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Summary: A data governance maturity model is a tool and methodology used to measure your organization's data governance initiatives and communicate them simply to your entire organization.
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In an organization where data governance protocols are absent, data quality cannot be guaranteed. When data is unstructured and changes made to it undocumented, its quality diminishes—fast. Not only is this a major headache for data teams, but it prevents business users from using company data to innovate.
Bad quality data and non-existent data management processes lead to inaccurate data sets. And when data is wrong there can be catastrophic consequences, from detrimental business decisions to potential data breaches and costly compliance violations.
To combat these issues, organizations must deploy a data governance strategy, but for this strategy to be a success, there needs to be a high level of data maturity. The best way to achieve this is by adopting a data governance maturity model.
To achieve a superior level of data governance maturity, organizations must adhere to a data governance maturity model. There are lots of examples of this model, but before we take a deep dive into the most recognized ones, let’s explain the terms that define them.
Data governance maturity refers to the stage an organization has reached in the implementation and adoption of data governance initiatives. An immature organization will have a great deal of unorganized data and will not be using this data to drive growth. Alternatively, a mature organization will be well-aware of the importance of data as a key business asset and governing and managing it accordingly.
A data governance maturity model is a tool and methodology used to measure your organization's data governance initiatives and communicate them simply to your entire organization. In a mature organization, all the processes to manage, access, and innovate using data assets are in place. Less advanced organizations can use the maturity model to achieve this objective.
There are a handful of well-known data governance maturity models, including examples from IBM, Stanford, Gartner, and Oracle. These models provide a method by which a business can learn how to manage data effectively, provide user access, ensure that data is of high quality, and make it possible for everyone in an organization to benefit from these advances.
When a company achieves the highest level of data governance maturity, it will see palpable results. Company-wide, data will be used to innovate and collaborate and make better business decisions, while these same organizations will avoid the huge fines that arise when data protection regulations are not observed.
Although there are several data governance maturity models out there, the best known were developed by OvalEdge, IBM and Gartner. As mentioned earlier in this blog, a maturity model is a tool for measuring the level of your data governance capabilities. So, you must ensure that when you adopt a maturity model, you also have in place a data governance framework and roadmap that follows the same methodology.
When you set out to decide on a data governance maturity model you need to consider many factors. These include key business drivers, the budget required to implement the model, the existing data management and governance framework, and the industry you operate in.
The objective of every data governance maturity model is the same, but neither Gartner nor IBM provide the detail required to overcome the challenges businesses will face. With our approach, we enable companies to track the progress of their data governance initiatives.
The OvalEdge data governance maturity model should be applied to three core areas of data governance: data quality, data access management, and data literacy. The aim is to apply this model to each of the three areas independently and tackle data governance progressively.
The best way to understand the level your company is at and then progress onto the next is to ask data users questions with a formal questionnaire. This will enable you to understand where your organization is, what your staff know, and what they don't.
The IBM data governance maturity model is one the most widely recognized. Developed in 2007, the model is designed to help you determine your progress across 11 core data governance areas. These include data awareness and organizational structure, data policy, data stewardship, data quality management, data lifecycle management, IT security and privacy, data architecture, data classification, compliance, value creation, and auditing.
To progress to level 2, data teams should audit the way data is shared in their organization and create a plan that includes data owners and other stakeholders.
To reach level 3, regulatory measures need further development and documentation. To initiate this, you need to begin creating models that map your key infrastructure and requirements.
As you continue to specify and implement data policies and management processes, your organization will progress to level 4.
To achieve the highest level of data maturity, you must concentrate on producing KPIs and other performance metrics. To achieve this, you must develop a clear, concise plan for executing data models.
Another widely recognized model is the Gartner data governance maturity model. Since 2008, the Gartner model has enabled enterprises to achieve five major goals:
Action item: Data teams and planners must educate key business leaders about the importance of data governance and focus on the potential implications of breaching compliance regulations.
Action item: Data teams must develop an EIM strategy that fits with existing enterprise architecture and strategic business goals.
Action item: Key business leaders must promote the initial procedures and encourage adoption. At the same time, an overall value proposition must be made available.
Action item: Create and present an EIM strategy to business stakeholders and management and seek out EIM opportunities at the departmental level.
Action item: IT management tasks must be inventoried to check they follow the EIM strategy. There should be a scorecard to rate data management processes.
Action item: Ensure measures are in place that guarantees EIM controls and quality standards continue regardless of changes at the leadership level.
Data is the most important driver of growth in modern businesses. Not only does it underpin the critical business decisions, but it makes it possible for collaborative practices that aid company-wide innovation.
However, if you do not govern your data smartly, it’s impossible to achieve these benefits. Wherever you are on your data journey, a data governance maturity model will enable you to calculate the level of data maturity your organization has reached. You can look back at where you have come from and determine the steps required to reach the highest level of data proficiency.
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