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The discussion around data has evolved tremendously over the past decade. High-fidelity data has become a prized asset and a key business differentiator for many companies and organizations across the globe. 

This carrot is enough of a reason for many companies to invest in maximizing the value from their data and maintaining a healthy data environment. For others, the stick of ever-increasing data regulation, such as the General Data Protection Regulation (GDPR) in the European Union or the California Consumer Privacy Act (CCPA), is a powerful motivator.

Maintaining your data is no longer optional

Prioritizing where to invest to improve data-related capabilities is a primary challenge. There are many areas to choose from across the spectrum of a model data organization—which include data governance, data management, data engineering, data platform, BI and reporting and data science, to name a few. But we consider the underlying policies, processes and people shaping and maintaining your data environment as foundational elements that need to be addressed from the very beginning. 

Often this gets described as a data governance and management program, an area we frequently see companies investing significant resources in to grow as part of a broader data-driven transformation effort. We think this is a great area to focus investment, but doing this intelligently requires understanding the specifics of these topics.

This article outlines a detailed data governance and management framework, which can be leveraged along with a set of prioritized use cases to get a foundational understanding of an organization's data maturity. We will also show you how to use that framework to build a roadmap to grow your data capabilities and prioritize investments. 

Defining data governance and data management

At WWT, we define data governance and data management as follows:

  • Data governance is the function that provides the overarching strategy and policy direction for data at an organization.
  • Data management is the function that implements the strategy and policy set forth by data governance via people, process and technology solutions.

WWT has developed a holistic framework to address these concepts that is rich in supporting detail. In order to make it holistic, we added a third category, data infrastructure, which is closely intertwined with data governance and data management:

  • Data infrastructure is the infrastructure that supports the data environment ranging from data platforms and the accompanying data architectures to the tools used for data analysis.

Together, these three areas provide the foundation WWT uses to evaluate data maturity.

A framework for evaluating foundational data maturity

WWT data maturity framework

Data governance

Data governance is divided into several distinct areas. First, there are the roles and responsibilities associated with a data governance and data management program. It is critical to have the right structure in place that fits the needs and culture of your organization—this can vary significantly.

Next, there are the policies which shape and define data governance. We choose to break these into two different sub-categories: data and security. Security warrants its own focus due to considerations such as access control, data classification and encryption requirements. Risk and compliance provides oversight focused on evaluating risk factors and tracking compliance against the policies defined above. 

Finally, there is communication—sometimes an afterthought—which is essential for sharing data governance and data management messaging across the organization. 

data governance sub-categories

Data management

Data management can be thought of as the execution function for the policies and direction set by data governance. It covers the entire lifecycle of data from beginning to end, including how it is described (through metadata) and how it is used to generate insights.

It starts with data integration—onboarding new data sources into a company's data ecosystem. This is where data not only gets integrated in an organization's data environment, but also onboarded into data governance and management processes.

Lifecycle management is the next stage and includes processes associated with maintaining the data and access to it until retirement. Metadata is a sub-category which addresses anything that is related to information about data. This is commonly referred to as a data catalog.

The last sub-category is data services, which addresses how data is being consumed by the organization through reporting and data science.

data governance sub-categories

Data infrastructure

Data infrastructure usually isn't discussed in the same conversation as data governance and management—organizations often keep the people and process side of data separate from the technology. However, since it is closely intertwined with those areas, we believe it is therefore important to consider as well.

It starts with the platform, which refers to all the data systems and data platforms that support the data environment (but is not intended to go so deep as to evaluate the physical and virtual hardware/software that supports them). Sitting on top of those systems and platforms is the data architecture layer. This includes the entire database landscape at an organization. 

Finally, services and tools refers to the data services (e.g. APIs) and tools used in the data environment.

data governance sub-categories

A use-case driven approach to grow data maturity

Armed with this framework, you can conduct a thorough evaluation of the foundational data maturity of an organization once all the necessary information has been gathered. The hard part is collecting the necessary information and then turning the evaluation output into a future-looking roadmap to prioritize investment. 

When conducting information gathering activities, we recommend taking a use-case driven approach to execute the evaluation. By anchoring the evaluation (and subsequent recommendations) around a small set (3-5) of concrete, high-priority data-driven use cases, the exercise becomes less academic in nature and ensures that the outputs are tightly linked to important initiatives for the organization. 

While it is important to anchor the evaluation with specific use cases in mind, the scope of the evaluation should not be limited to only those use cases. It should look broadly across the entire data footprint of the organization in order to be holistic and avoid creating data siloes in the future. 

To complete an end-to-end assessment concluding with a future roadmap, there are four major activities to complete.

  1. Prioritize use cases: Depending on data maturity of the organization, data-driven use cases may be readily available or may require discussions and thought leadership to identify and develop. Either way, it is recommended to prioritize three to five use cases that represent a diverse set of business interests, capabilities and complexity to support the evaluation.
  2. Gather information: In today's environment, it is common for data to be leveraged regularly across the entire organization. As a result, information gathering efforts require interviews and ideation sessions with a broad set of stakeholders. While it is important to thoroughly understand the use cases and associated gaps from a data maturity perspective, that analysis should not be limited to only those use cases. It is also important to get a more general understanding of the challenges and needs from data users across the entire organization. This will ensure that the effort is truly a holistic evaluation.
  3. Perform evaluation: Once the inputs have been gathered, all the raw material should be available to complete the current state evaluation. It is recommended to build this in an iterative fashion while conducting information gathering efforts—don't wait until your last interview to start populating your evaluation. The more stakeholders that can be involved in validating the findings (based on their background and areas of focus), the stronger the buy-in will be behind the final evaluation and recommendations.
  4. Build future roadmap: Once the current state evaluation is complete, the logical question to answer is: "so where do we go from here?" That gets answered through the strategic roadmap. The roadmap is composed of future capabilities (i.e. capabilities that don't exist today) that will address the challenges and gaps identified in the current state evaluation. These future capabilities will facilitate the organization's growth along the data maturity curve.


Organizations are faced with more pressure than ever before to grow their data related capabilities and, in particular, have a keen interest in maturing the underlying policies, processes and organizational structure shaping and maintaining their data environments. WWT's framework, composed of three major categories (data governance, data management and data infrastructure), provides a method for holistically evaluating an organization's foundational data maturity. 

By leveraging a use-case centric approach, you can ensure that the findings and recommendations are tightly aligned to important business outcomes and build a strategic roadmap to achieve the associated future-state vision. We hope this helps advance your thinking about how to move up the data maturity curve.

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