Data Operating Models patterns with Dylan Anderson
AgileData Podcast #60
Join Shane Gibson as he chats with Dylan Anderson about the patterns required to define a Data Operating Model.
Data Ecosystem Patterns
https://thedataecosystem.substack.com/p/issue-13-defining-the-data-operating
https://thedataecosystem.substack.com/
Listen
Listen on all good podcast hosts or over at:
https://agiledata.io/podcast/agiledata-podcast/data-operating-models-patterns-with-dylan-anderson/
Read
Read the podcast transcript at:
https://agiledata.io/podcast/agiledata-podcast/data-operating-models-patterns-with-dylan-anderson/#read
Google NoteBookLLM Briefing
Briefing Document: Agile Data Podcast - Operating Models for Data Teams
Source: Excerpts from "Agile Data Podcast" featuring Dylan Anderson of Perfusion Consulting.
Subject: Operating Models for Data Teams
Executive Summary:
This podcast explores the crucial role of operating models in enabling data teams to effectively support business strategy. Dylan Anderson, a Data Strategy Consultant, breaks down an operating model into three core components: Oversight & Direction, Structure of Delivery, and What People Do. The conversation highlights the common pitfalls of data strategies – being too business-focused without technical grounding, or vice-versa – and emphasizes the importance of aligning data strategy with overarching business objectives, fostering collaboration, and promoting continuous improvement within data teams.
Key Themes and Ideas:
Data Strategy Must Support Business Strategy:
The fundamental premise is that data strategy should be derived from and directly support the organization's overall business strategy.
Shane poses the question, "First thing I asked for is the organizational strategy, because my view is the data strategy is to support. The business strategy to be successful using data." This highlights the dependence of data strategy on a clear understanding of the business's goals.
Anderson agrees, stating, "The data strategy is really there to support the business strategy and enable that."
A key indicator of alignment is the team's understanding of how the organisation makes money. Shane suggests a simple, yet insightful question to assess this understanding.
Bridging the Gap Between Business and Technical Aspects:
A common problem is the disconnect between business-focused data strategies (fancy PowerPoints lacking tangible implementation) and technically-driven strategies (focusing on data and technology without clear business objectives).
Anderson notes, "One way is a pure business focused data strategy...but what you don’t get is the technical architecture...On the other side...you get the technical data strategy...but without talking to the business."
A successful data strategy requires both a strong understanding of business needs and the technical expertise to execute.
The Three Components of an Operating Model:
Anderson outlines a framework for understanding operating models based on three interconnected circles:
Oversight & Direction: Leadership sets the strategy, ensures alignment with business goals, and establishes performance management.
Structure of Delivery: The tangible processes, team structures, and reporting lines that enable the achievement of strategic goals.
What People Do: The day-to-day job descriptions and responsibilities that execute the strategy.
Importance of Change Management and Stakeholder Buy-In:
Implementing a successful data strategy requires more than just technical expertise; it demands effective change management and stakeholder buy-in.
Anderson emphasizes, "If you don’t think about this as a journey, and if you don’t involve the people who are going to be impacted by it, then it’s not going to work."
Data Teams as Horizontal Enablers, Not Siloed Verticals:
Many organisations incorrectly structure data teams as separate verticals, when they should be functioning as horizontal teams that support various internal business functions. This results in siloed work and limited collaboration.
Anderson: "A lot of organizations, their data teams are to support internal businesses...Yet most companies set the data team up as another vertical...whereas it should actually be a horizontal."
Key Patterns within Oversight & Direction:
Performance Management: Measuring progress, which often falls through the cracks, is essential to demonstrate the value of data initiatives.
Data Strategy and Team Goals: Clear alignment and direction are vital, ensuring everyone understands their role in achieving the overall strategy.
Governance Forums: Bringing people together to ensure alignment (and police policies), but these must be led effectively and have clear objectives to avoid being unproductive.
Operating Model Principles: Establishing a culture that fosters collaboration and effectiveness.
Program and Org Leadership: Requires both data leadership (accountability) and organisational leadership (direction) to be successful. Find and utilise internal "change agents" to help unblock progress.
Importance of Delivery Structure:
Reporting Lines: Determining who directs work versus who manages people's well-being.
Team Design: Utilising patterns like Team Topologies and Unfix to create effective team structures. Addressing the challenge of balancing fast-turnaround requests with large, long-term projects.
Workflow and Delivery Processes: Mapping out the entire process and ensuring effective communication at every stage. This includes stakeholders at scoping, dev and testing stages.
Communication is Key: Don't assume "no news is good news". Update stakeholders regularly with progress (and show incremental value even if the overall product isn't yet ready) to avoid the perception that the data team is a "black box".
Playbooks as a Communication Tool:
A "playbook" is recommended, a visual document that describes how the data team works to new team members and stakeholders.
It articulates the team's delivery models, engagement gates, and stakeholder involvement points.
Includes the team's value stream, team design, ceremonies and definitions of ready and done.
Evolving Roles and Responsibilities ("What People Do"):
Understanding individual career goals and tailoring roles to incorporate growth and development.
Moving beyond strict job descriptions to foster flexibility and engagement.
Understanding the different "personas" within a data team, their skills and specialities, and mapping gaps to develop training and hiring strategies.
Quotes:
"Everybody seems to focus on technology. They don’t focus on the people or the process or those other really important things that sit around a team and their technology to deliver." - Shane Gibson
"The data strategy is really there to support the business strategy and enable that. And how does data factor into what you do as a business and make what you do as a business better?" - Dylan Anderson
"If you don’t think about this as a journey, and if you don’t involve the people who are going to be impacted by it, then it’s not going to work." - Dylan Anderson
"An operating model is basically a ways of working that helps you understand how to collaborate across the organization and coordinate to deliver the initiatives and the tasks in front of you." - Dylan Anderson
Actionable Insights:
Ensure your data strategy is firmly rooted in your organisation's business strategy.
Prioritise clear communication and stakeholder engagement throughout the entire data project lifecycle.
Don't treat your operating model as a static document; encourage continuous iteration and improvement.
Focus on building a collaborative culture that empowers data teams to contribute effectively.
Structure your data teams as horizontal enablers that support various business functions.
Implement clear reporting lines and establish defined workflows and delivery processes.