Data Management Maturity Level
,While a business case for MDM will normally address one or more of the three reasons presented earlier, it is important to strengthen it with an accurate assessment of where the company is regarding the overall MDM spectrum. Two companies with the same business needs will not necessarily follow the same steps to get there because they might be at different maturity levels regarding what is necessary from an MDM practice perspective.
Several data governance maturity models exist and should be used as a frame of reference. Even though data governance in itself is just one of the components in MDM, it is perhaps the most pervasive one since it overlooks all other activities within MDM. That means a data governance maturity model can be used as guidance to understand where a company is and where it should be regarding the management of data as a strategic asset.
Multiple MDM vendors have their own maturity models, but most of the time their models can be used independently from their product lines. Besides, there are models from neutral companies such as Gartner, Data Management Association (DAMA), and the Data Governance Institute (DGI). “Data Governance Part II: Maturity Models—A Path to Progress,”2 a paper authored by the National Association of State Chief Information Officers (NASCIO), provides a good overview into data governance maturity models, and a closer look into several existing models by multiple vendors.
The end state of a data governance maturity model is essentially when a company achieves a level characterized as being proactively governed, optimized, effective, standardized, and quality controlled at a global level. Everyone in the company will typically agree in principle with the merits associated with achieving this end state or at least with achieving a significant advancement toward this end state. Advancement consists of many individual objectives and achievements, with each one rooted to solving problems that generally everyone should be able to recognize and relate to in the current state. Addressing these problems becomes the heart of the business case. Relating these problems to “here is where we are” in the Maturity Model, and being able to express the achievable objectives that will drive advancement toward the end state, will be extremely important in establishing the business case.
Let's look at some examples of this. The intent here is to provide examples that will express undeniable customer data management problems and their risks along with a realistic course of action needed to mitigate the problem and to create ongoing practices for advancing in the Maturity Model.
Table 1.2 Examples of Customer Data Management Problems.
| Problem | Risk | Action Needed |
| There is growing perception in our company that poor data quality is creating customer satisfaction issues, but we don't have any specific data quality measurements to qualify this. | Issues with data integrity, duplication, and fragmentation are going unchecked. This can definitely have an increasing impact on the customer transaction processes. | Data quality metrics and analysis techniques are needed to fully scope this problem in order to determine cause, effect, and a mitigation strategy. |
| The customer data in our data warehouses is inconsistent and cannot be trusted due to insufficient standards and lack of control at the data-entry level. | Without an accurate source of reference, operational reporting and customer analytics will be subject to error, interpretation, and cross-functional disagreement. | An overarching data governance process is needed to define and drive policies and initiatives that will establish the necessary standards, quality control, and trust of the data. |
| There is no single view of a customer, and each system has different representations and classifications of the same customer. | A 360° view of the customer cannot be achieved. Marketing, sales, and services cannot effectively synchronize on customer identity and continually are in conflict with customer reporting. | A customer data integration and hierarchy management strategy is needed to build a common customer view to be used as the foundation for consistent business intelligence and customer strategies. |
| Whenever we discover some major data management or data quality oriented issues, they always seem to require a major escalation followed by an inefficient and time-consuming process to define a focus team just to begin addressing the problem. | Data problems causing major business issues such as related to compliance, privacy, or operational performance, will be continually difficult to resolve and even further exacerbated if there is not a standard process with sufficient resources and roles for data quality management. | As part of a data governance model, an ongoing data quality management forum and process is needed to consistently and quickly address data-oriented issues that can impact business operations. |
These examples obviously need to be tailored to fit the type of issues and objectives that will be relevant to your company. Doing so will greatly help drive a shared perspective about the business problems, strategy, and direction needed to address them. This business case will also serve as a foundation for establishing the MDM model and creating the data governance charter that we will cover in more detail throughout this book. The point here is to build a strong central business case that will drive specific, measurable, attainable, realistic, and timely (SMART) goals throughout the MDM initiative.