How to Get Started with Non-Traditional Data Conversion

By: John Jones


I’ve spent several years on ERP Implementations as a Data Workstream Lead. So, whenever I think of data conversion projects, I immediately think of these traditional types of projects:  moving data from a legacy system to ERP, consolidating disparate ERP instances to a single global instance, or moving from on-premise ERP to cloud ERP.

If you’ve done one (or all) of the above, you know that there is a somewhat standard methodology or playbook to follow to ensure a successful conversion. But if you happen to find yourself venturing beyond these traditional types of conversions, data projects start to come in many shapes and sizes, and very often there isn’t truly an effective playbook to follow. You’re on your own because, in truth, your conversion looks nothing like any other.

When faced with such non-traditional data conversions, where do you start?   

1. Ask the Right Questions

Any successful data conversion depends on proper planning, no matter how exotic the subject matter. Getting your arms around the conversion solution needs to start with defining the boundaries of the project. 

You can plan your project by asking a variety of questions to define these boundaries. These include:

  • What kind of data needs to be converted?
  • What is the quality of data and its availability? Does it require full or partial conversion?
  • Which data should be moved to the target system or database?
  • Which data should not be moved?
  • What kinds of formats are needed for data conversion? 
  • What is the original data format and what is the final format?
  • What are the data conversion standards to be used, if any, for the successful completion of data conversion projects?
  • What are the guidelines for the process?
  • What would be the tentative duration of the project?
  • How frequently do you need to carry out the data conversion?

You may have other questions that you need to ask as well. The key is paying attention to the particulars of your project and understanding what you need to know, who can provide the answers, and how those answers align with your project goals.

2. Ensure Business Engagement

You cannot be successful without business engagement and support on a data conversion project, be it traditional or non-traditional. To that end, start getting the support that you need from business owners and subject matter experts who can explain where the data comes from, where it is used, and how it is used. These will be the folks who can answer those questions above.

Next, identify the key business stakeholders who are supporting or sponsoring the project who will need to be aware of project status. Make it a priority to engage them through regular communication.

Identify the key business users or functional groups that will be needed to help with testing and/or will be impacted by cutover-related activities (data freezes, black-out periods). Also, identify the internal audit and compliance resources (e.g. Quality Assurance or Total Quality) that will need to review and sign off on process or procedure documentation and testing results. And don’t forget to prepare the business for any dual-data maintenance during the period of extraction from source(s) to start of use in the target environment(s). 

Yes, there are a lot of moving pieces and parts in this step, but building out processes for business engagement will help that data conversion go more smoothly as you keep everyone who’s invested in the project apprised of what’s happening.

3. Implement Data Rules and Standards

Defining and implementing business rules, data standards, and data quality metrics that are rooted in business processes helps to ensure consistency between the source and target systems. 

Make sure you and your team agree on standards and metrics with key project and business stakeholders. Leverage existing data standards and metrics where you can. It’s important to have a way to measure and report on the quality of the data as you complete your data readiness activities.

4. Ensure Data Readiness

This is the main focus of your data conversion effort.  It’s an opportunity to learn as much as you can about the data you’re working with, address any potential risks or issues, and ensure 100% accuracy of the data to be converted.

The four keys of data readiness are: 

  • Data Mapping: Document the relationship between the source and target systems.  Include any transformations, calculations, cross-references, and lookups. Identify key fields based on target system design requirements.
  • Data Profiling: Determine whether key data conforms to particular standards or patterns.  Use this information to establish business rules for data quality analysis and reporting.
  • Data Analysis: Apply business rules to the data, generate data quality metrics, and review with business data owners and subject matter experts.  Agree on data cleansing requirements.
  • Data Cleansing: Bring the data into alignment with the business rules. Use data quality metrics based on business rules to measure progress. Perform data conversion testing to validate data readiness.

5. Plan for Data Management and Data Governance

In preparation for cutover, think about how data will be managed post-go-live in the target system. Establishing the right data management strategy will ensure the long-term success of your project.  It’s not just about a successful go-live, it’s about making sure that business processes and systems that rely on the converted data function as or better than expected now and in the future.

Here are questions to consider moving forward.  

  • What data definitions, standards, processes, and procedures are needed?
  • How will data be maintained (e.g. centralized, federated, decentralized)?
  • What are your data maintenance resource requirements?
  • What are your data quality reporting requirements?
  • How will data errors be addressed (issue resolution process)? 
  • What data governance and data quality tools are required?

Don’t make the all-too-common mistake of not putting attention on how your data will be managed and governed in the future, otherwise you waste all that work you did to convert the data.

Conclusion

Except for those few traditional “cookie-cutter” ERP implementations, most data conversion projects are one of a kind and have their own unique set of challenges. There isn’t always a set playbook to follow. Sometimes you’re armed with little more than intuition and experimental nature. Throwing pasta to the wall and seeing what sticks.

There may be failures along the way. You should count on them, in fact; as the saying goes, fail fast so you learn fast. But if you follow the steps captured above, you should be able to customize them to the specific needs of your non-traditional data conversion project.