Model 5: Data Steward by Project
A data stewardship approach by project may be the fastest way to introduce the data stewardship concept to organizations. Generally, consultancy companies prefer this approach by introducing stewardship on high-profile, data-intensive strategic initiatives. Some examples are data integration– and business intelligence–related projects.
Responsibilities related to provisioning and data quality are given to experienced team members. This approach is applied where the project implementation team is strong. As team members are starting, expertise in the data domain grows with time.
Figure 6-6 demonstrates data stewardship by project. Data governance under corporate governance umbrella of corporate governance and IT governance through project management office.
Figure 6-6. Project stewardship
As part of organization hierarchy, corporate governance includes management of IT governance. The IT governance team manages the project management office (PMO), along with data governance. This model is a temporary measure until the project ends. The PMO manages data stewards to interdependent projects ensuring synergy with primary and secondary domain expertise assigned. This gives an opportunity to formalize and extend data management standards, policies, and procedures, and to introduce data steward roles to other projects.
The benefits of data stewardship by projects are as follows:
• Increase speed of execution by introducing stewardship quickly
• Flexibility to customize data stewardship processes as per organization culture and desired outcomes
• Ability to Ability to realize initial and quick benefits and then subsequently extended and refined for broader deployment The challenges of project-oriented stewardship are as follows:
• As projects are of finite time and effort, once a project finishes, the team may get dispersed or allocated to different projects, which means that data stewardship can lose its momentum.
• Finding incumbent skills can be challenging for small companies or companies that use project-oriented or consultancy approaches. It is difficult to find skillful resources in data management, governance implementation, and execution.
As data stewardship is extended to different projects, adoption of the idea of data stewardship increases, and the role and model can evolve. Either the model will shift to another model or is modified or combined with another model to make it a hybrid; e.g., using both functional and system experts. The framework described is good for the introduction of data stewardship to an organization to prove value and without any significant disruption.
There are multiple levers through which data governance is implemented. As mentioned earlier, data governance includes designing and implementing the operating model, assigning stewardship by establishing role, responsibilities, and tasks, and enabling tools. This helps organizations better understand and protect data privacy and security, and fulfill compliance obligations.
Data governance framework comprise of following three capabilities.
Process: Understanding and setting up legal requirements, operating model policies, enacting procedures as per strategic objectives to consolidate business requirements, metrics, and rules associated with these objectives. Processes are set up around people and technologies.
People: Setting up teams consisting of individuals from within the organization with clearly defined roles, responsibilities, and tasks, and providing resources to them to perform duties, and also providing guidance. Increasing and including new skills about processes and technologies.
Technology: Using technologies including tools to aid people and processes in identifying and analyzing dataflows and reducing risk.