How can you use a Data Governance Maturity Model?

OK, let's set this off on the right footing! It's rare to get a Data Governance Maturity Model. In fact, I've never seen one. These models are Data Management Maturity Models. Data Governance is one of the capabilities you need when managing your data, so you want to measure Data Management maturity, not only Data Governance maturity.

DAMA to release the Data Governance maturity model quantifies how DG has garnered the importance within the Data Management’s landscape!! Might be common components from DM Maturity Model but must learn for me and the community. My two cents!

Which dimensions are relevant to the maturity assessment will depend on the organisation and its objectives. For example, if the focus of the project is to improve data quality then, whilst data security is an important area, it is not something which will impact on data quality.

Data management/governance is like teenagers sex: everybody's talking about it, but few knows about it and even less actually practice it. Maturity of this is a wet dream for most organizations, so why would they want to measure it?

DG, DQ etc. come under the general umbrella of data management rather than separate 'things' so some of these dimensions and capabilities for a DGMM would appear to be overreach into areas not of its responsibility?

One of the main challenges in adopting a data governance maturity model is the lack of understanding of data governance across the organization. It comes across as too theoretical, often philosophical and while everyone agrees that it is the right thing to do, most of the leaders don't have the appetite to start because the outcomes are far out or have too many variables associated to the success. Often an assessment leads to documented actions and it can potentially reflect on leadership capability. An assessment will prove the point, but could be detrimental to long term success So, it would be wise to start with smaller use cases, create success stories, repeat and scale.

A common pushback is that the leadership team will say that they know that they are immature, and they just want to focus on improvements. But by carrying out an initial assessment, it's much easier to quantify the benefits of your efforts.

Depends on the organisation, some prefer assessments as a vehicle to drive or act as a change agent. It's not necessarily a prerequisite. It can be a powerful tool in places where external expertise and opinion has a higher value than it should. Also can de-risk CDOs that lack "aircover", an effective shield when things don't quite go to plan. Others use it less effectively and it fast becomes a bureaucracy with annual assessments for the sake of box ticking. Watch out for expensive lock-in traps when utilising proprietary models that are peddled by certain consultancies.

A data governance maturity model can be an effective indicator and tool to identify and motivate a governance organization and company as a whole provided it has been developed to take both relevance and context of an organization’s defined operating model and vision into account. Often an organization knows data is pivotable to its success and locks it down to the point where it stifles its adoption and usefulness outside of the data governance organization. An effective governance maturity model must provide context to illustrate that data governance must be agile to be both strict yet accommodating in its promotion of activities that develop rather than hinder an companies data maturity.

Many would not like this, but who really want or can objectively measure results of this, especially when they have a lot of KPIs linked to management bonuses, which could be impacted by data governance/management maturity measures?

You can assess at both team and department level, and geographically, as well as an overall score. Company-wide average scores will likely mask a range of maturity levels around the organisation.

Using data governance maturity models can be overwhelming. Start basic. Start small. It's a big elephant and there's no need to tackle it all at once.

The maturity model itself won't improve your data. Rather, it helps guide you to the actions which add the most value. Don't get obsessed by increasing maturity scores and not considering business benefits.

The data governance maturity model helps a department or corporate function within an organization to see themselves where they are at present and where they would want to be in the next year or two. The matuity model helps the department to come up with the list of areas (data quality management, data classification and metadata, policies and standards to name a few) to focus on in the future to move on to the next level of maturity. Assessing the data governance maturity is not a one time activity and needs to be evaluated at a defined interval to see the progress made year on year.

Using one (or even multiple) maturity models allows you to get that baseline of where the organization stands. You can use this to make a business case for more resources (financial or human) and it can be used to generate discussion around existing procedures, policies, and processes within the organization.

Stakeholder engagement is key, especially if there have been previous data governance projects which have not succeeded. Ensure that you are getting a range of views, and don't let the feedback from a small number of noisy individuals skew the score for the whole firm.

Think big but start small. An organizations memory is not that elastic as to accommodate multiple attempts at getting thing right. Of the numerous data models that exist all are all excellent at providing the big picture but these typically fall short when an organization puts in a considerable amount of effort and see very little movement on its maturity scale. Knowing where to focus and when are paramount in shepherding an organization towards data maturity. From my years spent in reliability there are multiple tools like FMEA - Failure Modes & Effects analysis that can be applied to identify which key elements within the maturity model an organization will have particular trouble attaining and identify how these gaps can be closed.

Doing anything with data governance is HARD. Most people at the C-Suite don't get it and frankly, don't want to get it. I think the biggest challenge is getting buy-in for any kind of data governance effort.

An effective data management maturity model (not just "data governance") should identify all the areas / functions in data management, how they are performing now, what improvements each can make to achieve a desired new level of maturity, and how each improvement will affect other data management functions (e.g., how improving metadata management will help improve data quality), and a realistic timeline for each improvement. Don't forget the need to sustain those areas that are performing well!