Data & HR: the DIKW theory
Traditionally focused on human relations and administrative processes, the HR function must now embrace a data-driven approach to effectively manage human capital. This transition represents a significant challenge for a discipline that has not historically been data-centric. The DIKW (Data, Information, Knowledge, Wisdom) theory offers a relevant theoretical framework to understand and navigate this transformation.
About the DIKW theory
The information economy teaches us that information is a valuable asset that reduces uncertainty and optimizes decision-making. George Stigler’s work on the economics of information illustrates that searching for and collecting information has a cost, but the benefits often justify this investment (Stigler, 1961). The roots of the DIKW theory can be traced to early work in information management and cybernetics.
Pioneers like Claude Shannon and Warren Weaver, with their mathematical theory of communication in the 1940s, laid the foundation for understanding information transmission and processing (Shannon & Weaver, 1949). However, the formalization of the DIKW hierarchy is often attributed to Russell L. Ackoff, a systems theorist and manager. In his article “From Data to Wisdom” published in 1989, Ackoff clarified the distinction between data, information, knowledge, and wisdom, emphasizing the importance of each level in the decision-making process (Ackoff, 1989).
Data: the raw material
Data is the foundation of any informed analysis and decision-making, and also a crucial driver of competitiveness in the current context. In the specific context of HR, data encompasses a variety of metrics such as employee turnover rates, absenteeism rates, performance evaluation results, employee feedback, job satisfaction rates, and many other crucial indicators. Each raw data point, in isolation, may seem insignificant. However, their aggregation and analysis reveal essential trends and insights for strategic HR management (Ackoff, 1989).
The history of statistics reminds us that data collection has always been crucial for understanding social phenomena. Chinese, Egyptian, and Roman censuses counted people, goods, and even animals for administrative and tax purposes (Porter, 1986). In the 19th century, the rise of modern statistics transformed data collection into a rigorous science. Adolphe Quetelet, with his concept of the “average man,” and Florence Nightingale, with her rose diagrams, illustrated how well-collected and analyzed data could influence public policies and medical practices.
Historically, HR professionals have primarily relied on qualitative data and intuitive observations to guide their decisions. For example, individual interviews and subjective performance evaluations have long dominated the HR landscape. However, these methods, though important, often lack the objectivity and precision needed to meet contemporary challenges.
With the rise of information technologies, the collection and storage of quantitative data have become more accessible and systematic. Today, every interaction, every evaluation, and every event in the employee lifecycle can be captured and stored digitally. From talent management tools to continuous feedback platforms, HR data is now massive, disparate, and often disorganized. This mass of raw data, without adequate processing, is devoid of immediate meaning.
Information: structuring and contextualizing
Transforming raw data into usable information is a crucial step in the HR management process. This transformation requires structuring and contextualizing data to answer fundamental questions such as “who,” “what,” “where,” and “when.” In other words, it involves organizing data so that it becomes understandable and meaningful to decision-makers.
Structuring data means organizing it coherently and logically. For example, turnover data can be structured by department, job level, or quarter. This organization allows HR managers to detect trends and anomalies that would be invisible in an unstructured data mass. For example, a quarterly analysis of turnover rates could reveal seasonal departure peaks, while segmentation by department could identify specific units with abnormally high turnover rates (Davenport & Prusak, 1998).
Contextualizing data means placing it in a framework that gives it meaning. It involves understanding the circumstances and factors surrounding the data. For example, simply knowing that a department has a 20% turnover rate is not enough. To make this information useful, it is necessary to understand the specific conditions of the department, the potential reasons for the departures, and how this rate compares to industry benchmarks or the organization’s internal goals.
Contextualization also allows linking data together. For example, by examining turnover rates in conjunction with performance evaluation results and employee satisfaction rates, HR can obtain a more complete picture of the dynamics within the organization. This holistic approach allows for a better understanding of underlying phenomena and the development of more effective strategies.
Knowledge: understanding and interpreting
Knowledge is at a higher level of complexity and added value compared to data and information. It is produced when information is meticulously analyzed and interpreted to answer the questions “how” and “why.” This analysis allows identifying the relationships and patterns underlying raw data and understanding the complex dynamics that influence organizational outcomes.
Interpreting information transforms isolated facts into a coherent understanding of organizational phenomena. For example, suppose HR identifies a high turnover rate in a specific department. By delving deeper into the analysis, they may discover that this turnover rate is linked to an authoritarian management style or unfavorable working conditions. Understanding these links requires detailed analysis of employee feedback, performance evaluations, and contextual data on managerial practices (Nonaka & Takeuchi, 1995).
Organizational psychology offers valuable theoretical frameworks for understanding and interpreting human behavior and attitudes at work. Motivation theories, such as Herzberg’s two-factor theory, distinguish between job satisfaction factors (motivators) and dissatisfaction factors (hygiene factors). Herzberg (1966) showed that motivators like recognition, increased responsibilities, and professional growth opportunities are essential for employee engagement, while dissatisfaction factors, such as working conditions and remuneration, must be adequate to avoid demotivation.
A concrete example of transforming information into knowledge is analyzing team performance. Suppose a company notices a significant performance disparity between its sales teams. By examining the available information, HR may discover that the highest-performing teams benefit from participative management and regular training, while the lower-performing teams lack support and professional development. These elements allow HR to propose targeted interventions to improve the performance of the lower-performing teams, such as implementing continuous training programs and promoting participative management practices.
Insights: toward a strategic vision
Insights go far beyond simply understanding data and information. They represent flashes of genius that reveal hidden opportunities or potential threats. These insights are often the result of in-depth and creative analysis of existing knowledge, allowing companies to make informed decisions that can transform their performance and competitiveness.
Strategic insights are essential because they enable organizations to see beyond the obvious and anticipate future trends. For example, a company that discovers, through detailed analysis, that employees participating in professional development programs are more engaged and less likely to leave the company, holds a valuable strategic insight. This insight can guide HR policies toward investing in continuous training programs, thus increasing talent retention and improving overall organizational performance (Davenport & Prusak, 1998).
Developing unique insights is a crucial strategic skill. Michael Porter, in his work on competitive advantages, emphasizes that companies must constantly seek to develop insights that allow them to differentiate themselves from the competition and create value uniquely (Porter, 1985). For HR, this means using available data and knowledge to identify performance levers that are not immediately obvious. Insights, therefore, involve seeing beyond what the data tells.
A concrete example of a strategic insight in HR could be the discovery that employees with mentoring opportunities are not only more satisfied with their job but also more productive and engaged. By analyzing performance and satisfaction data, HR can identify that mentoring relationships significantly contribute to professional fulfillment. This insight can lead to the implementation of formal mentoring programs, thus strengthening organizational culture and improving talent retention.
Wisdom: leading enlightened application
Wisdom is the pinnacle of the DIKW hierarchy, representing the judicious and ethical application of knowledge and insights in decision-making. It goes beyond understanding available information to integrating ethical reflection and long-term vision. For HR, wisdom involves making decisions that maximize not only organizational performance but also the well-being and interests of employees, as well as broader sustainability issues (Ackoff, 1989).
Wisdom includes an essential normative dimension for ethical and responsible decision-making. Aristotle, in his philosophical works, defined practical wisdom (phronesis) as the ability to act justly and appropriately in concrete situations. This notion is crucial for HR, as it guides the development of policies and practices that balance the organization’s needs with those of employees while considering social and environmental implications (Nonaka & Takeuchi, 1995).
For example, in the process of restructuring a company, wisdom involves considering not only potential financial benefits but also the impact on employees, local communities, and the environment. This can lead to more balanced and sustainable decisions, such as investing in employee reskilling rather than massive layoffs.
Applying the DIKW theory in HR
Adopting the DIKW hierarchy in HR management involves transforming raw data into practical wisdom through a continuous and integrated process. It starts with acquiring accurate and relevant data, structuring it into usable information, interpreting it to generate knowledge, and identifying strategic insights. Finally, wisdom manifests in the ability to use these insights to make informed and balanced decisions (Davenport & Prusak, 1998).
For example, let’s consider reducing turnover rates. Initial data may include voluntary departure rates, segmented by department and quarter. By transforming this data into information, HR can identify the most affected periods and departments. By deepening this analysis to understand why these departures occur, HR gains knowledge about the underlying causes. Insights may then emerge, such as the importance of leadership in talent retention. Finally, wisdom guides the development of training programs for managers, aiming to improve their leadership skills and thus reduce turnover rates (Herzberg, 1966).
Beyond turnover management, the DIKW hierarchy can be applied to various HR aspects, such as recruitment, talent management, employee engagement, and strategic planning. For example, in recruitment, data may include application sources, conversion rates from applications to hires, and new employee performance. By structuring and analyzing this data, HR can identify the most effective sources and optimize recruitment strategies (Drucker, 1999). Similarly, in talent management, data on employee skills and performance can be transformed into information and knowledge to develop personalized career plans and professional development programs (Nonaka & Takeuchi, 1995).
Conclusion
Integrating the DIKW theory into human resources management marks a decisive turning point for the HR function. This approach transforms a traditionally intuitive discipline into a data-driven practice, aligned with the demands of the modern professional world.
By following the DIKW hierarchy, HR professionals can:
- Efficiently leverage the growing mass of available data
- Extract relevant and contextualized information
- Develop deep knowledge of organizational dynamics
- Generate strategic insights to guide decision-making
- Cultivate practical wisdom, integrating ethics and long-term vision
This holistic approach not only optimizes HR processes but also positions the function as a key strategic partner within the organization. It provides HR with the tools needed to anticipate trends, adapt human capital management strategies, and contribute significantly to the company’s performance and sustainability.
However, adopting the DIKW theory requires a paradigm shift and the development of new skills within HR teams. Mastering data analysis tools, understanding the ethical issues related to data use, and the ability to translate insights into concrete actions become essential. By embracing this approach, HR can not only improve their operational efficiency but also play a crucial role in creating long-term value for the organization and its stakeholders. The DIKW theory thus offers a powerful framework for navigating the complexity of the contemporary work world and shaping the future of human resources.
References
Ackoff, R. L. (1989). From Data to Wisdom. Journal of Applied Systems Analysis, 16, 3–9.
Aristote. (1991). Éthique à Nicomaque. Paris : Flammarion.
Davenport, T. H., & Prusak, L. (1998). Working Knowledge: How Organizations Manage What They Know. Harvard Business School Press.
Drucker, P. F. (1999). Management Challenges for the 21st Century. Harper Business.
Herzberg, F. (1966). Work and the Nature of Man. Cleveland: World Publishing Company.
Nonaka, I., & Takeuchi, H. (1995). The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press.
Polanyi, M. (1966). The Tacit Dimension. University of Chicago Press.
Porter, M. E. (1985). Competitive Advantage: Creating and Sustaining Superior Performance. Free Press.
Porter, T. M. (1986). The Rise of Statistical Thinking, 1820–1900. Princeton University Press.
Shannon, C. E., & Weaver, W. (1949). The Mathematical Theory of Communication. University of Illinois Press.
Stigler, G. J. (1961). The Economics of Information. Journal of Political Economy, 69(3), 213–225.
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[Article created on 15 july, 2024, by Jeremy Lamri with the support of the Claude 3.5 Sonnet and GPT-4 for structuring, enriching, and illustrating. The writing is primarily my own, as are most of the ideas in this article].
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