Reduce the Risks in a Data Migration

It is inevitable. At some point every nonprofit organization will have to migrate their data from one system to another. Whether it is for an upgraded version of their current system or to an entirely new system, the reality is that technology evolves. Older data structures weren’t designed with newer system capabilities in mind, so they can become less efficient. Plus, over years of input and use by a variety of people with evolving processes, data integrity can drift from its optimal state. Eventually, all data storage can benefit from being examined, cleaned, and optimized. While beneficial, optimizing a data set and then preparing to return it to the existing or new system does present many risks. It’s possible to lose, disconnect, or even corrupt years of valuable constituent history. In fact, as many as 84% of data migrations projects fail to meet expectations. Why?

There are many reasons, from ineffective collaboration to not fully understanding business objectives. In many cases, there is a misunderstanding around the structures that govern the data, which can spell trouble when moving to a new arrangement. Add to that a failure to test, inconsistent data or inadequate handling of legacy systems and you’ve got the potential for drastic consequences.

How to Reduce the Risks

The act of migrating data is complex, especially during a more extensive system transition. Combining long disconnected data sets into an optimized state has to be done right the first time. Without the guidance of a data specialist, there are many pitfalls that can impact the organization for years. And unfortunately, if there is a failure of the data migration, it can appear as though an entire CRM or analytics initiative has failed to achieve its goals.

Heller Consulting has been working with nonprofits and their data and technology systems for 20 years and we’ve developed a uniquely effective methodology to minimize the risks during data migration. Here are some tips from our data specialists to help make the process run smoothly. Data migration is complex, but the effort spent on doing it right is well worth the time invested.

Step 1: Get the data

Obviously, the first step is to obtain the source data, but many times organizations fail to keep their data secure throughout their migration process. It’s essential to establish protocols to encrypt and protect the data so that only those who are allowed to work with the data can access it.

Step 2: Create a data map

The next step is to understand how it is organized and how it should be used. The data should be analyzed to measure record counts, usage frequency and the true number of fields, along with their content.

Step 3: Evaluate the data

The data map is key in determining the most important data to migrate and identifying which information might be problematic or useless. The goal is to build a database system that can be leveraged across different departments, increasing the utility of the data, and opening more opportunities to gain value. Errors and inconsistencies can be eliminated, and unused historical data can be archived to reduce data storage costs.

Step 4: The conversion process

The next step is to convert the data with detailed translation rules, testing conversions along the way to identify any issues that may not be noticeable in the basic data structure. At times, exceptions can point to deeper issues that must be addressed. It’s much easier to fix these before the system goes live rather than after.

Step 5: Document the structure

It’s essential to document all of the work done during the mapping and conversion process. This documentation provides an in-depth understanding of the system and the data included, providing a referenceable outline of the data structure that will be essential for future work.

Step 6: Delta load

Any changes made during the period between the extraction of data for the final conversion and when the new system is live must be prepared using the same steps & processes as before so that it can be correctly incorporated into the new database. This ensures that no data was lost during the system transition.

Conclusion

Data migration of any kind, but especially those involving a large body of historical information, can be a challenge. There are certain risks involved but with a specific methodology in place, it’s possible to follow a process that is consistent and reliable. Most importantly, don’t let data migration be a last-minute consideration.

This is a brief overview of our process. Find out more about how to manage your data in our paper In Focus: Reducing Risk During Data Migrations. If you have any questions or need additional guidance, we’re happy to share how data migration fits into the larger puzzle of a CRM initiative.

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