How to Recognize and Gauge Maturity?

Up to this point, we have covered the planning and implementation of a Customer MDM model with particular emphasis on the business practices needed to drive the disciplines of data governance, data stewardship, data quality, and data access management. All of which are about moving from an unfocused state of data management to the highly managed and controlled state necessary to achieve true Customer MDM.

As much focus needs to go into institutionalizing and maturing these practices as is needed to initiate them. But it is necessary to first consider what constitutes a mature MDM model and how this can be gauged. This needs to be put into perspective because until a mature state has been reached, it's unlikely that you will be able to fully realize the true potential and extended benefits of MDM, such as improving the value of your data and creating an accurate and trusted customer 360° view.

Let's examine what constitutes a mature Customer MDM model and how to gauge where you are in this continuum. Evaluating maturity should start with your definition of MDM. You'll recall that in the Introduction we offered a definition of MDM. Whether you embrace the one given or another definition, achieving what has been defined and envisioned for MDM requires progress, adjustment, and control across the four disciplines on which we have been focusing. These disciplines also represent the areas where the maturity of the MDM practice can be assessed. Being able to gauge maturity across Data Governance, Data Stewardship, Data Quality Management, and Data Access Management will not only help differentiate where the focus, progress, and issues exist within the MDM model, but can also serve as an excellent overall program dashboard to use with the data governance council and for executive-level updates.

In the area of data governance, there already exist a significant number of definition and maturity models that have been defined and presented by a number of firms in the data management and MDM space. And not wanting to reinvent the wheel here, we can examine these governance maturity models to formulate a similar approach for measuring the overall MDM maturity.

Figure 9.1 is a simple example of this and indicates the dimensions we'll use for gauging maturity across the four discipline areas. As with data governance maturity models or with various types of data quality dashboards, the actual dimensions chosen for use in the model can be different than what we have chosen. No matter which are chosen, they should reflect dimensions that are clear, consistent, and meaningful to the audience and executive teams.

Figure 9.1 MDM Maturity Dashboard Example

The dashboard in Figure 9.1 reflects the following scenario:

• Data governance council has been initiated now for a number of months. The governance process is being well managed and progressing with increasing influence and effectiveness, but has a ways to go yet before it is able to establish the broader span of control and data management consistency it expects to achieve.
• Data stewardship discipline is lagging behind the maturity level of governance. Full global implementation of the data steward model has been delayed due to budget issues preventing some functional area data steward roles from being filled right away, but these roles are expected to be filled in the next two quarters.
• The intended focus and improvement projects around data quality management have yet to get started. Starting these efforts is dependent on getting the additional data steward resources in place.
• The data access management plan is moving forward with some adjustments to the schedule. Additional auditing and control processes are on target to be ready beginning next quarter; however, due to the delay in getting the additional data steward resources in place, in some areas the auditing and control processes will need to phase in later than had been planned.

When presenting a dashboard of this nature, be sure that for each measured area there is accompanying explanation to support the status and sufficiently highlight the positives or negatives. We'll dive further into each of these maturity areas to more fully distinguish the various aspects of maturity.

Data Governance Maturity

As previously mentioned, data governance maturity has been well studied and there exist a number of excellent perspectives on this topic. For additional reference on this, we recommend the NASCIO Governance Services publication titled “Data Governance Part II—Maturity Models—A Path to Progress”¹ and Tony Fisher's book The Data Asset: How Smart Companies Govern Their Data for Business Success.²

In Chapter 4, we discussed how the success of an MDM initiative will rest largely on an early and effective implementation of data governance. Therefore, it would be logical to assert further that the maturity of Customer MDM is highly dependent on the ability of the data governance to first reach a mature enough state so that it can influence and drive the maturity of the other MDM disciplines we have been discussing. When and how data governance reaches a mature state will basically come down to gauging a few things:

• When ownership, partnership, and cross-functional collaboration are established and continually work well.
• When the governance council can repeatedly make timely, actionable decisions.
• When it is clear that control is being maintained from both a reactive and proactive perspective. This refers to how effective the governance process is with turning the dials left or right to control its data domain.

Although a clear sign of maturing is moving from a more reactive state to a more proactive state (as many of the governance maturity models point out), becoming more proactive doesn't fully eliminate the reactive state. In a complex customer data domain, there can be many moving parts and process or people variables; therefore, there is always opportunity for surprises or unexpected events. Often, there are known uncontrolled areas or events in an environment that by design or decision are left that way due to the situation being considered a nonissue, low impact, or low priority. These cases, as well as unforeseen situations, can suddenly become significant or blow up unexpectedly due to any number of reasons, shift in events, or change in priorities.

So, whether a proactive or reactive scenario, the most important factor in approaching a disciplined state is to be able to recognize and effectively manage the scenario so as to avoid or minimize the potential for business risk and operational issues. This can be accomplished by having established a responsive and pervasive management dynamic where awareness, review, and decision making are well orchestrated in a timely and fluid process.

With achieving a well-managed state of governance, a controlled state can be achieved when people, process, and technology are well positioned and can be fine-tuned as needed to maintain a steady state and to successfully drive progressive initiatives that support the ongoing MDM goals, objectives, and priorities.

Data Stewardship Maturity

In Chapter 5, we stated that once the concept of data stewardship is fully recognized and a model is defined, it boils down to the combination of the people, processes, and a data caretaking focus that will establish the practice of data stewardship. We also stated that for data stewardship to be effective, the concept and practice of data stewardship needs to be clearly embedded in the key junction points between the entry and management of the master data. It's at these junction points where governance control, quality control, and control of data access management can best be influenced through use of committed and well-focused data stewards.

Probably the most difficult challenge with data stewardship is getting a broad enough and consistent enough commitment for the data steward roles. If this challenge isn't well recognized and addressed early in the MDM and data governance planning stages, this is likely to pause momentum of the MDM initiative due to forcing unplanned budget and resourcing issues to emerge or because the functional or geographic areas may open debate about the data steward model and plan. Late emergence of these types of underlying issues can be very disruptive. The concept and expectations for data stewardship must be agreed upon early and consistently applied in order for MDM practices to be well executed because the actionability of MDM largely rests in the hands of the data stewards.

The ability for data stewardship to reach a mature state will require that the data steward model is fully executed and functioning cohesively in alignment with the data governance process. It's this dynamic that lays the foundation for data stewardship to reach a controlled state whereupon the reactive and proactive needs we discussed in the data governance maturity section can be executed through responsibilities and action plans and assigned to the data stewards.

Similar to data governance, maturity of data stewardship will be gauged by how well this discipline can be orchestrated in a timely and fluid manner, but also by how successfully and consistently actions are addressed by the data stewards. A mature data stewardship model should have a visible closed-loop process tracking the major action items and mitigation plans that involve the data stewards. Not having clear or consistent closure from data stewards regarding global and local initiatives usually suggests a problem with accountability or priority, and signals that the MDM practice may be stuck too much in meeting mode and not enough in action mode.

Data Quality Maturity

In prior chapters, we have covered the planning, implementation, process, and execution of data quality management. We indicated that data quality is likely the single most important reason that organizations tackle MDM, and that trusted data delivered in a timely manner is any organization's ultimate objective. We also stated that establishing a culture of quality throughout the enterprise is mandatory for maximum benefits. Data quality is everyone's responsibility, but requires the organization to establish the proper foundation through continuous training, proper communication channels, effective collaboration, and an efficiently adaptive model that in spite of constant changes can continue to deliver results quickly.

To achieve a quality culture and quality improvement, there are many programs, mechanisms, roles, and relationships that need to work in harmony. Using our dashboard example in Figure 9.1, data quality management can't reach a mature state unless the data governance, data stewardship, and data access management practices are all enabled, functioning well, and have progressed to a managed state. That shouldn't be a surprise, though, because a quality culture depends on people, process, and technology to be in sync with the recognition, management, and mitigation of quality issues.

Determining the level of cross-functional collaboration and quality management effectiveness is the first factor in gauging data quality maturity. That collaboration is first established through the maturation of data governance, and should then translate into creating a foundation to drive and mature data quality management. Recall that in Chapter 6 (see Figure 6.4), we stated that reaching maturity in data governance should mean less effort is needed with regard to data quality management, since better-governed data needs less correction.

The second factor in gauging this is how well the quality of the data is serving the business intelligence process. We have previously indicated that data quality and integrity issues are the primary cause of the conflict and divide between operational and BI practices. Solving for this gap starts by creating more awareness and connection between the front-end and back-end processes, but ultimately it is about improving the quality, consistency, and context of the master data to increase the value of the data in terms of driving more top-line and bottom-line benefit through improving operational efficiency and sales opportunity. Value of the data needs to be measured by how well it is servicing both the operational and BI process. How valuable the master data is or needs to become should be a well-recognized factor and a key driver in a highly functioning and mature Customer MDM model.

Data quality maturity essentially equates to having reached a state where an acceptable level of quality management and control and a shared view about data value exists between operations and BI. Reaching this state of acceptability and harmony is, of course, a very tall order, but recall that in previous chapters we have stated that making significant strides with quality improvement will require time and stems from well-coordinated efforts, which are orchestrated from the data governance council and data quality forums. This is exactly why MDM and data governance need to be approached as long-term commitments and become well entrenched as priorities in both the operating and analytical models of a company.

Data Access Management Maturity

As important as this discipline is, and as it is probably the easiest of the four disciplines to gain tighter management and control over, it is often the most overlooked and undermanaged area of MDM focus. In Chapter 7, we pointed out how compliance and legislative requirements or the need to reduce corporate risk were the key drivers for a company to implement data governance. We also pointed out that the process and control used to manage data access is typically an IT-driven solution with very little management and monitoring responsibility assigned to the business side.

As we stated that data quality is everyone's responsibility, so too is data access management. IT needs to play a key role, but until you can more fully engage business users and managers through more awareness and participation, risk and opportunity for misconduct will have an open door. Gauging maturity of data access management is largely a matter of determining the control zones a company needs to have, and how much of these areas are being managed well. In Chapter 7, we provided a process and approach that enables the ability to identify and gauge data access control via a business-oriented gatekeeper. Whether using this type of model or another model, it's the ability to specifically pinpoint, monitor, analyze, and manage data access that will register on the maturity scale.

Data security, privacy, compliance, and regulatory control are broad concerns with any major company, but when you take a deeper dive into these subjects and ask very pointed questions or try to get specific reports to help qualify and quantify the management of this, there seem to be a lot of black holes and gray areas in the process, measurement models, and available data. Not being able to get really specific about the level of monitoring and control is a sign that focus and maturity are still lacking.

It should be expected and acceptable for the data governance team to examine this discipline area and begin to ask probing questions to at least get a baseline read on how well data access is being managed and controlled. By simply initiating more analysis and seeking more quantitative information, it shouldn't take long to get a realistic perspective on what, if any, gaps exist.

Organizations that can produce very specific user access detail and can demonstrate rigor with monitoring and auditing their processes are organizations that clearly recognize the need to manage and control their data. This also goes hand-in-hand with recognizing and protecting the value of the data. A maturity state of data access management can be recognized when both IT and the business organizations have implemented effective auditing and control practices that help augment, support, and substantiate corporate policy and employee training related to information protection and regulatory compliance.