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Customer data integration (CDI) or, more broadly, customer-centric master data management (MDM) solutions are gaining significant momentum, largely because of their ability to help organizations achieve critical cross-functional business imperatives to bolster customer profitability, reduce operational costs, and adhere to regulatory compliance. Companies have come to realize they can't achieve these cross-functional business imperatives without adopting customer-centricity throughout their business processes.
However, many IT departments are finding it difficult to persuade business to take the MDM plunge. Often they don't know how to get started to build a compelling business case - or their choice of architecture limits the return on investment making it difficult to deliver on the business case.
Chasing the Pot of Gold
Some companies choose to build a business case for a grandiose end state: an operational customer hub with a single, comprehensive MDM platform capable of supporting both analytical and operational processes in real-time. There is nothing wrong with this vision or its business potential. The problem is IT often selects a "big" architecture for this big vision: a persistent transaction hub with a fixed data model requiring significant custom programming and up to four years to implement (an investment akin to a large ERP implementation). Therefore, this big-bang integration makes it difficult to prove the value of business investment to users along the way.
For this approach to be feasible, the associated business case is only justified if you are thinking of replacing your company's entire IT infrastructure in the near term. As a result, this approach can be afforded only by a select few companies and is offered usually by mega vendors like IBM who promise a comprehensive MDM platform. In reality, this architecture is delivered on a patchwork of platforms and tools and lacks integration.
Quick Hit and Then What?
The other approach is to generate a business case for a single business function, say, marketing, through fast-to-deploy "registry" style architecture. Unlike the big bang implementation, this is a valid business approach and is easier to sell to senior management. However, the problem is this architecture may not be adequate to support the long-term economic case for a customer-centric MDM platform across the enterprise - and may result in yet another customer data silo.
Light-weight "registry" style CDI hubs are used to match data entities, and offer fast implementation with the promise of fast time to value. Hubs that use this approach tend to employ narrow data models that contain only the selective attributes needed to match similar records across multiple data sources - and then link these matched records to create a customer identity master store. Often, companies explore this approach as it can be quick to implement, has a low total cost of ownership, and is perceived as low risk.
While this approach addresses some of the over-arching data quality issues such as de-duplication for marketing processes, it typically will not fully address more complicated business usages such as compliance or privacy issues where full reconciliation of data is required along with its associated history and lineage. Nor does this approach offer the best, reconciled view of customer master data since it lacks the resolution of conflicting records and the history of past changes. Therefore, while the business value of this approach is valid it is also limited to only a certain class of customer problems. As this architecture can not deliver full customer-centricity across the enterprise, it limits the business potential that can be realized over time.
Building a Business Case That Can Be Delivered
In order to build a long-term business case, IT professionals need to follow a systematic process for assessing the comprehensive ROI of a customer-centric MDM platform and also identity an architecture that can deliver this ROI in measurable stages. The key steps along this journey are:
- Identify the extent of the master data problem;
- Correlate the master data problem with a critical customer-centric business issue;
- Quantify a significant return on investment (ROI) in a prioritized order;
- If needed, determine the total cost of ownership (TCO) and trade-offs of building versus buying a platform;
- Identify an architecture that delivers on full ROI - in measurable phases.
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