Introduction
I was fortunate enough to speak at the Housemark Data and Analytics Summit on Thursday last week in Nottingham. Speaking in two sessions 1) on digital transformation and how data strategy and operating models are key to its success, and 2) in a final panel which bought the whole day into a final conclusion, in which we discussed data, AI, culture change etc.
While, the summit was aimed at the housing sector, I believe that many sectors/industries are still missing the key component of their data and AI strategy, and that is the “Operating Model”.
In my simple words “the Operating Model is what makes your strategy real” without it, you are a boat without a rudder and compass.
Making strategy real isn’t easy for many companies, as they confuse strategy for planning and then get all messed up in what they need to do! But you can read another article that I have written “Why Most Data and AI Strategies Aren’t Strategies At All”.
Let’s get back to it for now and yes, of course, we know that in this day and age, data and now AI are key to staying competitive. But, just collecting data for the sake of it, isn’t going to give you any form of differentiation. You must truly know the purpose of why you are collecting it and have a vison that matches.
The true differentiator lies in a well architected operating model that integrates data, analytics, and AI into every layer of the organisation. These enablers need to be infused into the organisation and with the right structure, your strategy stops being an isolated initiative and becomes a core driver of business value. That’s where we want to get to.
Here’s what it takes to build a world-class operating model that doesn’t just talk about value but delivers it, and the clear benefits you’ll see as a result.
- 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 it be in the form of raw data or 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 organisation design hasn’t been set-up to incorporate a level of 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 organisation design that is set-up for masterful collaboration, breaking down the deep silos and embedding partnering and co-creativity into the mix.
As well as this, 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 existing culture of the organisation. The culture is already established, there are norms, there are structures in place, there are rituals and rites, there are power structures and there are stories. Many organisations have become too dependent on a bunch of trainers going around attempting to teach people about SQL or how to build a dashboard or data model. Becoming data fluent goes beyond technical skills, which let’s face it, what most data literacy training programs 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, people 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, its 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 a financial organisation I set-up a virtual data squad, made up from several parts of the data team and implemented a demand management function that helped streamline 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 isn’t easy for HR professionals, can you then imagine how difficult it is for data teams who don’t have the foggiest about this stuff.
Organisation design is just one factor, as well as flexibility which is essential for responsiveness in organisations and the competitive environment, they find themselves in. Organisations that adopt flexible, agile approaches can compete far quicker and are able to seize opportunities faster, responding more effectively to shifts in the market.
- 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 the credibility in the data team, which demonstrates 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:
- Increased Agility and Responsiveness: With an adaptable structure, teams can respond to market changes quickly, make decisions quickly, and capitalise 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.
- 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.
- 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 predict demand for specific products more accurately. This allowed managers to make informed purchasing decisions, leading to a 20% reduction in stockouts and excess inventory costs.
- Improved Customer Experiences: A value focused and outcomes approach allows 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 create personalised financial advice for clients. By tailoring services to individual needs, they boosted customer satisfaction by 40% and improved retention rates, especially among high-value clients.
- 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, enabling 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.
- Stronger Competitive Positioning: Organisations that leverage data and AI as a core strategic asset gain a clear competitive edge, as they can optimise operations, reduce risks, and anticipate 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 a core component of how the organisation functions and competes. To truly make them impactful, they must be embedded within 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.
I’m Samir, a data strategy and operating model specialist with a focus on turning data into real business value. If you’re navigating the challenges of aligning data with your business goals, or just want to talk about data and AI strategy, connect with me on LinkedIn.