Context
Recently my wife received a message from our erstwhile realtor that some mail had gone to our prior residential address which we sold 2 years ago. When we picked up our mail – I noticed that the correspondence was from a reputed global bank where we had held a retirement savings account – and closed in the prior year. What was surprising was that I had received correspondence for the same account confirming the closure of the account at my new address thereafter.
Naturally I was a bit perplexed. But given experience with other back offices of large, global service provider organizations – seemed clear that there had been some issue in synchronization of our new address across systems at the bank.
Needless to say – it still did leave a bit of a bad taste as well as reduced respect for the institution which has claimed to be a leader in automation.
Managing master data continues to be an issue at most organizations – services or manufacturing. There are many reasons that come to mind – ineffective data migration from one system to another; mergers and acquisitions and related systems consolidation; etc.
The cost of inefficiencies is not small – in fact there is a whole class of data integration solutions including a ‘magic quadrant’ by Gartner.
While digitization and micro-services continue to drive industrialization in data management – an effective operating model and organization, measurement of quality and performance of master data management services are equally important.
Call to action
A holistic approach to industrialize master data management as a service will need to cover a number of elements. For example:
1. Structural drivers
– Service journeys and catalog – creating visibility of master data management objects (e.g., customers, suppliers, price lists, manufacturing recipes/ bill of materials, employees) and stakeholder touch points (re.g., registration/ onboarding, maintenance, renewal and re-certifications)
– Target operating model and governance – roles of stakeholders around engagement/ relationship management, center of expertise, operational execution/ service delivery; location and coverage models (e.g., 24×7 for critical services, self-service); escalation model
– Process harmonization and managing master data journeys – taking a stakeholder back view of processes as end to end journeys through an organizations internal functions; optimizing for response times, first time right and resiliency/ back-up
2. Performance drivers
– Automation – from basic automation (eg RPA and workflows) to more real-time micro-services
– Performance measurement – efficiency, effectiveness and stakeholder experience metrics and review cycles; closed loop end user service experience feedback
– Capabilities and behaviors – making it all come together will need new capabilities, e.g., design thinking, agile delivery methods for rapid, iterative service enhancements
Epilogue
Going back to my own experience with the bank above – what could have improved my experience? Perhaps a simple email alerting me of the upcoming document delivery and letting me confirm/ update my address via a web portal? The end user oriented approach and implications of incorrect contact details – might have headed off this event.
Shared Services Imagineers 