A data operating model explains how an organisation turns its data strategy and policies into reality. It brings structure to how data is managed, ensuring everyone understands how their work contributes to shared goals. Strategy, policy and the operating model work hand in hand, each strengthening the other to create a shared foundation that helps the organisation get lasting value from its data.
This is our view of the eight essential elements a good Data Operating Model should have.
The 8 Essential Elements to a Data Operating Model
1. Capability model
The capability model defines the desired future-state framework and supporting materials. Adopt an industry standard capability model, such as DCAM®, that serves as the north star for the data management function. Ensure this future-state is included in the strategy vision statement. Create a data management business glossary of terms that align with the capability model to support common language across the function. Augment the capability model with best practices execution models aligned with the capability model.
2. Data structure model
The data structure model addresses organising the data ecosystem. Start with understanding how to organise your data structure logically. Data domains establish a logical boundary of data across business domains. Understand the vertical and horizontal data across the organisation. Define the relationship between data domains and data products.
3. Organisational structure model
The data structure and the capability model inform the organisational structure. This model establishes the business, architecture, data and technology roles required to operationalise data and analytics management. These roles align with the processes required to achieve all the capabilities in your capability model. Applying a RASCI matrix (Responsible, Accountable, Supportive, Consulted, Informed) to the roles and processes defines the roles, responsibilities and their relationship to one another.
4. Governance structure model
The organisational structure informs the governance structure model. The model defines the method for authoritative decision making about your data, and data and analytics management at the right operating level (i.e. enterprise, operating unit, domain) so it has authority.
5. Infrastructure Model
The strategy informs the infrastructure model. The model defines the technology in capability categories required to manage the data and analytics processes. The platform will support the data and analytics management processes to manage the metadata required to achieve trustworthy data accessible to the business to achieve value for the organisation. The operating model will confirm the current state platform and identify high-level platform gaps.
6. Funding model
The funding model identifies the various funding accountabilities and sources and the methodology for securing funding in alignment with the organisation’s overall funding cycle and protocols.
7. Data culture model
The data culture model establishes pillars for supporting a strong data culture organisation-wide in order to leverage data and analytics for decision making and problem solving. The value of data culture in an organisation is achieved by winning the hearts and minds of people to respect the value of data and understand their role in achieving data value.
Want to know more? Check out Anatomy of a Data Culture and the Data Culture Assessment.
8. Metrics
Defining metrics is an important step in ensuring the organisation can measure the operating model’s success.
- Align the metrics with the program outcomes to ensure they accurately measure success
- Make the metrics actionable and measurable to track progress and make data-driven decisions
- Set targets and baselines for each metric to track the impact over time
- Assign ownership and accountability for monitoring and reporting on the metrics to specific individuals or teams
Need help refining your data operating model? Talk to us at Ortecha.
