The biggest data migration mistakes you can make

 So you’re tasked with your organization’s next big data migration. Maybe you’re moving to a new CRM system. Maybe you’ve just acquired another company and need to integrate their data into your system. Whatever the reason, data migrations are critical processes that many businesses go through. The continuing explosion in the volume of data businesses collect, store and use suggests that the trend of most companies engaging in some data migration project isn’t letting up anytime soon. According to a recent data migration study, 91% of companies engage in data migration projects. 

A data migration is defined as the process of transferring data between storage types, formats or computer systems. This is essential for system implementations and consolidations, basically anytime systems of data need to be joined together to moved to a new place. Most commonly, migrations happen when there is:

• A merger or acquisition
• A de-merger or buy-out
• System replacement
• System upgrade or new system purchase
• Regulatory changes

While the concept sounds simple enough, data migrations are typically full of challenges, ranging from communication issues to differing data standards and system designs. With the success of key organizational initiatives often riding on the success of a data migration, look out for these five common mistakes we’ve seen in data migrations.

1. Not collaborating. Not communicating or sharing knowledge across departments can be a huge issue. Many times this can be due to a lack of project scoping or not engaging the right business stakeholders.
2. Misaligning on data standards. Traditionally in a migration, organizations are trying to combine a large number of data sources; each source is likely stored, standardized, maintained and structured in a different way, resulting in a large degree of inconsistency.
3. Overlooking poor system design. A migration can fail because the system designed to house all the information is not fit for a given department’s purpose. In addition, the system may not be fit to manage and handle the types of information required due to poor scoping or a lack of understanding.
4. Bad data quality. Most businesses do not understand the full extent of their data quality issues, which means that undetected problems could cause rework or damage to the integrity of the data migrated.
5. Poor interpretation of business data rules. When rules are created without thought to the data or the business processes that will be using information, you can’t guarantee your data will be fit for your purpose.

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