Why an age-old approach to handling information is stifling collaboration, innovation, and evolution
The human mind is naturally inclined to decipher and manage the intricacies of one’s surrounding environment and context. Breaking down complex ideas, tasks, or data into smaller, more manageable parts is a critical cognitive skill. It’s how we thrived in the industrial age.
Operating with a strategic mindset, this method is essential for efficient assimilation and storage of knowledge. People build their whole careers around their ability to find patterns in complexity. We have all built cognitive “shortcuts” (biases) that allow us to be successful.
However, our cognitive constraints limit this ability, as exemplified by the concept of the “Magical Number Seven, Plus or Minus Two.” According to a theory proposed by psychologist George A. Miller in his influential 1956 work, it is suggested that the number of items an average person can hold in their working memory is around 7 ± 2. As a result, most individuals can only hold a limited number of data “chunks” in their short-term memory at the same time.
We’re basically leading our businesses and making decisions based on the way the world was in the 1890’s based on a theory discovered in 1956.
Understanding cognitive constraints and data segmentation
With a limited capacity to juggle only 5 to 9 elements at once, there are significant implications for how individuals acquire knowledge, make decisions, and interpret their environment. We all use data segmentation as a strategy to simplify cognitive processing and retention. It involves segmenting information into categorical “chunks” to navigate constraints. When memorizing a long numerical sequence, people often break it down into smaller groups, just like how we remember phone numbers by segmenting them. This makes it easier to recall, rather than trying to remember it as one long string of numbers.
AI and computer systems and data manipulation
AI and computers obviously have entirely different ways of manipulating data. Today’s machine and digital infrastructures are designed to handle massive amounts of data simultaneously without being limited by human cognitive abilities. With the help of algorithms, databases, and computational units, these systems can perform complex calculations, manage large datasets, and carry out multiple tasks simultaneously. They can effortlessly handle vast amounts of data that would seem infinite to human capabilities.
The Importance of Organizing Data
The effective handling and utilization of data hold immense significance. It’s crucial to stay organized and make the most of the information at hand. This process is designed to make data accessible for humans to understand and use. It takes into account the limitations of human cognitive processing, as described by Miller’s theory.
Based on our limited capacity (ok, everyone is brilliant, but let’s be clear, we can’t handle the same amount of information as our computerized team members). We have superimposed our limitations onto the digital realm. The organization and manipulation of data have led to the rise of data silos, along with techniques to break down data into smaller, more usable parts. So we can “make sense” of things as humans. On the other hand, computers don’t need that kind of help.
Data segmentation is everywhere. It’s a common practice in various industries to enhance the effectiveness of information processing, comprehension, and recall for both humans and computers. It involves breaking down complex information into manageable units or “chunks” using a cognitive approach.
Here are some examples:
- Education is chunked: Organizing pedagogical material in educational technology often involves breaking it down into smaller modules, lessons, and subjects. This approach helps students gradually grasp complex topics. Implementing this teaching approach acknowledges students’ cognitive constraints, making education more practical and effective.
- Digital Interfaces are chunked: Information in the digital realm is organized into menus, classifications, and tabs, allowing users to navigate and understand intricate systems without feeling overwhelmed. This principle is evident in a wide range of applications, spanning from mobile software to complex enterprise systems, where prioritizing user-friendliness is crucial.
- Strategies are chunked: Data analysts skillfully break down enormous datasets into smaller, thematic segments to uncover trends and patterns. Take a large dataset on climate change and break it down into smaller subsets like temperature fluctuations, rising ocean levels, and carbon emissions. This will make the analysis more manageable.
- Work is chunked: We break down complex tasks into smaller, more manageable parts or stages, allowing individuals or teams to focus on specific aspects of a project, thereby improving efficiency, focus, and manageability. For example, in software development, a large project might be chunked into phases such as planning, design, coding, testing, and deployment, with each phase further divided into smaller tasks assigned to different team members or groups, enabling parallel processing and more focused expertise application. (If you’re into history, the org chart was also created in the late 1800’s.)
Rethinking the “Magic Number”
When we apply the principle of data segmentation, organizational data structuring and accessibility take on a whole new meaning. Initially rooted in cognitive psychology to enhance human memory and learning, this concept was the go-to approach for leaders to optimize their operations and manage budgets.
However, segmenting data sources without a comprehensive strategy for cross-functional accessibility or integration can have negative consequences for stakeholders and the organization as a whole. This unintended consequence is closely linked to the origin of data silos but also affects a range of other obstacles in data management and usefulness. The consequences of haphazard data segmentation are significant:
- Barriers to Collaboration: Data segmentation creates barriers to collaboration and knowledge exchange, limiting the potential for innovation that comes from diverse perspectives and interdisciplinary engagement.
- Inefficiencies and Organizational Blockers: Efficiently managing data across various platforms is crucial for maximizing resources and ensuring easy access to comprehensive information. This promotes timely decision-making and enhances productivity within organizational frameworks.
- Risky Data Integrity and Poor Decision-Making: The reality of data reliability and the ability to make informed decisions have become increasingly pronounced. Locking away data creates more errors and makes managing it more difficult. It is also harder to make informed decisions because there isn’t enough complete data, and immediate insights are missing.
To overcome these challenges, it is crucial to adopt a strategic approach to data stewardship that focuses on integration, accessibility, and fostering a collaborative mindset. Implementing data integration tools, fostering a transparent culture, and adopting a comprehensive data strategy are essential for success. With customer-centric approaches, organizations can transcend the challenges of data segmentation and enhance their data management protocols to improve operational efficiency and decision-making abilities.
The organizational structures of yesterday, aren’t supportive of the customer-centric realities of today.
Implications to Human-Computer Synergy
The difference between human cognitive thresholds and computers’ computational power has important implications for technological design, user interfaces, and information presentation. Designers and developers must recognize and adapt to human cognition’s limitations to create effective and user-friendly digital systems. It is essential to create intuitive user interfaces, avoid overwhelming users with excessive data, and present information in ways that align with optimal human processing methodologies (such as visual representations and data chunking).
In addition, this understanding highlights the effectiveness of computers as tools to enhance human abilities and help us be even more productive. By offloading intricate calculations, data storage, and analytical tasks to computers, individuals can focus on activities that require creativity, empathy, and strategic thinking — areas where human cognition excels. Computers become potent allies and work partners, enhancing our ability to understand and interact with the world around us.
Imagine a world where we organize information into manageable “chunks” in order to overcome our limitations using the cognitive strategy of data segmentation. And where computers process and store data quantities that far surpass our limitations. When we lean into our “human-ness,” we recognize and customize tools, methods, and approaches that harness the differences to create technology that enhances human decision-making and understanding, thus closing the gap between human cognitive limitations and the limitless potential of AI-enhanced systems.
