In every company’s journey toward becoming a data-driven organization, we must remember that true transformation extends beyond isolated changes. It requires a holistic approach, where every change aligns with the company’s broader vision. It is a commonly overlooked fact, that changing one part within a system, changes the whole system. This is especially important if your system is complex and opaque, like a company that has been grown organically over time and consists of people, that by default have very different interpretations and understandings of what role they play within that system and how the whole system works together. This is where “Systems Thinking” and the “Science of Networks” come in handy, and so we will complement our understanding of the principle of “Systems over Objects” by having a short look into those fields.

The principle “Systems Over Objects” is part of a set of 9 principles, initially written down by the co-founder and former director of the MIT Media Lab Joi Ito and adapted and successfully applied by Ulrike Reinhard in her consultancy work as well as in her social change project “Janwaar”. This principle underscores the importance of viewing and implementing data strategies as holistic, integrated, cohesive systems rather than a collection of fragments or siloed efforts. Let’s look deeper at what that could mean for your company and your Data Business & AI Strategy. As Ulrike Reinhard says, “When all of the principles are applied, change does happen”. So let us take some time to really understand them one by one.

The Holistic Vision

Ulrike Reinhard’s experience with transforming the rural village of Janwaar provides a compelling analogy for data transformation. She emphasizes, “The individual measure was always embedded in the vision of making the whole village ‘better’.” Similarly, within the complex network of a company, a data strategy must be embedded within the overarching corporate vision. Each department and function is interconnected, meaning that every technological, organizational, and personnel (TOP) change impacts the entire network and must contribute to the company’s long-term goals.

To yield a data-driven and future-ready company, a holistic and fully integrated data strategy should always be directed at the transformation of the given business model into becoming a “data business model”. It should be designed from the ground up with data & AI as a core and natural component, rather than attaching data & AI to the given model, without changing the system substantially. So this is a good time to have a closer look into the context of systemic thinking.

Defining a System and Understanding Its Importance

A system is defined as a set of interconnected components that work together to achieve a common goal. This definition underscores the idea that a system is more than just the sum of its parts. Each component within a system plays a crucial role, but the true power of a system lies in how these components interact and support each other to create the so-called “synergetic effects”.

In the context of a company, understanding the difference between a system and a collection of parts is essential. When we view a company as a system, we recognize that optimizing individual departments or processes in isolation can lead to suboptimal outcomes. This is because the value created by a system comes from the relationships and interactions between its parts, not just from the parts themselves. Often, also problems and fallacies can arise on those connecting lines when the synergetic nature of a system is not understood.

This broader perspective, where we understand that the whole is greater than the sum of its parts, is what makes strategies and their integration holistic and makes them work. By understanding and leveraging these interdependencies, companies can achieve more impactful and sustainable transformations.

This is why Datentreiber always applies a triad in its holistic method: only when you start to include the whole system do the strategic levers begin to emerge. Focusing on just one aspect will leave you with data issues (business understanding + user understanding – data understanding), business problems (data understanding + user understanding – business understanding), or a lack of acceptance and tailoring to your employees’ or customers’ needs (data understanding + business understanding – user understanding).

Avoiding Silos and Conflicting Objectives

During the transformation process, many decisions and changes have to be applied, and so with a growing number of decisions to be made, the number of pitfalls and uncertainties rises. One common pitfall is the creation of departmental silos. For instance, if the marketing department builds its own data warehouse independently of the sales department, not only are resources wasted, but the opportunity for integrated insights is lost. Even more detrimental is when these silos operate with conflicting objectives—marketing aiming to minimize customer acquisition costs through discounts, while sales seeks to maximize profit margins. This misalignment not only leads to inefficiencies but also hampers the company’s overall effectiveness.

And those relatively simple breaking points are not a rare issue: Departments often develop their own goals based on their specific functions and metrics. For example, the marketing team might focus on increasing customer acquisition through aggressive discount campaigns, believing that more customers will lead to higher sales. Meanwhile, the sales department might prioritize maximizing profit margins, which could mean fewer discounts and higher prices. These conflicting strategies can create a tug-of-war effect, where efforts to achieve one objective undermine the success of another.

Such conflicts are not always immediately apparent, as each department’s goals might seem reasonable and aligned with their mandates. However, when these goals are not coordinated with the broader corporate strategy, they can crash and sabotage the economic success. This misalignment not only stems from a lack of communication and collaboration between departments, as well as from leadership that does not emphasize the importance of integrated goals but also from a lack of understanding of the nature of systems and putting “Objects Over Systems”, in reverse to our principle “Systems Over Objects”.

Implementing Coherent and Consistent Strategies

A well-designed data strategy ensures that all changes are coherent and consistent. This means aligning the objectives across departments and ensuring that their actions support one another. Ulrike Reinhard’s approach highlights the need for a balanced focus on the broader impact of new structures, technologies, products, and processes. She asserts, “At the end of the day, there needs to be a balance of planet, people, and profit. Especially for companies.”

Instead of optimizing isolated objects or departments, it is crucial to improve the entire system and understand the bigger picture. Often, the root of many problems lies in the relationships between individual components—be it tools, process steps, or personnel—rather than the components themselves. By adopting a systemic view, organizations can identify and address these interdependencies, leading to more sustainable and impactful transformations.

Implementing the principle of “Systems Over Objects” requires a nuanced approach to strategy development. While some strategies offer generic actionable steps provided, they cannot fulfill their promises and they yield no competitive advantage. The core of a truly effective data strategy lies not in universally prescribed solutions, but in the unique, tailored strategies that stem from a deep understanding of a specific organization’s needs, competitive environment, and long-term goals. This requires holistic understanding and courage from everyone on board, as it is given that transformations create friction and also can trigger fears and pain points. This is the very nature of every truly transformative process, because the old is challenged, and the new has yet to arise.

Building a Proactive Data & AI Business Strategy with Datentreiber

Train, Think, and Transform: Datentreiber’s Approach

At Datentreiber, we understand that becoming a data-driven organization involves more than just implementing new technologies or collecting data. It requires a comprehensive transformation that touches every part of the company, ensuring that all changes are aligned with the broader corporate vision and that the entire system operates cohesively. Our approach is structured around three core elements: Train, Think, and Transform. Each of these elements is designed to support your company’s journey towards a fully integrated, data-driven business model.

Train

Our training programs are designed to build and enhance data literacy, strategic thinking, systems thinking, and design thinking (user-centric). These programs help develop an agile mindset and a deep understanding of value and business objectives in relation to technological constraints, trends, and existing solutions. Our goal is to equip leaders and employees from every department with pragmatic, solution-oriented thinking and a better intuition for how Data & AI can truly transform your business.

In today’s VUCA (Volatility, Uncertainty, Complexity, and Ambiguity) world, change happens rapidly, uncertainties are higher, and the systems that interact are more complex. Our training programs go beyond mere data literacy; they provide “transformation literacy” essential for thriving in the coming decade. As the future economy increasingly relies on data-driven decision-making, it’s crucial to have a fully integrated business model where complex processes are not only measured but also processed, understood, and embedded.

Our training covers various aspects, including:

1. Data Literacy: Understanding data sources, data quality, and data management principles.
2. Strategic Thinking: Developing long-term strategies that align with the company’s vision and leverage data for competitive advantage.
3. Systems Thinking: Recognizing the interdependencies within the company and the broader market to make informed decisions.
4. Design Thinking: Fostering a user-centric approach to problem-solving and innovation.
5. Agility: Cultivating an agile mindset to quickly adapt to changes and new challenges.

Think

Developing a data and AI business strategy is a critical step towards transformation. This phase involves understanding how to put “Systems Over Objects” into practice and starting a holistic approach to achieve measurable results and deliverables. Our “think” process employs workshops with an interdisciplinary team from your company. We always begin by identifying concrete use cases and providing a clearer picture of your current state, business model, and potential transformation needs. This involves a series of workshops to give you that holistic framework, usually as 2 days onsite and 4 half days online sessions per workshop phase.

Your company must absolve the “think” phase, before it can start into its “transformation” phase, as the “thinking”-phase is your business data & AI strategy development process and it will give your whole staff concrete, actionable guidelines, that build your transformative foundation.

The “think” process typically follows the “train” process but can also run in parallel. It involves continuous strategy development, starting at the outset and evolving as your transformation progresses. Regular re-evaluation is necessary to align with changing KPIs, cultural shifts, technological trends, and new market opportunities. This means, you can and should always come back to the “think” phase, even when your transformation process has already been started.

During the “think” phase, we:

1. Conduct strategic workshops to co-develop objectives with leaders and frontline employees.
2. Analyze the current state of your business to identify strengths, weaknesses, and opportunities.
3. Develop tailored strategies that reflect your unique organizational needs and goals.
4. Ensure ongoing alignment and adaptability as the transformation unfolds.

Transform

The “transform” phase focuses on executing the strategic foundation and managing the complex transformation process. We provide expertise from our network of respected professionals, covering areas such as data architectures, advanced analytics, machine learning, organizational change, and legal considerations. Our experts work closely with you, guided by your developed data and AI business strategy, ensuring that every aspect of the transformation is handled with precision and foresight.

Depending on your Data & AI Strategy that has been developed in the “think” phase, as well as your specific needs, these aspects can be included in the “transform” phase:

1. Unified Data Architecture: Developing a customized data architecture that supports seamless integration and interoperability across departments.
2. Cross-Departmental Collaboration: Implementing effective collaboration strategies that foster interaction and innovation.
3. Dynamic Objective Alignment: Continuously aligning objectives and strategies through regular assessments and adjustments.
4. Cultural Transformation: Promoting a holistic thinking culture with targeted training programs, leadership endorsement, and incentives.
5. Scenario Planning and Data Agility: Using data to anticipate future trends and adapt quickly to new technologies and market conditions.
6. Advanced Analytics: Investing in predictive analytics and machine learning to drive innovation and maintain a competitive edge.

Our track record of successful projects and a strong network of experts ensures that we provide comprehensive support throughout your transformation journey. By focusing on systems over objects, we help you avoid common pitfalls and achieve the results you envision.

What have we learned?

The broader message here underscores that a successful data-driven transformation hinges on creating a cohesive and integrated approach tailored to an organization’s unique context. By focusing on systems over objects, organizations can develop data strategies that are not only effective but also sustainable and aligned with long-term business objectives. This approach allows companies to not just keep pace but stay ahead, turning data into a strategic asset that drives growth and innovation.

Start to apply this principle and start to see your company as a connected, synergic network, rather than isolated, solid islands, where your employees work in disconnection.

As we get on this transformative journey, let’s keep the big picture in mind. Each change, whether technological, organizational, or cultural, should contribute to a unified vision of success. By adopting the principle of “Systems Over Objects,” we can navigate the complexities of data-driven transformation and emerge as stronger, more resilient organizations.

Stay tuned as we continue to explore more principles that guide our data strategy and transformation efforts. At Datentreiber, we are committed to helping you unlock the full potential of your data by embracing a holistic, integrated approach.