Imagine a Chief Data Officer (CDO) preparing for an executive committee meeting. She has led her team through an intense period of building a state-of-the-art data hub, migrating all data to cloud warehouses, meticulously cataloguing data, and establishing data governance committees. When she asks her team for highlights to present at the meeting, all the responses she receives are deeply technical, seemingly disconnected from the business outcomes.
This scenario reflects a familiar tension in the world of data and analytics today. The role of the CDO, present in just 12% of organisations in 2012, is now a critical position held in 82% of firms according to the NewVantage Partners Data and Analytics Leadership Annual Survey 2023. This rapid adoption of the CDO role mirrors the explosive growth and complexity of data management – from real-time processing and cloud storage to governance frameworks and data cataloguing.
This blog post delves into the tension between the increasingly complex technical aspects of data management and the need for clear business value generation, which is the ultimate purpose of any data programme.
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.
Our imagined CDO in the introduction, in her effort to navigate the intricate landscape of data management, is grappling with a growing gap between the technical execution and business outcomes. On the one hand, the CDO must manage an increasingly intricate data ecosystem. On the other hand, she must translate the value of these technical undertakings to the rest of the organisation.
What’s important to remember here is that data, at its core, is a tool designed to aid business decisions. If the connection between the technicalities and the business value gets lost, the tool loses its purpose. This is the tension that needs addressing – aligning the technical intricacies of data management with clear, tangible business value.
From intent to execution
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.
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 explore a few examples:
- A machine learning model predicts customer churn, empowering the sales team with proactive strategies, and hence, safeguarding revenue.
- Supply chain analytics uncovers bottlenecks, leading to streamlined operations and cost savings.
- Enhanced risk management analytics diminishes potential losses, improving overall compliance.
These instances provide tangible value, whether it is increasing revenue, decreasing costs, or reducing risk. But business use cases extend beyond these primary categories. They also encompass necessary compliance reports, and support key performance indicators (KPIs) aligned with broader company objectives, like promoting diversity or sustainability.
The role of business use cases is to bridge the gap between the intricate, technical aspects of a data programme and the practical needs of business. Identifying them and understanding their value is a crucial first step towards aligning your data programme with the business value it needs to deliver.
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, and an openness to change. 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’s 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’s 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.
Initially published in Towards Data Science in July 2023