Data Stewardship (2014)
CHAPTER 9 Rating Your Data Stewardship Maturity
The dimensions and levels of Data Stewardship maturity are discussed, and their use in rating your stewardship effort and how it has progressed are analyzed.
maturity level; dimension; roles; structure; standards; disciplines; policies; procedures; value
The Data Stewardship effort matures as you develop it, and noting the (hopefully) advancing maturity can be a great way to show progress (in addition to the metrics discussed in Chapter 8). The maturity can be thought of as occurring in levels, and through a number of dimensions. Each dimension can have a different maturity level. By laying out the dimensions and levels in a grid (as discussed later in this chapter), you can show the current maturity, as well as the target maturity.
There are many capability and maturity models (CMMs) for data quality, and given that one of the primary goals of Data Stewardship is to improve data quality, it is no surprise that the maturity model presented bears many similarities to those presented by others. For example, in Chapter 3 of Information Quality Applied (Wiley, 2009), Larry English presents the information quality maturity management grid. This grid (adapted from P. B. Crosby’s quality management maturity model) shows how a data quality effort progresses from uncertainty (ad-hoc) to certainty (optimizing) across six categories. In Chapter 3 of The Practitioner’s Guide to Data Quality Improvement (Morgan Kaufman OMG Press, 2011), David Loshin catalogs eight sets of component maturity descriptions (including one for Data Governance). The chapter details how each of the eight topics (such as data quality expectations, data quality protocols, information policies, etc.) progresses through five levels of maturity, moving from initial to repeatable, defined, managed, and optimized. This exceptionally detailed set of component maturity descriptions is an excellent resource in constructing your own maturity model. Finally, the Infogov Community (infogovcommunity.com, managed by IBM) has a maturity model for Data Governance as well.
Data Stewardship Maturity Levels
The maturity model presented here has five levels, not including “level zero” where there is no Data Governance or Data Stewardship at all. Each level can be broken down further into four categories. You may want to start from this model and customize it to suit your own needs.
Maturity Level 1: Initial
- Response to data issues: Reactive—responds to issues as they arise and does not attempt to prevent them from happening. Root-cause analysis begins as the realization is made that just fixing the data in the target data store doesn’t work. Areas to correct become more apparent. Processes form around fixing data issues; those charged with fixing data recognize patterns of issues and develop a framework around fixing them. Documenting of resolution processes begins to occur.
- Attitude of management: Perception that poor data quality is an IT issue and not a business issue. There is little encouragement to form an organization to manage data and metadata.
- Handling of metadata: Attempts at cataloging and managing data definitions and other metadata are scattered and there is no centralized effort to gather and document metadata.
- Development of formal organization and structure: Small teams form to recommend changes.
Maturity Level 2: Tactical
- Response to data issues: Data issues are starting to be responded to using repeatable processes that are becoming more formalized. Individuals charged with fixing data may begin to see stewardship duties in job descriptions and objectives that reflect those duties.
- Attitude of management: Data quality is still seen as an IT issue, though there is more involvement from business areas that are affected by poor-quality data. There is recognition that business areas are responsible for their metadata, data, and data quality.
- Handling of metadata: There is a recognized need to gather metadata around systems and applications, and store that metadata in a central location such as a metadata repository.
- Development of formal organization and structure: Business Data Stewardship is beginning to appear, as are some Data Governance standards. These are limited to a few business functions that are most affected by data issues. Data ownership is beginning to be recognized as well.
Maturity Level 3: Well Defined
- Response to data issues: Data quality issues are being rigorously tracked. The organization includes risk assessment for data quality in the project process. Data integration efforts begin and include Data Stewardship as a crucial participant. Data quality metrics are beginning to be measured.
- Attitude of management: Business areas are stepping up to own their data. The importance of data and data quality is communicated across the enterprise. Business and IT partner to support Data Governance and data quality.
- Handling of metadata: The need for robust business metadata is recognized and it is stored in a central location such as business glossary.
- Development of formal organization and structure: Standards are developed, documented, and communicated. The change process now includes data quality and Data Governance as the corporate culture changes to embrace these disciplines. Performance metrics for Data Governance and Data Stewardship are beginning to be measured. A formal Data Stewardship Council and Data Governance Board has been instituted, however, not all business functions are represented. The beginnings of a Data Governance Program Office are in place.
Maturity Level 4: Strategic
- Response to data issues: Tools are added for data quality and profiling with ongoing improvement efforts. Data Stewards are always involved in data quality improvement efforts. Risk assessments for data around projects are done early. Data quality issues and resolutions are measured, monitored, and communicated.
- Attitude of management: Data Governance and Data Stewardship metrics have become a primary corporate measurement of success in managing data across the enterprise. Senior management drives the Data Governance strategy. Data is seen as a valuable corporate asset. Accountability for quality and understanding of data is practiced across the enterprise. Data quality is a corporate objective, not a business or IT problem. Ongoing investments in managing data and metadata are supported and championed. Stewardship metrics are included in assessments of projects and employee performance.
- Handling of metadata: Expertise increases in metadata management and master data management. Single sources of the truth for both metadata and data are identified and documented. All key business data elements have full metadata collected quickly and efficiently.
- Development of formal organization and structure: All business functions are represented in Data Governance and Data Stewardship, and participation is mandatory. The executive leadership team gets regular updates and handles escalated issues quickly and efficiently. The Data Governance Program Office is fully staffed and funded, and reports progress, metrics, and issues to senior leadership on a regular basis.
Maturity Level 5: Optimized
- Response to data issues: Innovation becomes key in maintaining the vision of improving data quality and remediating data issues. Requirements are in place to safeguard data quality in data from outside business partners.
- Attitude of management: The corporation is ready to innovate where Data Governance/Data Stewardship and data quality are concerned. Innovation drives the vision of Data Governance. Management and the Data Governance staff keep abreast of important emerging trends in data management and adapt accordingly. Creativity and competitive advantage in using high-quality data is encouraged. Staff is freed up to explore new ideas and new technologies.
- Handling of metadata: All metadata is collected and stored in a central repository. Data profiling results are used to automatically open and measure remediation of issues on an ongoing basis.
- Development of formal organization and structure: Data Governance and Data Stewardship expand to incorporate outside business partners. Standards and controls are in place and have become the corporate culture. The company is considered an example of good Data Governance and Data Stewardship in the global business community.
Data Stewardship Dimensions
The dimensions of Data Stewardship maturity establish the measurement criteria for evaluating each of the five maturity levels. For example, the Value Creation dimension starts in Level 1: Initial with no stewardship value recognized and unknown value of the data. In Level 5: Optimized Data Stewardship has a proven track record of driving value. Level 3: Well-defined is where the value of data is becoming well recognized. As stated earlier, in this model there are four of these dimensions: Organizational Awareness; Roles and Structures; Standards, Policies, and Processes; and Value Creation.
The Organizational Awareness dimension rates how well Data Stewardship is integrated into the organization, sponsorship, and the development of the metrics. Table 9.1 shows the various levels of maturity for organizational awareness.
Organizational Awareness Dimension by Maturity Level
Roles and Structures
The Roles and Structures dimension rates how well defined the Data Stewardship roles are, as well as how effectively those roles are being staffed and executed. In addition, this dimension rates the completeness and integration of the supporting structures. Table 9.2shows the various levels of maturity for roles and structures.
Roles and Structures Dimension by Maturity Level
Standards, Policies, and Processes
The Standards, Policies, and Processes dimension rates how well defined is the framework for supporting policies, processes, practices, and standards. In addition, the existence and robustness of the policies, processes, practices, and standards themselves are rated. Finally, having executive support (i.e., endorsement) of the policies is a critical success factor and the level of the endorsements increases with increasing maturity. Table 9.3 shows the various levels of maturity for standards, policies, and processes.
Standards, Policies, and Processes Dimension by Maturity Level
The Value Creation dimension rates the recognition of the increasing value of data, as well as the recognition within the organization of the value of Data Stewardship. Table 9.4 shows the various levels of maturity for value creation.
Value Creation Dimension by Maturity Level
Measuring Progress in Maturity
The most useful way to use the designated levels and dimensions of Data Stewardship maturity is to lay them out in a grid, as shown in Figure 9.1. By examining the intersection of each dimension with each level (as detailed in Tables 9.1 to 9.4), you can rank your Data Stewardship maturity for each dimension. Figure 9.1 shows the appropriate cells (intersection of the current level in each dimension) circled.
FIGURE 9.1 The Data Stewardship maturity grid with the initial (current) levels for each dimension displayed (solid rounded rectangle). The text in each of the cells is a summary of the text in Tables 9.1 to 9.4.
The next step is to have Data Stewards recommend (and Data Governors approve) the target levels for each dimension. It is not always possible or necessary to get to level 5 in each dimension, but Data Stewards and Data Governors should set the goals that the organization will strive for, and the goal should be to show progress in increasing level of maturity. These goals can then be documented by the arrows stretching from the current state to the target goals in the grid, as shown in Figure 9.2.
FIGURE 9.2 The Data Stewardship maturity grid with the target levels for each dimension displayed (dashed rounded rectangle).
Once you have your initial level for each dimension recorded, you should periodically revisit (perhaps every six months) the stewardship maturity grid and ask the Data Stewards to ascertain the current level for each dimension. Planning tasks and setting goals that increase the maturity levels in each dimension will cause the overall level of maturity to increase over time, reaching the assigned targets and maintaining Data Stewardship at those maturity levels.
The maturity of your Data Stewardship effort—measured in levels across a set of dimensions—is an important way to rate how the program is progressing and becoming more robust. The first step is to establish the levels and dimensions, most likely based on some maturity model available in the literature, then adjust them for your organization. The next step is to rate the current maturity levels and determine what the target levels should be. This exercise enables you to identify and prioritize areas of improvement so that you can get more out of the Data Stewardship program. Finally, you need to revisit the maturity on a periodic basis to determine the current maturity level and see if progress is being made.