A few months ago I was catching up with a friend of mine who makes a successful living as a residential architect and he was sharing with me a unique approach he takes with his customers...
“Most architects ask about the nuts and bolts of designing a home. They ask questions like, ‘How many bedrooms do you want? Do you want a garage?’ I don’t focus on the rooms and square feet. I focus on the lifestyle of the homeowner. Are they indoors or outdoors people? Are they social or introverted? Their responses tell me so much more about how the house than anything they could tell me about the ideal number of rooms or the floor plan. Simply put, I focus on the client’s lifestyle which in turn dictates the home’s demands the priorities of the design.”
This serves as a good analogy because when building a fit-for-purpose Data Management Organization we have a similar outlook. Much like how most homes have a bedrooms, a kitchen, and a living room -- most data organizations share common attributes including components of Business ownership, an Operating Data Governance layer, domain Subject Matter Expertise, and support from IT. However, instead of drawing up a generic blueprint and stuffing names into the typical boxes, we design the model by complementing the dynamic needs of the business to the “data demand” imposed on the organization. In other words, we think “lifestyle” instead of rooms and floor plan. Here are some of the key focus areas:
What are the tasks and activities the data organization will manage?
- Data Maintenance: How do we manage requests, workflow, mass changes/updates, mass loads, and control change
- Data Governance: How will we define, document and govern standards and rules, data processes, workflows, data processes, and change management
- Data Quality: How will we define and establish data quality metrics, create data quality reports, publish Data Quality reports, and remediate Data Quality issues
- Other: What are the data tools we leverage, how will we manage project support, and flag records for deletion/obsolete/inactive and archiving
- Ownership: Who will be asked to provider, maintainer, approver, and consume the information?
- Contextual Knowledge: Will a centralized or federated team be able to apply the right value in support of the process, analytical or compliance requirements?
- Maturity of tools for automation: What are the maturity steps necessary to take before we are ready to deploy?
- Central, Federated, Hybrid, Decentralized: This may vary by domain. Enterprise attributes versus local attributes for example.
- SLA’s & Plant needs: For example, plants operating under Just-In-Time models
- Data Volumes
- Service Levels
- Business Complexity
- Tool Maturity
By taking a “data demand” approach, we are able to build a sustainable Data Management Organization which complements the “lifestyle” associated by the business priorities, needs and culture.
Ready to discuss a DMO that aligns with your data demand? Contact us for a personal consultation.