If you’re reading this article, it’s probably because your financial institution has adopted a new accounting platform for your trust or IRA — and you’re involved in the data conversion.
Now, you’re fretting about how the company is going to get all the information from the old system into the new one.
Have no fear.
Countless administrators and managers have successfully navigated data conversions before you, dating back to the days when handwritten ledgers transitioned to analog devices and then from analog devices to the digital age.
With the right information, you can anticipate challenges and plan for success.
Leveraging a well-defined Trust or IRA data conversion process is crucial. Otherwise, data silos in your organization are unavoidable when you are using multiple applications to create, analyze, or store your data.
Data conversions require a plan, a collaborative culture, and a keen eye on opportunities to improve processes. You can set yourself up for success by following this basic work outline developed by all the people who have come before you.
Breaking Down a Standard Data Conversion Process
1. Identify Data Sources
- Review where the data within current source systems to identify information that might be missing or inconsistent. Identifying outliers early is key to optimal data transformation and transfer.
- Ask yourself: Are records generally complete? Are there fields which have been used inconsistently over time?
2. Identify Data Destinations
- Create a map to guide the transfer of source data to the new destination database and identify related data transformation needs.
- Create the database systems into which the transformed data will be loaded.
3. Transform Data
- Task your engineers to do the heads-down work of translating the data from the source system into an optimized format that will work with the new system.
- Understand that automated data validation rules ensure that all data is entered into fields with appropriate integrity and formatting – just as if you it was performed by hand. (For example, no text in date fields.)
4. Data Load and Review
- Load, review, and verify the integrity of your data in a safe, ready-to-use test environment.
- Correct any identified discrepancies, repeating the transformation and loading process as necessary so that you are 100% confident when accessing your production environment for the first time.
What’s Next? Why Data Conversions are So Important
Data conversion is a critical step in helping your organization adopt new technology. Proper data conversion is critical because so many business functions depend on that data.
Good data helps your people serve your customers and grow your business. The data transformation process is about enablement, finding efficiencies, and serving all respective areas of your business.
A data conversion might seem daunting at first, but like so many other operations, you can reduce your anxiety and achieve success if you do a little bit of planning and then execute your plan one step at a time.