Tracing data evolution: A Reflection on the role of context

Recently, I found myself reflecting deeply on how data and information evolve over time. It started with a simple thought experiment. Imagine we have a data object for example a person, described only by their name and age. At first, this seems straightforward. But what happens when we add context? For example, if I introduce a parameter like "death", the meaning shifts dramatically depending on whether the person was 6 or 60 years old. The same data takes on a whole new significance. And if I replace "death" with something like "experience", the interpretation changes yet again. Age now becomes a marker of growth or wisdom. This led me to think about how much the context we place around data shapes its meaning, and what that says about the evolution of information itself.

In this article, I want to share my reflections on how data changes as it interacts with different contexts, and what that means for the way we understand, design, and analyze information.

The Misconception of Data Objects as "Trivial"

Before entering the subject of data evolution let's clarify something important!

Data objects like "employee," "invoice," or "transaction" are frequently treated as if they are simple, straightforward entities. It's easy to fall into the trap of thinking that data objects are just collections of attributes, such as names, dates, and values without deeper meaning.

For example, when a developer encounters a data object called "person" with attributes like name, age, and salary, they might initially view it as a mere structure for storing information. However, for an HR professional, this same object represents a human being’s employment lifecycle, career trajectory, and value to the organization. The meaning extends far beyond the raw data.

The issue arises because people, especially those who work closely with data, like developers are accustomed to handling these objects as technical constructs rather than entities loaded with business significance. This often leads to a lack of communication about the context in which this data is used, interpreted, and valued by the business.

The Expectation for Developers to Understand Meaning

It's common in organizations for business teams (like HR, Finance, or Marketing) to ask developers to challenge assumptions or suggest new ways to extract value from data. The underlying expectation is that, since developers interact so frequently with data, they should be able to spot patterns, generate insights, and make suggestions that improve business processes.

But there's a flaw in this assumption: Why should developers know the deeper meaning behind data objects when they're not domain experts?

Take, for example, an HR data object that contains employee tenure, performance reviews, and training records. A developer might be able to manage this data, store it in a database, or build a system to process it, but understanding how it should be interpreted, how sensitive it is, or how it ties into HR strategy is not their area of expertise. This creates a mismatch between what business stakeholders expect (insights and suggestions) and what developers are equipped to offer (technical solutions).

The Importance of Collaboration Between Developers and Business Experts

This disconnect highlights the importance of close collaboration between developers and business experts. A developer’s primary role is to manage the technical implementation of systems, not to be an expert in HR, Finance, or any other domain. They can handle data efficiently, but without the business context, they can't provide meaningful insights.

On the flip side, business stakeholders often don't realize that their domain-specific knowledge is needed to guide developers in understanding why certain data is important, what insights could be valuable, and how those insights could impact business decisions.

3 reasons why developers shouldn't be expected to know the meaning behind data objects:

1. Domain Expertise: Developers are experts in building systems and managing data flows, not in understanding the nuances of HR or Finance. Expecting them to know how to interpret data from domains they are not trained in is unreasonable.
2. Contextual Knowledge: Without the business context, developers may not understand why certain data points are important or how they are used in decision-making. For example, a field like "performance rating" may mean one thing in HR but could be interpreted entirely differently in another domain like customer service.
3. Business Impact: The meaning of data is tied to business outcomes, and developers typically lack insight into how data-driven decisions affect the broader strategy of the business. For example, understanding why employee turnover data is critical for workforce planning requires HR expertise, not just technical knowledge.

4 ways for developers and business professionals to bridge the gap:

This disconnect can be resolved with better communication and shared understanding between developers and business stakeholders. Here's how that collaboration can work effectively:

1. Business Leaders Should Share Context: Business stakeholders (like HR managers or Finance directors) should take time to explain the context behind data objects. For example, an HR manager could explain why training completion rates are a key indicator of an employee’s future performance, rather than just expecting developers to guess.
2. Developers Should Ask Questions: Developers, for their part, can ask clarifying questions to understand what business needs the data fulfills. Rather than just implementing technical requirements, they could inquire: "Why is this data important?" or "What decisions will this information drive?"
3. Co-create Specifications: Instead of assuming that the data is understood universally, business professionals and developers can work together to create more meaningful data specifications. This ensures that both parties know the purpose and importance of the data being collected, stored, or analyzed.
4. Iterate and Learn Together: A dynamic feedback loop is essential. As developers build systems and work with data, they should receive feedback from business experts on how the data is performing in real-world scenarios. This ongoing collaboration can ensure that data is not only technically correct but also business-relevant.

Data is Not "Just Data"

In short, the expectation that developers should understand the meaning behind data objects without the necessary business context is misguided. Data is not trivial, it holds different meanings depending on the domain and the context in which it's used. Developers can manage the technical side of data, but understanding its business significance requires collaboration with domain experts.

By fostering better communication and a deeper understanding between developers and business professionals, organizations can ensure that their data-driven initiatives are meaningful, accurate, and valuable.

Data only becomes truly powerful when its meaning is properly understood by all parties involved.

How context shapes meaning?

It is only when placed in context that data transforms into information and, eventually, knowledge. In reflecting on this idea, I realized how powerful context is in shaping the meaning of raw facts. The same data can tell vastly different stories, depending on the lens through which it is analyzed.

Data as Raw Facts

At its most fundamental level, data is a collection of raw facts. Consider a simple data object, a person with a name and an age.

For example, take a person named John who is either 6 or 60 years old. These facts, in isolation, are neutral. They provide no deeper meaning beyond the numbers and text. Without context, we cannot draw any conclusions about John’s life, experiences, or circumstances. He is merely a data point in a sea of facts.

Context adds meaning

Context is what breathes life into data, transforming it into information that can be interpreted and understood. When we introduce a contextual parameter, such as "death" , the raw data starts to take on significance. If we know that John died, the interpretation of his age, whether 6 or 60, changes drastically.

If John was only 6 years old, his death may be seen as a tragic event, a life cut short. The number 6, when combined with the concept of death, conveys the story of lost potential and sorrow. On the other hand, if John was 60 years old, the same concept of death may be viewed through the lens of a more natural life cycle, possibly evoking a sense of closure or completion rather than tragedy. The same numerical data (age) now holds vastly different meanings, entirely dependent on the contextual frame surrounding it.

Language and context shape perception

The influence of context goes even deeper when we consider how language itself can reshape the meaning of data. Let’s change the context parameter from "death" to "experience". Suddenly, the data shifts focus. If John is 6 years old, we might infer that he has relatively little life experience. However, if John is 60, we could assume he has a wealth of experience gained over decades.

Here, the exact same data, John’s age produces entirely different interpretations. The number 6, once associated with the tragedy of premature death, now signals the innocence and early stages of life. The number 60, which might have represented the end of life in one context, becomes a marker of accumulated wisdom in another. This demonstrates how even subtle changes in context or language dramatically shift our understanding of the same raw data.

From data to information to knowledge

This evolution of meaning reflects a broader principle: data only becomes meaningful when placed in a specific context, which in turn shapes how we interpret it. Data alone, John’s age is merely a fact. But once we add context, such as death or experience, the data transforms into information, which we can now analyze and understand. From there, this information can evolve into knowledge, helping us draw broader conclusions and insights.

If the context is death, the information we derive from John’s age speaks to mortality, the human life span, and our understanding of life’s fragility. If the context is experience, the information speaks to the accumulation of wisdom, time lived, and perhaps the value we place on certain stages of life. In both cases, we begin with the same raw data, but depending on the context, we extract entirely different forms of knowledge.

The reflection on how data evolves when placed in context illustrates a simple yet profound truth: meaning is not inherent in the numbers or facts themselves but is shaped by the contextual lens through which we view them.

The interplay of data, context, and language allows us to transform neutral information into meaningful knowledge, depending on how we choose to interpret it.

AI the driver of strategic decisions

One critical point about AI and data systems is that they are often narrowly focused on optimizing individual processes, while missing the broader implications that span across an organization or, in extreme cases like a pandemic, entire societal systems.

The DIKW framework in practice

The DIKW Framework (Data, Information, Knowledge, Wisdom) is often discussed but rarely implemented in a holistic manner. The framework suggests a progression:

- Data: Raw facts and figures.

- Information: Organized data that provides meaning.

- Knowledge: Insights and understanding derived from information.

- Wisdom: The ability to make informed decisions based on knowledge.

In theory, this framework helps organizations evolve from managing simple data to making wise, strategic decisions that align with long-term goals. But as I observed, in practice, many implementations of this framework stop at the "I" stage, focused only on gathering and presenting information, rather than evolving toward actionable knowledge or wisdom.

Process AI: Focused on the "I," but what about "K" and "W"?

Take Camunda and other AI-enhanced process management platforms, for example. They use AI to analyze data from processes in real time, helping users identify issues like bottlenecks or inefficiencies. This is extremely useful for process optimization but is still largely stuck at the information level of the DIKW framework. It provides insights on what is happening but doesn't evolve into guiding actions that address why these events occur or how they could impact the broader organizational goals.

The gap I’ve identified is that these systems tend to be reactive rather than proactive. For instance, in the case of hospitals during a pandemic, AI might flag immediate bottlenecks (such as ICU capacity), but it lacks the broader organizational wisdom to predict systemic risks, such as resource shortages that can cripple an entire healthcare network.

The missed opportunity: Integrating AI for knowledge and wisdom

This is where my reflection leads to a compelling idea. AI should be designed not only to optimize processes in real time but also to help organizations anticipate future challenges and align processes with mission-critical objectives. Here's how AI can be extended further into the Knowledge and Wisdom stages:

1. AI and Knowledge: Moving beyond analyzing real-time process metrics, AI should be able to take in historical data, external factors (like pandemics), and organizational goals to predict how today's process issues could impact tomorrow's strategy. For example, an AI system could integrate hospital resource data with public health forecasts to anticipate future patient overflow situations.
2. AI and Wisdom: At the highest level, AI could guide strategic decisions by providing a holistic view of the organization, making sure that every process is aligned with the organization’s mission and risk management goals. In the case of healthcare, this would mean not only predicting that a hospital might run out of ICU beds but also suggesting alternative courses of action that the government or healthcare leaders can take to prevent a crisis.

The Hospital Example: A Missed Strategic Opportunity

The example of hospitals during the pandemic perfectly illustrates the limitations of current AI implementations. Many hospitals had advanced systems to monitor their operations and processes, but they weren't equipped with AI that could give early warnings about system-wide risks. Imagine if hospital AI could have forecasted a shortage of ICU capacity weeks in advance, providing data to government officials who could have made more informed decisions, like redirecting resources, scaling infrastructure, or implementing stricter public health measures earlier on.

Instead, the AI systems in place were mostly limited to telling healthcare managers about immediate process inefficiencies. While that’s useful, it doesn’t address the broader systemic issues that hospitals faced during the pandemic. The wisdom part of the DIKW framework, understanding long-term risks and making decisions that align with the mission of the organization was missing.

AI in Risk Management and Governance

As pointed out earlier, AI could play a transformative role in Enterprise Risk Management (ERM). AI systems could continuously update risk models based on both internal process data and external information, such as market trends, regulatory changes, or public health data. This way, when you design a new process in an organization, the AI would not only suggest ways to optimize it but also highlight potential risks and implications across the organization.

For example, a hospital that is updating its pandemic response protocols could use AI to assess not just operational efficiency but also the system-wide impacts of different strategies. How different resource allocation plans might affect patient care, staff wellbeing, and overall hospital capacity. The AI could alert top management to potential risks before they escalate, allowing leaders to make informed, strategic decisions that align with the hospital's mission of providing continuous care.

AI as a Tool for Strategic Wisdom

In conclusion, the role of AI in the DIKW Framework needs to evolve beyond its current focus on data and information. To be truly effective, especially in critical environments like healthcare or finance, AI systems must move into the realm of knowledge and wisdom, helping organizations anticipate risks, align processes with long-term goals, and ultimately make strategic decisions that account for both immediate performance and future impact.

In a follow-up discussion, it would be interesting to explore how AI could be designed to better support process governance and risk management by proactively identifying risks and ensuring alignment with organizational missions in industries like healthcare, finance, and beyond. This could radically transform how organizations not only optimize processes but also navigate complex, unpredictable environments.

In the world of business process management (BPM), where workflows and decisions heavily depend on well-defined data, understanding how meaning evolves is critical.

Conclusion

In this article, I've explored how context shapes the evolution of data, transforming raw facts into meaningful information that drives business decisions. By examining the disconnect between developers and business experts, the importance of collaboration in order to ensure data is used effectively. As we move forward in this series, I’ll take you inside my thought process, sharing how I plan to build adaptive data systems from scratch, applying these insights to create processes that are not only efficient but aligned with strategic goals.

I invite you to join me on this journey! Engage with the content, share your thoughts in the comments, or feel free to private message me. Together, we can dive deeper into the fascinating world of data evolution, AI, and process optimization. Explore how to turn data into a powerful tool for business growth. Stay tuned for the next chapter, where we’ll begin transforming these concepts into actionable, real-world solutions.

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