The fundamental challenge and goal for any organization are to best cater to the needs of the stakeholders. It can often be complex due to the variety of external and internal variables that are ever-evolving rapidly. The analytical data collected provides a powerful critical insight into strategies' trends, movements, and execution. The Chief Data Officer (CDO) formulates Data Governance frameworks to create a broad, all-encompassing directive for the stewardships to execute. However, further aligning data governance goals requires exploring novel models, such as a Maturity Assessment model.
What is the Maturity Assessment Model?
Maturity Assessment Models are essentially a framework to evaluate and improve the ability of an enterprise to perform and execute a strategy to its full extent in any given discipline. They examine the infrastructure in place, the human resources, and how capabilities can optimize allocation. These assessment models play a vital role in better defining data governance models and evaluating them according to business goals.
Each maturity model is defined around the characteristics of firmly being qualitative with quantitative aspects. They tend to be pretty broad, containing a critical organizational philosophy that can align with the Business goals.
What are the Key Steps of the Maturity Model?
In the 1980s, The Capability Maturity Model (CMM) was initially devised with Carnegie Mellon University's SEI (Software Engineering Institute) for the Department of Defense to better assess their software stack's maturity. CMM was quickly embraced by enterprises and software vendors to expand and tailor it to various data governance needs. Here are the five critical processes that form the basic foundation of all maturity models-
- Level 1 (Initial): The nascent stage of maturity where the organization is mainly unstructured and driven by reactivity rather than proactive thinking. The critical focus is removing ad-hoc processes and building toward repeatable actions.
- Level 2 (Repeatable): Enterprises now have a basic structure, and some processes are executable with good results.
- Level 3 (Defined): In this level, the initial standardization of policies begins by keeping in mind the current capability of businesses. Certain inferences are made upon how the various processes might perform in multiple variables and be able to achieve the business goals.
- Level 4 (Managed): Large amounts of metrics are collected to refine the processes further and establish competency. The system's reliability is defined by further honing the maturity level of the system.
- Level 5 (Efficient): Various iterations are implemented over the system's lifecycle to enable efficiency and flexibility to change parameters. Key metrics are continuously analyzed to keep systems optimized and up to date.
How do various Maturity Models differ from each other?
CMM is the base maturity assessment model from which many other models are based or take inspiration. However, there are significant derivations as a single maturity model does not support all enterprise customers. They must be tailored to suit the various requirements of the stakeholders while being financially suitable on a short and long-term basis.
Here are some of the leading Maturity assessment models and their following unique features for varying business goals-
1. IBM- Their maturity model takes key inspirations from the CMM with similar five levels of assessments. However, it diverges from many other models by encompassing data management and governance as an integral focus. They have 11 different domains with which enterprises can assess governance policies. The core disciplines comprising their maturity model are-
- Data Quality Management
- Information Life Cycle Management
- Information Security and Privacy
2. Kalido- Their maturity model squarely focuses on the idea of having the entire organization working in an interconnected fashion and being on the same level to be fully mature. It has only four levels, with their ranking being-
- Level 1: Application Centric
- Level 2: Enterprise Repository
- Level 3: Policy Centric.
- Level 4: Fully Governed
Kalido prioritizes the whole organization to achieve significant headway in the Data governance policies.
3. The Stanford Model- Unlike many other models based on CMM, it was entirely developed internally at Stanford University. Their model is designed squarely around the enterprise market, with projects, people, and capability being a key focus.
Maturity Components- Two dimensions comprise the model-
- The foundation contains the following organizational features: awareness, formalization, and metadata. These are the qualitative measures to analyze organizations' structure and critical understanding and have key metrics to formalize objectives.
- The foundational maturity translates directly into crucial enterprise projects in Stewardship, Data Quality, and Master Data.
Each dimension is divided into Projects, People, and Capabilities to ensure better maturity assessment across organizations. This model has gained notoriety for being an open-source model and is an inspiration for companies to adapt. Beyond the three models mentioned here, many assessment models exist to explore and organizations to experiment with.
Key Benefits to Adopt Data Maturity Models
- Compliance- The large-scale adoption of GDPR policies has made strong data governance policies mandatory for all small businesses to large enterprises. To ensure proper data compliance, enterprises must maintain strict documentation and structured datasets to be easily manageable. New Data Maturity models are designed to ensure compliance within the model, making it easy to adapt to new data policies.
- Historical perspective- Adopting Data Governance maturity models can provide a more long-term perspective on how companies have evolved and the level of productivity over time.
- Comparative study- Maturity models can be used to assess organizational capabilities with competitors in a similar field. The models can focus on making qualitative analyses within the structure.
- Foundational Alignment- While adopting maturity assessment models, many enterprises undergo introspection or are given inferences by third-party consultancy on how to align better the organization, process, and technology to extend the capabilities. They further ensure that all the various dimensions in an organization evolve simultaneously.
Challenges of Adopting Data Governance Maturity Models
- Bias - The models are designed to be more experiential and have subjective elements baked in to be more flexible ever to change the industry. Due to its more scientific nature, there is a significant risk of bias and subsequently requires a robust democratic process to ensure empirical interpretations of reality are upheld. Multiple stakeholders and an outside analysis can limit the incursion of discrimination.
- Significant Expenditure- Due to a large amount of analysis and resources invested on an organizational level, the spending can run relatively high. For many companies focusing on agility, the time it takes to implement such protocols may not be worth the challenges.
- Lack of Concrete Standards- Many Data Governance Maturity Models do not have a robust specification or quantitative metric, leading organizations to adopt the highest model to appear industry-leading.
Most stakeholders should look at the Data governance maturity assessment models as a guide to achieving the company's core business goals rather than as a rulebook. These models are intentionally broad and do not fit all categories or structures of business.
Survey your organizations and get a complete analysis of the company's goals to hone in on the key focus areas through the qualitative questions. They can work as a great tool to introspect into the current standings of Data Governance and execute according to the desired business requirements.