall into category (2), the rest - category (3)

(2) Records found in target catalogues validate

  • A more complete record between target and legacy systems is identified and set as primary
  • The corresponding record in target system is completed based on legacy system to become primary, where applicable

(3) Records not found in target catalogue – add new

  • Normalization of records in accordance with approved data cleansing rules and standards document and
  • Migration of cleansed records as new records into target system

Data Migration Strategies for Successful Business

Before data migration, it is important to develop an effective strategy as it saves you costs and averts downtime. You want to keep your business running. At this point, you should think about any limits as well as the overall scope of the data migration process. While there are multiple aspects to consider, most strategies align into two categories, big bang or trickle.

a. "Big Bang" Migration

A big bang data migration completes the whole transfer in a short period. Data is processed through the extract, transform, and load process and sent to the new database.

The appeal of this strategy is, of course, that everything happens in a single time-boxed event that takes very little time to complete. However, the strain might be tremendous because the company functions with one of its resources unavailable. This puts the implementation in danger.

b. "Trickle" Migration

In contrast, trickle migrations complete the migration procedure in stages. During installation, the old and new systems function in tandem, eliminating downtime or operational disruptions. Real-time processes can keep the data migration indefinite.

In comparison to the big bang technique, these implementations might be somewhat sophisticated in terms of design. However, if done correctly, the increased complexity generally decreases rather than increases risks.

How to Successfully Conduct Data Migration

Follow these steps to carry out a data transfer strategy successfully:

(a) Evaluate Your Systems Source

You must understand the data you are migrating before you initiate the process. This stage is essential to learn how the data will integrate into the target system. Learn the type of data being pulled over and how much of it there is.

For example, data with several fields may exist; some of these fields may not need to be mapped to the destination system. Run an audit on the raw data included, in addition to fulfilling the requirements for data fields to be transmitted. If there are several errors, reconsider the migration.

(b) Plan and Establish the Migration Process

You should consider whether to migrate in a huge explosion or a trickle throughout the design process. You may start defining deadlines and any project problems after taking into account the design, the data to transfer, and the destination system.

(c) Create a Migration Sequence

You may be tempted to develop a bare minimum migration solution. But it is important to follow the correct approach since you will only do it once. A common tactic is to break the data into subsets and build out one category at a time, followed by a test. If an organization is working on a particularly large migration, it might make sense to build and test in parallel.

(d) Perform a live test

After evaluating the application during the build step, the testing process is not complete. To confirm the quality of the execution and comprehensiveness of the data migration sequence, it is crucial to test the data migration design using actual data.

(e) Switching the Light On

Following final testing, implementation can start utilizing the planned approach.

(f) Audit

Set a procedure to audit the data once the deployment has gone live to confirm the integrity of the migration. To guarantee the best possible data management outcomes, you'll need to supervise ongoing improvements and keep an eye on the transferred data's quality.


If your organization is modernizing its system, switching to cloud-based services, or consolidating data, then data migration is imminent. It's a significant step that must be completed correctly to preserve the data's integrity.

Synopps can support you in data reconciliation and cleansing, as well as help you to streamline every stage of your data migration operation. Start your data migration procedure off right by considering the applications that will help you.

Successful cases of our master data services
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