Data Operating Model

Anchor Everything in Business Outcomes

I’ve seen many data initiatives focus inwardly on data itself rather than on the results it can enable. That’s also where people get data strategy wrong, as they somehow think it’s about the movement of data, which it isn’t. If you want an integration strategy, then call it that!

As I’ve always maintained, data is an enabler, whether in raw form or as information. A world-class data operating model begins with a clear vision of the desired business outcomes. What role should data play in improving customer experiences, boosting revenue, reducing risks, or cutting costs? By defining the ultimate goals from the outset, data becomes a purposeful asset that directly serves the broader ambitions of the business.

Build a Framework for Cross-Functional Collaboration

At the conference last week, many people talked about there being many silos in their organisations, and that is very true across all industries and sectors. Why? Because the organisational design hasn’t been set up to incorporate collaboration. I have consulted many organisations, where even the data teams live in silos. Go figure!

An effective data operating model isn’t siloed. Instead, it’s a real collaborative framework that connects the business, data, and IT teams. I say 'real' because it has a true organisational design set up for masterful collaboration, breaking down deep silos and embedding partnering and co-creativity into the mix.

In addition, the structure needs to be based on transparent goals, clear lines of accountability, and regular, open communication. When done right, it helps every team see the big picture, understand their unique role, and realise how they collectively contribute to value creation. This is how data shifts from being an isolated asset to a shared resource that drives alignment and measurable results.

One organisation I worked with, a large software company, had so many fractious silos, which were created by the founders, and I had to start at the highest level to remove this kind of thinking. This is where honest conversations must start and where proper change management comes into play. Not just lip service.

Embed Data Fluency at Every Level

No data strategy will thrive without data fluency woven into the organisation’s culture. I’ve always maintained that a “data culture” isn’t necessary; what is necessary is to weave data into the organisation's existing culture. The culture is already established: there are norms, structures in place, rituals and rites, power structures, and stories. Many organisations have become too dependent on a bunch of trainers going around trying to teach people SQL, how to build a dashboard, or how to build a data model. Becoming data fluent goes beyond technical skills, which, let’s face it, is what most data literacy training programmes are focused on today.

It is about cultivating a mindset across all levels that values how data can influence decision-making and how AI (or let’s just call it the fad GenAI) can help make employees more efficient. Leadership plays a critical role in promoting data fluency, fostering curiosity, and ensuring that employees have the resources and confidence to use data meaningfully.

When data fluency is embedded in the culture, insights will flow more freely, and people will understand the decisions and actions they need to take, ensuring the organisation becomes better equipped to act on insights from a decision-led perspective, rather than a technical one.

Embrace New Ways of Working

I have worked with many organisations worldwide to implement their operating models, and one thing I must ensure is that it is not a rigid framework. If it’s rigid, it gets stifled, it's overly governed, and decisions take longer to make. If that sounds familiar, yes, that is your organisation.

The operating model must be adaptable, embracing new ways of working that move beyond traditional hierarchies. In an organisation, I set up a virtual data squad comprising several parts of the data team and implemented a demand management function that streamlined requests and workflows. It doesn’t have to be a virtual team; it could mean forming agile, cross-functional teams with the autonomy to experiment and innovate or introducing faster cycles for decision-making from insight to decision to action.

Organisations today need to rethink traditional structures, and yes, of course, for many, that can be very uncomfortable. Organisation design and integration aren't easy for HR professionals; imagine how difficult it is for data teams who don’t have a clue about this stuff.

Organisation design is just one factor, alongside flexibility, which is essential for responsiveness in organisations and the competitive environment in which they operate. Organisations that adopt flexible, agile approaches can compete far more quickly and seize opportunities faster, responding more effectively to market shifts.

Focus on the Right Metrics

Metrics are just way out of line these days! Too many, too confusing and focused on the wrong areas! Success within your operating model requires tracking THE-right metrics. Rather than just measuring output, a world-class operating model should track the impact of data and AI initiatives on business outcomes. The operative word here is business outcomes. We aren’t tracking whether your data quality has improved or how many data owners have been established! Everything must be connected to a business outcome.

Key metrics might include efficiency gains, revenue growth, the speed at which data can inform strategic decisions, and so on. By measuring the results that matter, organisations reinforce the role of data as a strategic driver, giving you credibility within the data team and demonstrating your understanding of the business and your contribution to both immediate goals and long-term objectives.

Keep Value Creation at the Core

The cornerstone of a high-impact data operating model is value creation. Before we get into this, I want to be clear about value creation in the context of Data & AI.

Value creation refers to the measurable and strategic benefits that organisations derive from implementing data and AI initiatives that directly contribute to business objectives. Unlike a focus purely on technology or data management, value creation emphasises how data and AI drive outcomes such as revenue growth, operational efficiency, customer satisfaction, and innovation.

Data and AI strategies alone don’t drive results unless they are embedded in an operating model that aligns with core business needs. From establishing clear, outcome-focused goals to enabling cross-departmental collaboration and data fluency, each aspect of the operating model should be oriented toward driving value. When every element of your data and AI strategy supports broader business objectives, they become a catalyst for meaningful change and transformation.

What Are the Clear Benefits of Implementing a World-Class Operating Model

Organisations that build a robust operating model experience a range of strategic, organisational and operational benefits:

1. Increased Agility and Responsiveness: With an adaptable structure, teams can respond to market changes and make decisions quickly, capitalising on new opportunities as they arise.

   • Example: A global manufacturing company used real-time data monitoring to identify supply chain bottlenecks early and make swift adjustments. This proactive approach allowed them to respond to unexpected demand surges and supplier delays, improving their response time by 30% and minimising potential losses.

2. Higher Operational Efficiency: By aligning data practices with business processes, teams can eliminate redundancies, streamline workflows, and reduce time-to-insight, leading to substantial cost savings.

   • Example: A telecommunications provider utilised data insights to optimise network traffic and reduce bandwidth waste. By adjusting service offerings based on user data, they cut operating expenses by 15% and improved network efficiency, leading to smoother service delivery and fewer outages.

3. Enhanced Decision Quality: When data fluency is embedded across departments, decisions are grounded in business insights, leading to more accurate forecasting, risk management, and strategic planning.

   • Example: A retail chain implemented an inventory management solution to more accurately predict demand for specific products. This enabled managers to make informed purchasing decisions, resulting in a 20% reduction in stockouts and excess inventory costs.

4. Improved Customer Experiences: A value-focused, outcomes-based approach enables organisations to better understand customer needs, preferences, and behaviours, leading to personalised services, higher satisfaction, and stronger customer loyalty.

   • Example: A financial services firm analysed customer transaction data to provide personalised financial advice. By tailoring services to individual needs, they boosted customer satisfaction by 40% and improved retention rates, especially among high-value clients.

5. Revenue Growth and Innovation: Operating models fuel innovation by making it easier for teams to test new ideas and initiatives. With insights readily available, organisations are better positioned to identify and capture new revenue streams.

   • Example: An online marketplace leveraged data and analytics to develop dynamic pricing algorithms that enable real-time price adjustments based on demand and competitor pricing. This strategy helped them increase revenue by 25% during peak shopping periods and strengthen their market presence.

6. Stronger Competitive Positioning: Organisations that leverage data and AI as a core strategic asset gain a clear competitive edge by optimising operations, reducing risks, and anticipating industry trends before competitors.

   • Example: An insurance company used predictive analytics to anticipate policyholder needs and proactively offer relevant products. By staying ahead of customer expectations and industry trends, they gained a competitive edge, acquiring new customers 20% faster than their closest competitor.

Making It Real

Data and AI should not be isolated assets, managed solely by IT or data teams; they should be core components of how the organisation functions and competes. To truly be impactful, they must be embedded in a well-structured operating model that adapts to the business and grows with it. A well-crafted operating model turns data and AI investments into engines of productivity, efficiency, and revenue generation.

A world-class operating model does more than just support today’s goals; it enables resilience and agility, equipping the organisation to thrive in a fast-paced, evolving market. Organisations that understand this won’t just keep pace; they will lead, using data not as a byproduct of operations but as a strategic cornerstone that guides every significant decision.

If your organisation is ready to elevate data from isolated insights to a strategic advantage, it’s time to take the next step. Invest in an operating model that integrates data into every layer of the business, from daily operations to high-level strategy. This commitment will empower your teams, streamline your processes, and transform your data into a powerful driver of growth and resilience.

Don’t wait for change to come to you; make data and AI a fundamental part of how you work, innovate, and lead. The path to value creation is within reach; seize it and build a foundation that drives true, lasting value.