Data Governance: Priority in a Data-Driven World

Creating a roadmap to protect and manage vital data assets


As organizations continue to embrace the benefits of leveraging data – both big and small – data governance as a strategic business program has never been more important. A solid data governance policy not only mitigates the risks associated with data mismanagement and possible security breaches but also provides the ownership, accountability, process, planning and performance management required by every organization, department and individual that handles data.

Data GovernanceWhile some may regard data governance as managerial overkill or even unnecessary, a number of common data-related challenges make the case for why proper governance is critical today:

- Redundant or inconsistent data residing in organizational silos undercuts a single view of customers;

- No clear ownership of key data assets obfuscates who is responsible for managing and protecting the data;

- If processes and other intellectual property related to data management are undocumented, that knowledge typically disappears when employees leave the organization;

- Poor data quality can reduce the value of the assets; and

- The absence of a common naming convention for metadata can hinder effective searching.

And these are just a few of the many issues that can leave an organization vulnerable to inefficiencies and ineffectual initiatives downstream if they are not managed systematically.

Data governance provides a set of processes to ensure key data assets are formally managed throughout an enterprise. The most common elements include policies and procedures around data integration, data storage and retention, data issues management, data quality KPIs and third-party access. However, the specific elements of a data governance program will depend on the organization’s business priorities, such as regulatory compliance, risk management and its reporting structure to address governance gaps or pain points. Once these issues have been clarified, a roadmap can be developed to guide the effective implementation and management of a governance program, which may include some or all of the elements listed below.

Data governance – Common program elements

Policies Data cataloguing
Data privacy and compliance Data documentation
Data breach management Data transfer and data migration
Data standards Data integration
Data security and access Data storage
Data stewardship Data retention
Data quality Data issues management
Data architecture and data models Data quality KPIs


As with most business process and workflow management initiatives, creating a roadmap for data governance is critical. It provides not only the building blocks for structuring the program and putting it in place, but also defines the rules for ongoing management and modification. The task of roadmap development may fall to one key individual such as a Chief Data Officer or Chief Privacy Officer, but often it will be spearheaded by a multi-person, multi-departmental task force (sometimes referred to as a Data Governance Office). The benefit of the latter approach is that, by including key stakeholders from multiple business units, departments and functional areas, and gaining their buy-in on the key objectives and processes for data governance, there will be a higher degree of compliance across the enterprise.

Developing a data governance roadmap includes the following key steps, typically led by a Data Governance Steering Committee established within the Data Governance Office:

      1. Define the program scope, priorities, mission, roles and development timelines.

2. Establish regular data governance overviews and educational opportunities to involve stakeholders in the effort, as well as to identify Data Stewards by functional area (individuals who can act as representatives of the Data Governance Office in their department or group).
3. Develop, document and communicate data policies including data access controls, and identify any key data issues.
4. Prioritize data issues to be addressed and implement tools for data cataloguing (e.g. data dictionary, metadata naming conventions, etc.).
5. Initiate data quality programs (to address data priorities from step 4) and implement tools to measure the value of data governance (e.g. scorecard to measure compliance, issues resolved and policies documented).
6. Convene quarterly status meetings for the Data Governance Office to review policies and procedures such as measuring and new metric setting, issues identification and trending, reporting and communications and collaboration.

With data increasingly viewed as a critical asset to any organization, it only makes sense that data governance should play an equally vital role. It is a strategic priority that must be embraced and led from senior management, and it is of concern to every individual or group that creates, collects, processes, manipulates, stores or somehow handles data. In today’s data-driven business world, that means virtually everyone. And to be successful, data governance must be established as a program guided by documented policy reinforced with ongoing communication among all stakeholders. The importance of the program cannot be overemphasized, and its value, supported with metrics, should be publicized throughout the organization.


Evan Wood is Senior Vice President, Marketing and Sales Operations, at Environics Analytics.