From Information Overload to Insightful Action. Why the Knowledge Age Demands More Than Data—and How Organizations Can Adapt

Written By Finlay McNeill

“We are drowning in information, while starving for wisdom.” 
—E.O. Wilson 

For decades, business leaders have invested in tools to capture, organize, and analyze more and more data. We've built data warehouses, data lakes (and, yes, data lakehouses), dashboards, KPIs, and AI systems in the hope that more information will unlock smarter decisions. 

And yet, in the face of complexity, volatility, and accelerating technological change, many organizations still find themselves paralyzed – overwhelmed by inputs, but short on real insight. 

The problem isn’t that we don’t have enough information. It’s that we haven’t built the capacity to make sense of it wisely. 

Traditional models like Cognitive Load Theory have helped explain how excessive information can overwhelm working memory. However, recent research challenges the idea that cognitive load is strictly a bottleneck. For instance, Van Ede and Nobre (2023) argue that working memory isn't a passive storage system but a dynamic process that selectively guides behavior through attentional prioritization. This reconceptualization suggests that what matters is not just how much information we process, but how flexibly we can align memory and attention with goals in real time.  

In other words, it’s not only that cognitive load creates decision gridlock but that working memory also shapes how we decide what matters as we’re working through information. And, unfortunately, how we behave.  

From Information Age to Knowledge Age 

To understand where we’re headed, it helps to understand where we’ve been. Our recent history can be broken into three major phases: 

  1. The Information Age 
    Marked by the advent of personal computing, the internet, and networked machines. The explosion of content and connectivity made information cheap, accessible, and ubiquitous. 

  1. Peak Information Management 
    Organizations implemented ERP systems, HRIS platforms, and enterprise knowledge management strategies. These often created siloed repositories with limited interoperability and emphasized codification over human sensemaking. 
    (See: Alavi & Leidner, 2001. Knowledge Management and Knowledge Systems: Conceptual Foundations and Research Issues) 

  1. The Emergence of the Knowledge Age 
    With cloud computing, data lakes, and machine learning, organizations are now applying algorithms to optimize and automate routinizable tasks. But this moment is less about raw data, and more about synthesizing insights, applying judgment, and learning in context. 
    (See: Davenport & Prusak, 1998; Nonaka & Takeuchi, 1995) 

In short, we are entering an era where organizational advantage lies not in accumulating information, but in cultivating wisdom. 

The Limits of Data-Driven Thinking 

Big data has taught us that more isn't always better. Decision-making quality depends not just on the volume of data but on: 

  • Interpretive skill 

  • Contextual awareness 

  • Timely action 

  • Ethical judgment 

McKinsey’s research on analytics-driven companies (2019) shows that only a small fraction of firms realize significant returns from data initiatives – often due to lack of integration with culture and decision-making processes. In case you missed the point above: more isn’t always better.  

While the premise of limiting cognitive load has shaped many workplace learning tools and dashboards, Mayer (2024) notes that our understanding of the problem must evolve beyond managing overload toward supporting active integration.  

Similarly, Krieglstein et al. (2022) question the reliability of commonly used cognitive load questionnaires, suggesting that subjective perceptions of overload don’t always map onto cognitive strain in a measurable way.  

It’s probably not a shocker, but asking people whether they feel overwhelmed is not a solid way to determine whether people are overloaded with information. This highlights the need to rethink how organizations diagnose and respond to “overwhelm.” 

As philosopher Herbert Simon put it, “a wealth of information creates a poverty of attention.” Organizations must evolve from data hoarding to attention steering, from knowledge management to knowledge application. 

Yet this “poverty of attention” isn't purely a matter of volume – it also depends on attention control. This too is probably not a shocker to many of us. We need to be able to focus as well. Draheim et al. (2022) show that attentional regulation is a distinct capacity that explains variance in performance on complex, real-world tasks beyond working memory capacity. In short, organizations may need to cultivate not just knowledge, but the attentional skill to focus on what matters amid noise. 

From Data to Wisdom: A New Model for Organizations 

The classical DIKW hierarchy – Data → Information → Knowledge → Wisdom – is useful, but it often breaks down in practice. In reality, organizations get stuck at the “knowledge” level: codifying rules, building repositories, and treating knowledge as a thing, rather than a flow or capability. 

To make the leap to wisdom, organizations must focus on: 

  • Synthesizing across domains 

  • Understanding context and consequence 

  • Learning from collective experience 

  • Applying ethical and strategic judgment 

Organizations can play an important role in Insight formation, by recognizing that insights rely on metacognitive awareness – knowing when we know or don’t know. Krasnoff and Oberauer (2023) found that individuals often misjudge the limits of what they remember, suggesting that building systems for collective insight requires not just information synthesis, but structures that support reflection, humility, and confidence calibration. (As well as encouragement toward curiosity, but I don’t have space to cover that here.) 

Wisdom, in brief, is active knowledge – knowledge applied well in uncertain situations. 
(See: Rowley, 2007. The Wisdom Hierarchy: Representations of the DIKW Hierarchy) 

Culture: The Missing Link in Wisdom Work 

Many early knowledge management system (KM) failures were not technical – they were cultural. As Schein (2010) emphasized, organizational culture determines whether learning and change are possible. Without psychological safety, tacit knowledge remains unspoken. Without trust, insights are hoarded. Without shared purpose, information lacks meaning. 

Research by Amy Edmondson (1999) shows that teams with psychological safety are more likely to share information, learn from failures, and innovate. Emerging research also shows that not all learning happens consciously. Drigas, Mitsea, and Skianis (2022) emphasize the role of subliminal processing in shaping emotional and behavioral regulation. In environments where conscious dialogue is limited – due to hierarchy, stress, or cultural norms – technology-assisted subliminal and emotional training tools may offer complementary pathways to wisdom development. 

Wisdom generation requires not just tools and processes, but an environment where people can speak, reflect, and learn together. 

Why This Matters Now 

In the Knowledge Age, competitive advantage comes from: 

  • Acting on what matters, not just knowing more 

  • Integrating human and artificial intelligence wisely 

  • Creating organizations that learn and adapt continuously 

Recent work by Cowan et al. (2024) calls for an integrated model where attention and memory are not separate systems, but tightly interwoven mechanisms that jointly serve behavior. This supports the idea that learning organizations must go beyond knowledge capture – they must foster environments where attention, memory, and insight are aligned through collective, adaptive practices. 

The future belongs to learning organizations – those that continually generate collective phronesis (practical wisdom), align human values with machine capabilities, and turn complexity into clarity. 

Your Turn 

Where is your organization on the journey from data to wisdom? 
Have you seen successful practices for turning information into collective insight – or experienced challenges in doing so? 
Share your reflections or lessons learned in the comments; let’s build some collective wisdom together.  

References Cited 

 

Want to be part of the (r)evolution?  

I am putting the finishing touches on the first draft of a book with a friend and colleague Andrew Lopianowski on the concept, which we are calling HumanCorps. If you’d like to learn more about the book, or perhaps have some amazing stories of people who are putting these efforts in motion to be the change we need, please drop me a line.    

Finlay McNeill
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