As a part of preparation for a System's replacement initiative, the hustle around the data migration is generally in the context of what needs to be moved and how.
You are enlightened with various approaches, Big Bang or Trickle. You get into the know-how of Extract, Transform and Load (ETL). The key aspect of ETL, Transformation here focuses on, what is in the source system and how the destination accepts this.
This preparation is absolutely necessary, but is this sufficient? Is there a need for "beyond"?
Let's look at a company (John Doe Insurance, JDI) which tried something different as a part of their data migration and the outcome of this new approach.
JDI, serviced multiple LOBs - Life, P&C. They had two systems for P&C, and one for Life. They were now replacing their IT systems into a single Enterprise IT platform and started prepping for the same.
As a part of one of the brainstorming sessions, a Business Manager raised a question - "why can't we take this as an opportunity to clean our data? We know that there are a lot of issues and are unable to leverage it the way we want to - for better decisioning, operations, rates and servicing"
The team then realized, that this isn't just about moving the data from one system to another but this is the opportunity to improve their data health, as there's no better time than now. Ignoring the state of their current data today, will lead them into living with the same problems. They realized, this preparation has to be theirs and it cannot be left to the vendors.
Considering the magnitude of this exercise they have set up another team exclusively for this initiative, to work in parallel with the implementation team.
The data migration team, then re-defined their approach - Standard Vendor Defined Approach (What needs to be moved and how, following best practices for risk mitigation) plus New Additions for ensuring the healthy and interconnected data is moved to the new system.
The new additions, were,
- Decide on what to bring over and what to let go - They defined a clear strategy to determine "What is useful and what is useless." E.g.: In addition to ensuring that you meet requirements of regulatory bodies, audit committees, do not miss out the key data points on the customer, policy or claims that help you in better decisioning (fraudulent claims, blacklisted customer etc.)
- Review Data for Migration - Data was looked at in the context of Availability, Sufficiency & Quality and they identified a lot of errors
- unique identifier was not captured mandatorily, making it difficult to identify the common customers across LOBs
- lot of duplicate customer records were identified in the P&C system as there was no duplicate check
- there was not enough contact information captured to get into other modes of communication (like email, WhatsApp etc.)
- UW's had been insuring risks for a particular roof type that was no longer to be insured, all because this field was not a defined list but a free form text
- Plan of Action for corrections -
- Transformation Rules (for data to be migrated) - Determination of transformation logic for cleaning the data as a part of migration. E.g.: Identifying the phone numbers from email field and mapping them to the appropriate fields in the new system etc.
- Approach Definition (for missing data) - Identification of the right approach and method for collection of inconsistent / missing data. E.g.: Mass email communication for capture of missing information etc.
- Guidelines & Standardization of Rules - Definition of the information capture process best practices & rules in the new IT for ensuring the critical data points are captured mandatorily and in right format. E.g.: Making the Unique Identifier capture mandatory, adding duplicate check, defining data types - that accepts only email in email field etc.
The Going Beyond approach helped them clean their data and plan for further improving their data maturity so, they can make the right use of data for reaching their business goals.
To re-iterate, data migration preparation cannot just be in the context of what and how, it has to be beyond - for ensuring that the right and quality data is moved into the new system. This is 'the' opportunity for you to make it right.
You cannot continue to kick the can, after all, transformation has to be holistic for you to become a brand new.
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