Establish the Target Operating Model Needed to Execute Your Data Strategy

Engineer a model that is co-owned, codesigned, and strategically aligned.

Analyst perspective

Be relevant, not mature.

Nysa Evagelinos Research Director, Data and Analytics Info-Tech Research Group	Stephen Willis Practice Lead, Data, Analytics, and Enterprise Architecture Info-Tech Research Group

Leaders make three big mistakes when designing data operating models:

They design a model without a strategy. Designing an operating model without a strategy is like designing an engine before you know what it's supposed to power. Sure – it'll feel like you are doing something, but when you bring it to the business and ask them to use it, the first question they will ask is "what's this for?"

Our methodology roots your operating model design directly within your organization's strategy – ensuring you are designing the right model to get the right results.

They get the balance wrong. There are three critical dimensions to data operating models – capabilities that ensure data can be trusted, capabilities that ensure that data is available, and capabilities that ensure data drives tangible value. Leaders and organizations focus on one area to the detriment of the others and end up limping.

Our methodology focuses your thinking and ensures that you are balancing these critical areas of investment into data.

They design in a silo and don't negotiate. Data isn't managed by the data team. It is produced, used, managed, and exploited by every team in your organization. All teams have accountabilities over data. Data operating model design is about figuring out where accountabilities should sit to best deliver your organization's strategy. You cannot decide and dictate this on your own from the comfort of your office and expect others to comply. You must negotiate, directly.

Our methodology arms you with the relationship-forward approach that will lead you to design a data operating model that will get funded, drive value, and be resilient in the face of change.

Executive summary

Your Challenge

Data leaders must define an operating model that:

• Clearly outlines the flow of operations and how different functional areas will work together to meet the strategic goals defined in the data strategy.
• Defines which data capabilities are a priority for meeting strategic goals and the extent to which they must be developed.
• Actively services key stakeholders in meeting their strategic goals while balancing delivery of tactical workloads.

The model must be one that enables strategic outcomes, has sustained stakeholder buy-in, and receives the funding it requires, while maintaining flexibility to adapt to business change.

Common Obstacles

Leaders turn to frameworks that do not serve them

• In attempting to drive capability excellence, leaders turn to common maturity frameworks to assess their performance relative to "industry best practice." These frameworks lack organizational relevance, overrepresent management tasks to the detriment of value delivery, and leave leaders with a list of things to do with little to no strategic justification.

Leaders under-design core operational spaces

• In facing the complexity of the data space, leaders design for technology, often believing that scale and technical design excellence are the primary enablers of value.

Collaboration is challenging when negotiation points are unclear

• Teams often jump from strategy to execution, neglecting the negotiation on accountabilities and ways of working. This results in misalignment, lack of ownership, and ultimately, failed initiatives.

Info-Tech's Approach

Don't struggle to define and visualize the data operating model:

• Conduct focused negotiation discussions with co-owners of your organization's strategy, and align on the right choices for your context.
• Set the stage for data service implementation plans – what will be built, how it will be sustained, and the cross-organizational accountabilities that will shepherd continuous improvement over time.
• Understand the principles that drive success in different data capabilities, and the implications this has for your operating model design.
• Build a model informed by constant, real-world dynamics, developed through collaborative choices, and sustained through shared views of accountabilities and common goals.

Info-Tech Insight

Establishing the right target operating model (TOM) requires you to focus less on selecting the perfect operating model. What matters most is ensuring you're balancing three core success dynamics through direct, intentional collaboration with your organization's leaders. Many leaders believe they've mastered the balance between competing levers, but often they haven't. They tend to over-engineer data orchestration, become disheartened by limited business engagement with the meaning and use of data, and under-design collaboration in the data services – the only layer that delivers tangible value. Understand the dynamics your operating model needs to solve for and the conversations you need to have to align on a choice of operating model that has the foundations it needs to be funded, be sustained, be agile, and drive value.

Your challenge

This research is designed for data leaders who want to understand how to think through the dynamics that matter – not just the steps to follow.

• "Operating Model" is often dismissed as a consulting buzzword. Leaders leapfrog the process of assessing and (re)designing it, skipping from strategy to execution – but they hit a wall when their way of working (i.e. their operating model) gets in the way of execution efforts.
• Existing guidance on operating models describe "what is it" (variations of people, process, technology), archetypes (variations of "hybrid," "embedded," "centralized") and "the things you need to do" (plan, train, shift), but they don't help you think through the problems you need to solve for with data – why data causes these problems and the role your operating models plays in exacerbating or resolving for these challenges.
• The pace of change within the data space is overwhelming. Trending capabilities shift and become obsolete, making catching up seem nearly impossible. You need a stable and flexible mental framework to help you assess your problems and options that is grounded in stable realities around who is best skilled to make certain types of decisions, what problems technology can solve and what problems it cannot solve, and what core dynamics you must solve for over time to continue your path to success.

The biggest challenge CDOs face is how to drive value from data, and the biggest design challenge they face is meaningful organizational engagement. But many data leaders try and design their operating model in their office with the door closed. You can't fix an engagement problem unless you engage.

94% of business leaders believe they should be getting even more value from their data
Source: Salesforce State of Data and Analytics, 2023

Common obstacles

These barriers make this challenge difficult to address for many organizations:

• The pressure for rapid delivery often leaves little room for thoughtful reflection or strategic alignment.
• A constant influx of directives from vendors and consulting firms sets an external pace that can overwhelm internal priorities.
• Concepts like maturity models and capability frameworks can lead teams into complexity without clarity.
• There is often an over-reliance on executive sponsorship, which can stall progress if not paired with distributed ownership.
• Problems are frequently misdiagnosed as literacy issues when the root causes lie elsewhere.
• Teams may lack confidence in translating data capabilities into business-relevant language, making it harder to gain traction with stakeholders.

The pace of transformation within the data space is challenging even the most seasoned leaders

39% of leaders struggle to keep pace with rapidly evolving tools and tech.
Source: Enterprise Strategy Group, 2023

42% struggle with optimize spend to drive end results.
Source: Enterprise Strategy Group, 2023

Insight summary

The data value trinity

All three elements of the data value trinity work in harmony to delivery business value and achieve strategic needs. As one changes, the others need to change as well.

How do these three elements relate?

• Data strategy tells you what you need to achieve to be successful.
• Data operating model aligns resources, processes, measures, stakeholders, value streams, and decision rights to enable the delivery of your strategy and priorities. This is done by strategically structuring data capabilities in a way that enables the organization's vision and considers the context in which the model will operate.
• Governance is the confirmation of goals and strategy, which ensures the alignment of data initiatives and business strategy and is the mechanism by which you continuously prioritize work to ensure that what is delivered is in line with the strategy.

Too often strategy, operating models, and governance are considered separate practices – strategies are defined without clarity on how to support them. A significant change to your strategy necessitates a change to your operating model, which in turn necessitates a change to your governance and organizational structure.

Target Operating Model 101

If the strategy is "what," the operating model is "how."

• A data operating model is a visual representation of the way your organization will function using a clear and coherent blueprint. This visualization demonstrates how capabilities are organized and aligned to deliver on the business mission, strategic objectives, and technological objectives. It represents a series of decisions around ownership of people, process, technology, and ultimately, outcomes.
• Additionally, it should clearly describe the flow of work so that key stakeholders can understand where teams are accountable for data inputs and outputs. Investing time in the front end to get the operating model right is critical. This will give you a framework to rationalize future organizational changes, allowing you to be more iterative and your model to change as the business changes.

Operating models translate data ambition into execution.

• Access to data
• Time to insights
• Quality and Accuracy
• Agility and Responsiveness
• Compliance
  Adapted From: Enterprise Strategy Group, 2023.
  

Target Operating Model Archetypes 101

Leverage common archetypes to express patterns of ownership.

Centralized

Capabilities are owned and operated by leadership within data. The data organization takes full responsibility of the functional areas and maintains control over the outcomes.

Hybrid

Capabilities are owned and operated by a variety of leadership roles throughout the organization. This can shift from data team ownership to other organization leadership. The decision of ownership is typically to enable speed of response or mitigate risks.

Embedded

Capabilities are owned and operated by leadership outside Data. Another area of the organization has taken authoritative power over the outcome of this functional area in order to respond quickly.