Eyes on the Prize: Keeping Business Value at the Core of Data Programmes
Maximise the business value of your data investments
In my role as a software executive, I frequently engage with Chief Data Officers (CDOs) across industries. Some are inherently technical, armed with deep knowledge of data architectures and AI algorithms. Others lean more towards the business side, possessing a sharp understanding of how data can unlock business value.
A few weeks ago, I was having a friendly conversation with Alex, a CDO at a financial institution, whom I consider more on the business side.
Alex explained to me that not long ago, while preparing for an executive committee meeting, she asked her management team to provide input on their recent accomplishments. However, all she heard back were technical achievements mostly linked to the recent cloud migration of their data infrastructure. She knew that none of this would resonate with her business stakeholders, so she had to construct a whole new narrative by herself, which she managed to do. This was when she realized that she was mostly alone in selling the business value of the data program to business executives and that the stories they would tell were not how the people in her team perceived their job at that point in time.
This is a conversation that I keep having with CDOs, from the most technical to the more business-oriented. They all feel this increasing tension between the technical complexities of data management and the practical business value that these initiatives are supposed to deliver. The role of the CDO, once a rarity found in only 12% of organizations in 2012, is now firmly established in 82% of firms, according to the NewVantage Partners Data and Analytics Leadership Annual Survey 2023. The sheer complexity of data management has grown in parallel, making this chasm even more daunting to cross.
The following sections will delve deeper into this issue: the tension between the sophisticated technical aspects of data management and the pressing need for clear business value generation.
Firstly, we explore the idea of a business value first approach, wherein the contribution to business goals becomes the primary measure of a data programme’s effectiveness.
We then introduce the concept of business use cases — the direct business-facing deliverables of a data programme, which are the true drivers of business value.
Next, we delve into the crucial role of business stakeholders in the continuous, collaborative process of business value measurement, a critical factor in aligning data initiatives with business goals.
We then examine the profound implications this approach has on the organisation and its people, fostering an environment where everyone is oriented towards the same goal: business value creation.
Finally, we outline the process of transforming into a business value-driven data organisation, discussing the strategic and tactical shifts required, the challenges to expect, and the steps needed to ensure that data programmes become more than just complex technical projects.
Understanding the Tension: The Complexity of Technical Execution vs Business Value
Data programmes are multifaceted, demanding a high degree of technical proficiency to execute. This complexity extends from developing intricate data pipelines, setting up sophisticated cloud storage systems, implementing strict governance frameworks, to cataloguing vast amounts of data. Undoubtedly, these tasks are significant accomplishments, marking crucial steps in the journey of a data programme.
However, the complexity often brings with it a sort of tunnel vision. The deeper data teams dive into the technical execution, the more they risk losing sight of why the programme exists in the first place — to generate business value. While a data programme’s technical accomplishments are crucial, they are means to an end, not the end itself.
What’s important to remember here is that data, at its core, is a byproduct of operational processes, not necessarily well designed to aid business decisions. This is why we need complex data programmes to make data usable for business decisions. But if the connection between the technicalities and the business value gets lost, the whole programme loses its purpose. This is the tension that needs addressing — aligning the technical intricacies of data management with clear, tangible business value.
Business Value First: Focusing on the Ultimate Goal
While it is natural to be absorbed in the technical details of data programme execution due to their inherent complexity, we have identified that these tasks are but means to an end. The complexity and the focus on technical tasks have inadvertently obscured the ultimate goal — driving business value. But how do we resolve this tension between technicality and business value? By putting business value first.
But what does that actually mean? It means shifting from a data-first perspective to a business-first perspective. It’s about looking beyond the jargon, algorithms, and infrastructural achievements to answer one fundamental question: “What is the impact of our data initiatives on the business?” This calls for aligning each technical task to a corresponding business goal and continually evaluating its effectiveness in terms of business outcome generation.
The truth is, irrespective of the complexity of data pipelines or the number of data sources integrated, if a data programme does not contribute positively to your company’s bottom line or other key performance indicators (KPIs), it is falling short of its primary purpose. That’s why taking a business value first approach is not just a strategic decision, but an essential shift in how data programmes should be assessed and executed.
This shift in focus does not negate the importance of technical tasks. Rather, it ensures these tasks are directly aligned to the central goal: generating tangible business value. This approach ensures that each decision made within a data programme, no matter how technical, is oriented towards driving meaningful business outcomes. In other words, it places the business context at the heart of data and analytics operations.
Introducing Business Use Cases: The Deliverables That Count
In any data programme, we find a variety of deliverables, some directly contributing to business value and others indirectly supporting this goal. The deliverables directly associated with business value, are the ones we define as business use cases.
Let’s consider a few real-life examples drawn from my experience interacting with our customers. You’ll notice that these business use cases are described in a ‘business value first’ manner — in the way that a CDO would present the accomplishment to the business:
Energy Efficiency Improvement: One large pan-European industrial company we work with implemented an analytics solution to decrease energy costs. This move captured the business’s attention as it led to savings close to a million euros and reduced CO2 emissions by 1000 tons per year. The implementation involved deploying sensors to measure energy consumption, creating simulation models, and visualising the highest contributors to understand energy flow.
Real-Time Defect Detection: A major UK car manufacturer we serve implemented a roaming Process Failure Sheet (PFS) to detect and address defects in real time during the production process. The system moved along with the assembly line, covering its entirety and enabling the efficient identification of production anomalies. Previously, their defect reporting process was inefficient, time-consuming, and error-prone. The implementation of the roaming PFS significantly reduced checking time and the number of technicians required per shift. As a result, the company saved £1.75 per vehicle, adding up to over £400,000 over the year — a clear illustration of the business value of this data initiative.
Business use cases provide tangible value, whether through increasing revenue, decreasing costs, or reducing risk. But the scope of business use cases extends beyond these primary categories. They also encompass necessary compliance reports, and they support key performance indicators (KPIs) aligned with broader company objectives, such as promoting diversity or sustainability.
Since business use cases generate the entirety of a data programme’s value, it is crucial for CDOs to ensure their value is well understood and fully endorsed by the business.
Measuring Business Value: A Continuous, Collaborative Process
The value of business use cases is not static, it evolves along with the market dynamics, shifting regulatory landscapes, advancements in technology, and a multitude of other external factors.
Let’s illustrate this with an example from recent history — the COVID-19 pandemic. At its height, data teams developed business use cases around workplace occupancy rates, air quality indexes, and other pandemic-related data. These use cases were of high value then but as we move into a post-pandemic era, the value of these specific use cases have diminished, making way for new priorities. While the capacity to respond swiftly to similar events still holds value, it is not as immediately or quantifiably significant as during the peak of the crisis.
Importantly, the onus of assessing the value of these business use cases lies squarely on the shoulders of the business stakeholders, not the data team. Business stakeholders are the ones who experience the operational impact of these use cases day in and day out. Their insights are crucial to gauging value and distinguishing between transient benefits, like a one-time cost reduction, and structural ones, such as a long-term increase in operational efficiency.
As such, the process of measuring value should be ongoing, capturing shifts in perception at different points in time. This ongoing assessment not only serves as a tool to fine-tune the execution of the data programme but also ensures it remains aligned with evolving business needs.
However, this exercise isn’t complete without taking into account the cost of implementing business use cases. Striking a balance between the perceived value and the associated cost becomes crucial. Add to this the complexity introduced by the potential overlap in benefits between business use cases, or double-counting, and it’s clear the valuation process is far from simple.
To navigate this, it’s imperative to establish a continuous dialogue and feedback loop with business stakeholders. This open communication allows the data programme to continuously realign with the business’s realities and priorities, thereby ensuring its relevance and its consistent contribution to the bottom line. Through this constant engagement, we can cultivate a data programme that is technically robust and acutely attuned to the needs of the business.
Transforming into a Business Value-Driven Data Organisation
Transitioning to a business value-driven data organisation requires more than an adaptation in strategy, it necessitates a deep transformation permeating all levels of your data programme. This is not just about the CDO presenting business-oriented reports, it is about making business value the guiding principle of all activities within the data programme.
In many organisations, data-related tasks can feel detached from the realities of the business. But this approach can result in a disconnect between the data programme and the business needs it is supposed to serve.
The transformation to a business value-driven organisation involves questioning whether any significant work performed with data contributes to creating business value. This change influences how tasks are prioritised, how success is measured, and how resources are allocated. It helps tie every team member’s work to the overall business objectives.
This transformation doesn’t happen overnight and certainly comes with challenges. It requires committed leadership, a clear vision, an openness to change, and the right tools to support the transition. But the rewards can be substantial: a more efficient organisation, better alignment with the business, and a stronger contribution to the business’s bottom line.
However, to effectively carry out this transformation, there needs to be a tactical and strategic plan of action. In the following section, we outline the crucial steps to making this shift from data tasks to delivering actual business value.
Making it Happen: From Data Tasks to Business Value Delivery
Starting from the principle of transformation discussed above, shifting a data organisation’s focus from purely technical tasks to creating tangible business value is indeed not a trivial endeavour. It not only demands a change in perspective but a complete realignment of priorities and a deep understanding of the business and its needs.
First and foremost, it is crucial to develop a culture of collaboration that spans across the data organisation and the business units. The business must play a leading role in defining and measuring the value of business use cases. On the other hand, the data team needs to gain a deep understanding of these use cases, their value drivers, and the data requirements that enable them.
Importantly, this collaboration must also extend between the people consuming data for delivering business use cases (the business-facing part of the organisation) and the people providing packaged data for consumption.
The alignment should then be propagated throughout the data programme.
Every task, every pipeline, every model needs to be clearly associated with the business use cases they serve. This is not merely about having a traceability matrix for reporting purposes. It’s about making the value generation visible and tangible at every level, thereby fostering a sense of purpose and engagement among all stakeholders, from the most technical data engineers to the business users of the insights.
To drive this transformation, it is essential to establish measurable goals that reflect the new focus on business value. This includes setting targets for business value creation, time to value, and overall alignment of tasks with business goals. The process of assessing business value itself should also be refined and improved over time, taking into account the evolving business context and lessons learned.
This transformation is an ongoing commitment, demanding constant attention and adjustments as business needs, data availability, and technological capabilities evolve. Rather than a one-off project, it’s a continuous journey of improving alignment, collaboration, and value delivery.
In sum, the process of aligning a data organisation with business value creation requires deliberate action and continuous effort. But the rewards — a data team that is deeply engaged with the business, initiatives that are prioritised based on their impact on business goals, and an organisation that harnesses the power of data to drive real value — make this journey well worth undertaking.
