Donald Rumsfeld, the former Secretary of Defense, delivered a quote that has since been analyzed and discussed in various fields regardless of your politics, including project management, risk analysis, and knowledge management.
"There are known knowns; there are things we know we know. We also know there are known unknowns; that is to say, we know there are some things we do not know. But there are also unknown unknowns — the ones we don't know we don't know."
- Donald Rumsfeld – Pentagon Briefing 2.12.2002
His classification of knowledge into - known knowns, known unknowns, and unknown unknowns, albeit enigmatic, resonates strongly with the world of data and analytics. Let’s dive deeper into this connection, providing a new perspective on the integral process of addressing the challenges of 'knowns' and 'unknowns.'
The 'Known Knowns' Are Quite Simply The Things We Know We Know.
In the world of data analytics, these are comparable to the structured, easily quantifiable data businesses have at their disposal. Such data could include past sales figures, customer demographics, or product attributes—readily available, comprehensible, and applicable information in decision-making processes.
These 'known knowns' allow organizations to generate descriptive analytics, which is the interpretation of historical data to better understand changes that have occurred in a business. It is a retrospective view that helps understand what has happened and, to a certain extent, why it happened. Descriptive analytics is the backbone of sound decision-making and a starting point in the data analytics journey.
The ‘Known Unknowns’ Are Things We Know We Don't Know.
In data and analytics, these might represent areas where a business recognizes a knowledge gap but isn't sure how to fill it. For instance, a company might know that customer satisfaction impacts sales but does not know the extent of this relationship.
To tackle these 'known unknowns,' businesses can leverage predictive analytics. This data analytics component uses statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. It aims to go beyond the descriptive to provide the best assessment of what will happen in the future. It equips organizations with potential scenarios and enables them to strategize effectively in response to those predictions.
Finally, We Come To The 'Unknown Unknowns,' The Knowledge We Don't Know, We Don't Know.
These represent the blind spots, the questions we don't even know to ask. In the business context, this could be a market trend that hasn't been identified, an unseen customer behavior pattern, or a new opportunity or threat in the business environment.
Prescriptive analytics deals with these 'unknown unknowns' by leveraging both descriptive and predictive findings and recommending actions. This type of analytics goes beyond predicting future outcomes and suggests actions to benefit from predictions. It involves techniques like graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning.
Data-driven organizations go even further to unearth these ‘unknown unknowns’, deploying exploratory data analysis, data mining, and advanced AI algorithms to sift through large volumes of data and discover hidden patterns. Using these methods opens new avenues of inquiry and insight, taking organizations from a reactive stance to a proactive one, often resulting in a significant competitive advantage.
Rumsfeld's infamous quote provides an interesting lens for businesses to think about how they can gain a deeper understanding of how businesses can effectively harness the power of their data. The 'known knowns' represent the foundation of any data analytics effort, the 'known unknowns' embody the potential of predictive analytics, and the 'unknown unknowns' represent significant business growth opportunities.
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