Is Agile Data Governance Right For You?

Traditional data governance approaches: centralized, decentralized, and hybrid, focus on governance councils and committees. These organizations are effective at driving development of policies, processes and overseeing enforcement. However, they are often seen as unwanted bureaucracy when enterprise needs to bring out new products and services to the market quickly. The answer may be in retooling of the data governance program to ensure that it is relevant and drives value for a rapidly transforming data-driven organization.

The concept of data governance is still valid: it is the decision-making framework to establish and enforce the strategy, objectives, policies, procedures, and standards to effectively manage data.

An effective data governance program:

• Determines what to govern and establishes business value driven by governance
• Ensures that the right people are represented and creates accountabilities
• Articulates decision-making and communication processes for the roles, responsibilities, accountabilities, ownership, and decision rights needed to govern
• Adapts to meet the organization’s changing needs
• Represents the governance framework that isn’t a hierarchical
• Provides the right resources to deliver the right business value at the right time

Agile Data Governance

There is an emerging interest in applying agile thinking to existing data governance programs: a so-called agile data governance model.  This model intends to enable data democratization and to recognize the rise of the “data citizen” role. What makes agile data governance compelling is that, unlike traditional models that emphasize a top-down approach, agile emphasizes bottom-up, encouraging data accountabilities as close as possible to the point of data usage and consumption.

Agile data governance is a model that enables those who create the data and use the data to participate in the governance activities, simultaneously empowering them and connecting them to the knowledge about the data to improve enterprise-wide data literacy. This approach encourages data practitioners to follow established governance guidelines and avoids rigid, prescriptive procedures driven by the governance decision-making framework.

Don’t mistake agile data governance for an agile project management framework.  Agile governance isn’t about epics, sprints, or user stories.  It attempts to leverage existing formal and informal data leadership that makes sense, especially at a tactical level, in order to minimize the need for creation of formal data governance bodies and roles.  In other words, tactical data governance is accomplished through informal means, avoiding formal data working group structures by data domain.

An agile model is also well positioned to leverage new and improved technologies that monitor and enforce governance practices, and automate governance functions, while supporting the enterprise-wide demands of distributed data practitioners and the increasing complexity of their data-related concerns.

Agile Helps to Re-imagine Governance Responsibilities and Titles

Adopting an agile governance model does not eliminate the need for traditional roles that include titles such as data governance lead, data owner, and data steward. Instead, it encourages you to re-imagine those roles (possibly renaming them) and to embrace new ones.

For example, the traditional data steward may be re-imagined as a data trustee or data guardian. Data owners may become data champions. These title changes are prompted by greater awareness of the participant’s critical accountabilities for data, coupled with a desire for more engaging and less authoritarian-sounding titles.

Agile data governance thinking may also contribute to more granularity in certain roles. The traditional data steward may be subdivided and further defined in different ways.  A business data steward may be defined as equivalent to what was commonly called a data steward in the traditional model. A technical data steward, previously known as a data custodian, becomes the business steward’s IT counterpart, who is responsible for tools that support the business data steward, and is also likely to be an IT subject matter expert in the systems and applications that support a particular data domain.  These steward roles work hand in hand to ensure business processes are supported by technology infrastructure, with the technology data steward recommending improvements in processes based on better and more creative technology usage.

An agile model may identify explicit stakeholder roles, such as data consumers, data producers, and other participants in data governance, even when they may not be formally engaged in governance activities. Their participation is truly important and should be recognized.

New Agile Governance-inspired Roles

Today, many companies are expanding the C-Suite by hiring a Chief Analytics Officer (CAO) who, is the executive turning data into decisions. The CAO is often a counterpart of the Chief Data Officer (CDO).  A CAO typically oversees the entire analytics process and environment. The role may report to the CDO, but more commonly is seen as a peer.

Also new to the governance landscape is the role of a data acquisition lead, who is responsible for the end-to-end process of identifying new data the organization needs. This role oversees the negotiation and procurement of data, its ingestion, and ensures that it meets all internal compliance requirements (e.g., governance, legal, privacy, and security). Another responsibility of the data acquisition lead is making the enterprise aware that data is available through effective communication and by creating a provisioning or access strategy.

The data acquisition lead doesn’t just manage data acquired from outside sources. In many large organizations, this role also pertains to internal data moving around the organization. As data governance has evolved to become a service line to the rest of the business, this role has become increasingly important in facilitating the creation, distribution, and delivery of services.

Putting Agile Data Governance and its Roles into Practice

Here are several factors to consider when standing up or reworking the data governance model in your organization:

• Take into account your company’s existing decision-making processes and structure. This includes organizational complexity, maturity, and data domain complexity and maturity. Consider how governance can best scale across the organization.  Would agile thinking be rewarded or avoided?
• Make sure the governance team is cross-functional and includes people from multiple lines of business. This is key to obtaining buy-in and to ensuring the adoption of standards, processes, and guidelines at the tactical level of your organization.
• Provide managerial and leadership support to self-directed teams.  Taking a bottom-up approach means empowering those who create and use data to participate in governance activities including decision-making.  Tactical-level data practitioners may require coaching and support to feel comfortable in setting their own direction.
• Encourage teams to meet on a regular basis and hold them accountable for doing so. Establish a cadence where people regularly come together to identify issues and create and implement solutions, particularly those connected to new and emerging programs, so that governance remains connected to the real-time needs of the business and isn’t seen as an impediment.
• Promote clear, open communication; not just with your governance counterparts and first-level stakeholders, but with all data practitioners.  Recognize there are other stakeholders, project managers, and potentially other domain stewards who will benefit from involvement and, at a minimum, at least periodic status updates. Ensure these communications are frequently face-to-face and leverage multiple channels, with e-mail being the least frequent.
• Regardless of where you are on your data governance journey, whether establishing an operating model or revising an old one, leverage governance work that’s already been done at your organization. This helps reduce the amount and the perception of change the organization is required to make, and eases adoption of a new, improved model.

Agile data governance doesn’t mean there isn’t a need for rules and decisions about data such as those provided by a centralized, decentralized, or hybrid approach. Taking an agile approach incorporates the best parts of traditional models but shifts the focus to providing support that empowers individuals to collaborate and get more value from data.

Postscript: Many thanks to Kelle O’Neill and the consultants of First San Francisco Partners for their thinking and leadership in the area of agile data governance.

Mr. Luikart has over 40 years of in-depth information technology and management information systems experience in the financial services, government, higher education, and consulting sectors. A former Chief Information Officer, he has extensive business and program management experience, at various departmental and enterprise levels, including business transformation and assessment, data strategy and governance, information systems strategy and architecture, business application development and portfolio management, data warehousing, business intelligence systems, document and content management, electronic commerce (EDI), Enterprise Resource Planning (ERP) selection and implementation, enterprise networking, and advanced technology integration.

His technical experience spans a wide range of platforms and architectures including traditional, large mainframe-based data center environments, distributed and multi-tier environments, and more recently, leading edge virtualized hosting and cloud-based configurations supporting data lakes and analytics. He has extensive experience with data warehousing and business intelligence platforms, including large-scale database systems and BI reporting and analysis tools, as well as ERP, supply chain, and enterprise networking technologies.

Mr. Luikart’s current focus is on the transformation of business and the creation of high performing organizations through strategic data management. He has extensive experience with the development of data strategies, data governance and quality programs, data warehouses and analytics platforms, I/T strategy, business process management, business assessment, enterprise networking, ERP and supply chain management, advanced technology integration, I/T architecture and systems integration, project and program management, and lifecycle methodologies.