Data Governance Roadmap Example: A Strategic Approach

Written by

Chuen Seet

Quality data is a critical success factor to the development of the business world’s most forward-thinking capabilities, such as data analytics, machine learning and artificial intelligence. As a result, data has a vital role to play when it comes to digital transformation.

Why does your organization need data governance?

Without proper data governance, however, organizations can end up building corrupt models, making inefficient decisions, or even breaking the law. To quote business thought leader John Ladley: “some very hard and dangerous lessons are happening as bad data drives bad models that drive bad actions, all as the result of a deceptive or biased AI model.”

Unfortunately, it’s harder than ever to establish a data governance capability. The amount of data gathered today is growing exponentially, with more and more sensors and sources of data. Furthermore, organizations are increasingly shifting their data to cloud platforms, which raises concerns over privacy, sovereignty, security and regulatory compliance (e.g. the European Union’s General Data Protection Regulation (GDPR)).

How does a data governance strategic roadmap help?

As a result of these challenges, organizations need to start paying more attention to data management – and the sooner the better. Developing a data management and data governance strategic roadmap is an important step in helping you to understand why you need to manage your data, what needs to change, and visualize how you should implement your changes.

Let’s first review the basic definitions, then look at how to develop a strategic roadmap for your organization’s approach to data management and data governance.

Data management and data governance definitions

According to DAMA DMBOK®, data management is “the business function of planning for, controlling and delivering data and information assets.” You’ll notice that this definition has a strong emphasis on a key principle: treating information and data as an asset.

A key sub-function of data management is data governance, which is defined by DAMA DMBOK® as “the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets.” In other words, data governance is needed to ensure that the information assets of the organization are valued and managed appropriately.

Data management and data governance capabilities

Over the years, we have learnt that effective data governance is not just a matter of implementing a set of processes or buying a tool. Don’t get me wrong — having a good set of processes/practices and tools definitely helps, but we also need to consider roles, responsibilities, culture and mindset.

That’s where the concept of a business capability is useful. A capability-based approach looks at the capability building blocks of the organization, which describe what you do or can do, not how you are organized. So, instead of being tied to the departmental or organizational design, data governance should be treated as a cross-functional, virtual activity.

We already use building block constructs, such as an organizational chart, to describe the people, roles or titles in an organization. Similarly, we use a function or process map to describe the process building blocks of an organization; and we use an asset map or infrastructure diagram to describe the physical building blocks of an organization. A capability map is just a visualization of the capability building blocks of the organization, whereby each capability describes what it does from a people, process and physical perspective.

Taking this approach, we have put together an example of what a data management capability map looks like, shown below. In this example, we have also defined the data governance capability.

Note that since this is based on a specific company, it appears different from the example in John Ladley’s aforementioned book.

Data governance capability map

Having a data management and data governance capability map is a good start for identifying the specific capabilities that need to change, or need to be created (if new to the organization).

Example data management and data governance strategic roadmap

Suppose you want to create a data management and data governance strategic roadmap. What would that look like and how can you go about building it? We’re going to walk through a very simple capability-based approach to building a strategic roadmap, which we call the Jibility Steps.

To build any roadmap, we need to start with understanding why we’re doing what we’re trying to do. Then we analyze what needs to change in order to visualize how we will ultimately achieve our strategic objectives.

There are six Jibility Steps: Challenges and Objectives to understand why; Capabilities and Courses of Action to analyze what; then, finally, Initiatives and Roadmap to visualize how.

For this example, we’re going to explore the data management and data governance roadmap for an example organization called RedYabber. RedYabber is a traditional wooden toy manufacturing and sales company that has made a strategic decision to undertake digital transformation. Some key pillars for this transformation include shifts from:

  • Traditional store front sales to online sales
  • Heavily labour-intensive manufacturing to automated manufacturing
  • Experiential and intuitive decision-making to data-driven decision-making

For the purposes of this example, we’ll be focusing on the third pillar.

Understanding why: Challenges and objectives

For RedYabber, a major challenge is their inability to drive decision-making based on data because there is a fundamental distrust in the accuracy and quality of their data. This is reflected in their list of challenges and objectives shown below.

Data governance challenges and objectives

Analyzing what: Capabilities and actions

Taking a capability-based approach, we link the capabilities to our list of objectives (shown by the corresponding numbers in our capability map below). Doing this will enable us to identify which capabilities will help us meet which objectives.

We can also apply a colour code to the capabilities to identify which are new (purple) and which require a degree of change (red for high, orange for medium and yellow for low). The capabilities which are blue do not require any change.

Data governance capability map analysis

Once we have identified the capabilities that require change, we can then describe the changes in the form of actions.

Visualize how: Initiatives and roadmap

Before we can develop a visual roadmap, we need to formulate initiatives. Initiatives are packages of work formed by collecting associated actions. An example of the initiatives required to implement changes related to the data governance capabilities is shown below: the pink blocks are the action, packaged into the orange boxes for initiatives.

Data governance initiatives

We then plot our prioritized list of initiatives onto a roadmap, which is structured based on rows called themes, and columns called stages. Our example below shows eight stages over a two-year time horizon. This two-year time horizon is simply the timeframe that RedYabber chose for their data management and data governance to be fully implemented – yours may be shorter or longer.

Data governance roadmap

Finally, we need to remember that a roadmap should not be a static artefact, because things change regularly. You need to have a method and tool that support an agile approach, so you can adapt your roadmap frequently and keep it alive!

Read Jibility ebook for free

Written by world-class business and IT consultants, Jibility’s ebook takes you through illustrative case studies and real-life examples to quickly build your roadmap as outlined in this article. The book will cover concise and logical six step method, and enables you to build a strategic roadmap quickly.