Dmbok Maturity Assessment

[Katina Piccirilli]

Thebeginning of the DMMA process includes determining which business lines to assess. The mortgage bank decided to include their primary and reverse mortgage businesses, retail banking business and cross-business functions as the business lines o assess. A key part of the maturity assessment process is determining the people to be interviewed. This assessment was done with a very large number, but usually a much smaller number of people are included. It is important that those interviewed include people from both the business and technology areas involved with each business lines to be assesses as well as from both senior level and operational level people involved with the day to day processing of the organization data. Once these decisions were completed then a Current State Analysis was conducted, which included the completion of a series of interviews with around 100 employees in various departments of the bank, about 15 interviews with people who were judged to have the best perspective on current data management maturity in the organization, and then interviews of the interviewers by Ms. Reeve. The results of the interviews were collated and an assessment was created based on the 5 levels of the CMM framework:

Any organization that undertakes a DMMA needs to be aware that once the results are finalized, a specific roadmap must be developed and followed or the entire process will likely fail. The mortgage bank could not implement every change identified in the maturity assessment: The improvements in data security management alone would take considerable time and an investment in training, upgraded software, better hardware and a range of new processes throughout the organization. The goal of having every KPA at level 3 was unrealistic in the immediate future, especially when none of their current numerical values even reached 3. Thus, they had to weigh the relative importance of each KPA and move forward on those most important.

Most organizations complete a three year roadmap, but even with such a map stabilized and under implementation, it is also necessary for a yearly reassessment to make sure the roadmap is still being followed, how well it is working and what changes need to occur. Creating a roadmap from the entire DMMA process and then not following it is not only a waste of time and resources, but also costly. Graphic Four shows the basic roadmap that was created after the maturity assessment (a fully functional one is much more detailed) for the mortgage bank in 2011. It illustrates the enabling activities the bank needed to employ to move the roadmap forward, the immediate priorities and gave a colored graphical illustration of what areas were being worked on with a timeline for the completion of certain projects.

Our analysis starts with the identification of the comparison criteria. I have developed a framework for the comparison based on the maturity models by Carnegie Mellon University and the Institute of Internal Auditors. Let us take a look at it.

Different models use different business blocks and their hierarchies. Processes, capabilities are examples of building blocks. The structure of components, capabilities, and sub-capabilities of the DCAM 2.2 model is an example of a hierarchy.

Several different maturity models exist. This is the state-of-the-art to compare them. Considering the very different natures and backgrounds of these models, data management deliverables/outcomes/artifacts is one of the feasible factors to make a reasonable comparison. I have demonstrated such an approach in the previous article of this series.

DCAM 2.2 has another approach. It includes the consideration of the maturity in each (sub)-capabilities. The method to aggregate the maturity levels of each (sub)-capability at the higher level of abstraction is unclear.

Both frameworks have different viewpoints on data management. They have several building blocks similar by names: Data Governance, Data Architecture, Data Quality. In reality, the differences are even deeper. For example, the deliverables of Data Governance by DAMA-DMBOK2 quite differ from those by DCAM 2.2 Data Governance.

DAMA-DMBOK2 offers four assessment criteria: Activity/Process, Tools, Standards, People, and Resources. DAMA-DMBOK2 recommends applying these assessment criteria to Knowledge Areas. It does not provide the methodology to perform such an assessment and aggregate the results per assessment criteria.

The methodology used by DCAM 2.2 is also not fully clear, maybe because of restrictions for the publicly available materials. Each of the sub-capability has a Rating Guidance based on 6 levels of maturity. A Rating Guidance describes the status of each sub-capability depending on its level of development. The method to derive the overall capability level per component is not clear.

Data management is a set of capabilities that enable the data chains to transform data into information. Data modeling, information system architecture, data quality, and data management frameworks are key data management capabilities. The model can also include other capabilities as well. Process, role, tools, resources, and data are key components that enable every capability. The methodology allows measuring the maturity level of each capability component. Then the results are automatically aggregated to the level of separate capabilities and the overall data management maturity.

Based on this methodology, Data Crossroads has developed a free-of-charge maturity scan. Since March 2019, more than 700 companies worldwide have performed this scan on an anonymous basis. A review of the results has been published in 2019 and 2020.

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An assessment is the process of documenting, usually in measurable terms, knowledge, skills, attitudes and beliefs about a topic. Since assessments can be objective or subjective, the optimal assessment combines attributes of an audit while maintaining the assessment qualities of documentation and impressions.

An assessment can help an organization measure its growth and maturity in a particular area of data management or prepare the organization for a new effort. Many companies request a data management assessment or data governance assessment to determine:

Rather than fear the assessment process or dismiss its possibilities, organizations should embrace the concept of an independent assessment of their data governance practices. A good assessment would include a review of the implementation of data governance and enterprise data management concepts and techniques. This enables companies to learn what is working, what is not working, the reasons, and how to improve their data governance efforts.

Performing any type of assessment has a variety of challenges, some common and some unique to the organization or to the type of assessment. Experts from a variety of sources including the CMM Institute have listed the following issues encountered in developing and performing any type of assessment:

An assessment of the current state of data governance for an organization will provide many benefits to any organization. These benefits include: an objective review of the current state of data governance based on best practices and industry standards where applicable, development of business goals for governing and managing data according to approved policies and standards, and refining the approach to data stewardship and the management of metadata.

In the third blog post of the Data 101 series, we discuss how implementing good data management practices helps organisations achieve their goals effectively. We also explore what is a data maturity assessment and how it can help the public sector identify and address data issues.

From our experience collaborating with the Irish public sector clients, the DMA results reveal several common issues. In addition, we have included some ideas on ways to improve data management practices. Organisations can use this information to guide their future data strategy planning.

Evaluate your data management and usage practices to identify the level of maturity, develop policies setting out your data practices and assess your legislative environment from a data perspective to (re)establish your data needs.

But if I did want to run that marathon, I would use my fitness assessment, and regular measurement of progress against my fitness improvement plan, to ensure I had the right levels of fitness to achieve that goal. Organisations can leverage maturity assessments in a similar way as part of their strategic planning by using the results to develop a a roadmap for progression towards the required level of data management excellence is needed in the data management capabilities critical to delivering on their strategic goals.

I write this as one of the contributors to the DMBOK2 chapter on Data Management Maturity Assessment and as someone who started using DMBOK as a guide in maturity assessments way back in 2010 when DMBOKv1 came out. It is possible to use the DMBOK as a reference guide within the context of a Data Management Maturity Assessment, but you need to do some work to define the objective assessment scale and identify the key practices, methods, and outcomes that are to be looked for.

This focus on objective statements of practices, methods, and outcomes is important as it is only with a good methodological baseline that you can then compare scores between different assessment periods and track progress on the maturity journey.

We have extensive experience carrying out data management maturity assessments in organisations of all sizes and assisting them in developing realistic roadmaps for data management improvement. Uniquely, we have extensive experience in integrating the DAMA DMBOK into data management maturity assessments. Our team have also contributed to the development of other frameworks such as the IT-CMF and the DCAM.

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