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Originally published 14 December 2006
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 begin 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 the 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 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, for example marketing, through a 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 because 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 cannot 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 identify an architecture that can deliver this ROI in measurable stages. The key steps along this journey are:
Identify the Problem
While many organizations realize they have a master data problem, the majority do not know how to analyze their master data and identify the exact amount of duplicate, invalid, conflicting and untrustworthy data. In addition, most IT departments struggle with how to quantify or articulate how the master data problem is hindering a specific business process. To address these issues, companies should first conduct a “data life cycle audit” to determine existing data issues and the associated benefits of implementing a customer-centric MDM solution. More specifically, a data life cycle audit should comprise:
A data life cycle audit will identify the extent of an organization’s unclean data, deliver a list of quantifiable benefits associated with having reliable data and outline an ongoing process for data management. After all, ROI cannot be measured if you do not have an understanding of how the improvement of your unreliable master data can affect your organization and, more importantly, how various business processes can be improved.
Quantify the Value
Now more than ever, companies are required to determine ROI before embarking on an IT project, especially when faced with competing priorities. Once they have completed the data life cycle audit, companies can then conduct an “MDM ROI analysis” to develop a thorough cost-benefit analysis of using a customer-centric MDM solution. More specifically, an ROI audit will quantify the value of probable benefits and estimate the initial required investments and ongoing cost of managing a MDM solution. The analysis will also enable you to determine the potential hard and soft cost savings by implementing a customer-centric MDM solution.
Determine the Total Cost of Ownership
Other considerations to achieving business ROI include the ongoing total cost of ownership of the platform and other risk factors such as changing requirements – all of which can differ vastly depending upon each MDM vendor’s specific approach and platform. Often, a full “build versus buy” analysis is needed to determine the solution that delivers the lowest total cost of ownership. Such an analysis should tie into the ROI work and not be considered a standalone exercise.
The Golden Mean: Pay As You Go with an Adaptive Architecture
The next question is how to deliver on the full estimated ROI across an enterprise. Fortunately, there is an alternative to choosing between a fast implementation with limited business benefit and a full enterprise MDM platform with illusive ROI. Today, organizations can embrace an adaptive and evolutionary approach that allows them to start with a specific business case for a defined customer-centric business process to deliver rapid ROI, and then adapt the approach over time across the organization to implement a comprehensive MDM platform. This “pay as you go” approach contains the initial project costs and associated risks while demonstrating a fast ROI to further support adapting the platform to other business processes and data entities.
The foundation of this approach requires an adaptive MDM platform that supports a range of CDI/MDM architectural styles and is not locked into a single one. While such a platform may begin with “registry style” architecture to accurately identify customers across multiple interactions, it should also adapt to a full transaction hub that can authoritatively reconcile and synchronize the customer-centric master data across systems. Most importantly, this platform should coexist with multiple legacy hubs and external data sources, and evolve over time with additional capabilities and to new geographies and business units.
Building a winning business case for MDM involves addressing a number of data and business process considerations while factoring in the varied MDM approaches. So before taking the MDM plunge, consider conducting a data life cycle audit followed by a ROI and TCO analysis to understand the reliability of your master data and the benefits of its use in business process. In addition, recognize that key factors such as architectural limitation and implementation risk need to be taken into account when evaluating various MDM vendor approaches. Ultimately, only an adaptive architecture that supports all the common CDI styles can deliver sustained economic benefits by starting small and growing over time to a full MDM platform.