Master Data Management Implementation Checklist
Iter’s Master Data Management Implementation Checklist:
- You must have senior management (ideally executive) support and organisation buy-in. Without this you won’t be able to push through any transformation requirements or hold any vendors to task for delivery. You will need them to support you at board and to manage governance
- Understand what would make the work that you are doing here a success. Why do you specifically need MDM, and when must it be done by. It’s important to start to consider what you think the cost will be and what benefits or savings are you anticipating so that you can start to develop a plan of where that return of investment will come from including what is changeable and what can’t be changed;
- Strategy and policies – what strategies and/or policies are needed, and which already exist which may need to change If you don’t have a data strategy, it would be helpful to consider developing one and remember not to start with an outdated business strategy.
- Start to consider and maintain the opportunities and threats logs
- Third party requirements – the software to deliver these solutions can be expensive, have lead times or need infrastructure to deliver (new or changes)
- You need to start to consider and record what functionality is required to deliver the solution – what must be done, what would add value and what can be ignored (needed for tool selection). What should be delivered by the tools (to staff, citizens etc) or the application
- When considering data, you need to understand what data is to be mastered and what data will link to the mastered data to make up your warehouse. What domains are you interested in and will the mastering be phased or big bang? Are you mastering internal system data or including data from external sources such as social media? In addition, when considering governance for mastering purposes or warehousing and reporting will most likely need new staff or at least relocation and reskilling staff meaning likely organisation and/or structure requirements/changes in teams such as BAU, governance, existing cleansing. MDM, data architecture, managing and forecasting using big data is all an overhead so staff costs both for initial cleansing phase and for BAU need to be considered and included in the overall business plan.
- Metrics, reporting and forecasting. Current reports, data volumes forecasting opportunities and general reporting requirements need to be considered and factored into any reporting costs, as well as business impact analysis. Also include information such as how many clients; how many access via customer access/own login; how many likely to opt out of any data sharing agreement. You must understand at this stage what does success look like, and what you can afford to drop/not include in the initial phase
- Aside from considering the organisations security policy, you must consider where you want the data kept; do the data sharing statements reflect any new use; what happens when people (customers?) opt out or when their data is deleted (retention policy) etc. This latter is particularly important in respect of master data management. What are the ethical, privacy and legal issues likely to be.
If you’d like to discuss the issues raised in this list and/or hear about Iter’s expertise, please contact email@example.com