Data as language: good for business and people

Aim of blog

The aim of this blog is to look at the relationship between data, high performance, and social wellbeing and to present an innovation in understanding social impact.

Data as language

At the first annual meeting of the Society for Organizational Learning Humberto Maturana explained that language is the recursive coordination of actions. 

“Language is not abstract. Language has the concreteness of doings, it is the coordination of doings.” [1]

The Swiss psychologist who inspired Arie de Gues, Jean Piaget, wrote of knowledge as the general coordinations of individual and collective actions[2]. Language and knowledge are inextricably linked. For organizations interested in high performance and social well-being this is fundamental because the coordinations of collective actions in dynamic social networks is the source of all value creation.

Data and recursion

In 1928, Walter Shewhart and WE Deming transformed our approach to quality. Once thought to be a linear process of inspection determining whether specifications were met, Shewhart and Deming described a ­recursive learning process later described as the plan-do-check-act cycle and later by Deming as a plan-do-study-act cycle of continuous improvement.

Once plans are made by organization leaders, workers begin to do the planned work, taking time to study whether their actions create value triggering the continuous improvement cycle coordinating individual and collective actions. If studies validate the planned work creates value – the plan is sustained and if the studies show the planned work does not create value, plans change, and improvements are made. This is a recursive cycle, Deming described as more of a spiral than a closed circle [3].

Data criteria

Data creates knowledge and guides individual and collective coordination of actions. To do so, data must meet simple criteria. Simple, but, depending on the culture of the workplace, sometimes difficult to implement.

Data must be timely, actionable, relevant, and accessible.

1. Timely data is data that can be used in our daily living to take effective action.
2. Actionable data improves performance while data without action can be a distraction and misleading, triggering ineffective actions resulting in negative human and social capital. The cost of misleading data is enormous.
3. To be actionable, data must be relevant. When we think about our conversations in the workplace, we can see that many conversations are not related to individual and collective performance, despite our intentions. Relevant data, needs no explanation, its value is in being timely and actionable.
4. Of course, without having access to the data, actions can not be taken. Openness and transparency are essential qualities of a data-based performance culture.

A systemic architectur: The Accomplishment Model

Besides establishing the criteria for data, leaders need to link daily data to the purpose of the organization. This is done following a simple process following two questions. The first question is “What must be accomplished?”­­­ and the second, “How will we know we have achieved our accomplishment?” Both questions are applied at different levels of organizational performance.

In 1980, people with developmental disabilities were put into state hospitals and subject to unimaginable abuse and neglect. I opened a not-for-profit business, and our purpose was to employ people with developmental disabilities. Behind our purpose was a logic that if the most vulnerable in Oregon’s institutions could become productive citizens, we could make a case for closing state hospitals and use the savings to start community-based employment services.  Regarding the purpose, what needed to be accomplished was employing people with developmental disabilities and we knew we achieved this accomplishment by studying our monthly wage data.  This was the first step in creating an Accomplishment Model linking our purpose and daily performance data.

At the second level in the Accomplishment Model, I asked, “What do we need to accomplish to achieve our purpose?” The answer was quite simple. we needed to achieve four measured accomplishments.

1. Get work.
2. Keep work.
3. Train the workforce.
4. Support social integration.

I then asked, “How will we know we have achieved these accomplishments?” and established the performance measure shown below.

Completing the Accomplishment Model, I asked, “What needs to be accomplished for each of the four accomplishments?” and “How will we know when we have achieved them?”

The Accomplishment Model is a systemic performance tool. The measure of our purpose was perpetual, with monthly wages reported annually. At the second level, our measures for getting work, keeping work, training our workforce, and supporting their social integration were measured monthly. At the third level of the Accomplishment Model, our data were collected and studied daily, using the Deming cycle to constantly improve our services. All data measures were charted daily, publicly posted, and reported [4, 5]. The Accomplishment Model system worked. Within a year of our opening, Oregon passed legislation closing state hospitals and established community-based services [6].

Making data-based decisions

How does an organization achieve high performance? There are two paths and one answer. Decision-making is either based on data, or, rank and opinions. Opinionated decision-making occurs in management control hierarchies. Workers learn that the opinions and judgments of those in authority do not necessarily lead to high performance or social wellbeing. In contrast, data-based decision-making leads to high performance and social wellbeing.

Data at work: Generating a culture of high performance and social wellbeing

Our global passion for sports teaches us that using data to improve performance is vital to individual and team success. Take, for example, the history of men running mile events.

Since the inception of the International Association of Athletics Federations men running the mile have improved from 4:10 minute miles to running a bit over 3:40 miles.

The mile run data shows the history of high performance in the global running community. But, how does data connect to our wellbeing?

1. Data creates equity. Jackie Robinson was the first African American to play in major league baseball. It wasn’t his African descent that enabled him to cross baseball’s “color line”. It was his performance data. A year before he started with the Brooklyn Dodgers, he played 124 games with the Montreal Royals, batting .349 with 25 doubles, eight triples, three home runs, 66 RBIs, and 40 stolen bases. When data-based decisions include all workers, data creates social equity in the workplace culture. 
2. Data creates accountability. Data that is actionable, timely, relevant, and accessible fosters accountability—the workers’ responsibility for their individual and collective actions. Without data, accountability becomes an often contentious debate.
3. Data improves productivity.  Productivity is measured as the creation of value over time. When an organization uses data-based decision-making on a daily basis, workers are recognized for making a difference, and, because the recognition comes from their peers, it is a powerful reward. Data improves productivity because it decreases the time it takes to create value by rewarding value-creating actions measured by the Accomplishment Model.
4. Data builds confidence. Canadian psychologist, Albert Bandura explains how our success is dependent on confidence in our abilities. Group success is dependent upon the group’s confidence in their collective abilities. Accomplishment data boosts confidence because the continuous improvement data builds individual and group confidence that their performance can consistently meet the demands of the organization. Measured accomplishments are a principal source of efficacy [7].
5. Data improves work relationships. In a data-based decision-making culture, we know when we are generating value, and when we know we are generating value, we know we are working well together. With decades of research, including Hewlett-Packard’s Work Relationship Index [8], we know that knowledge workers often have an unhealthy relationship with work. The relationship is so unhealthy that it can create unnecessary heart disease and ill mental health [9].  Using the Accomplishment Model will bring about what HP’s CEO calls for in his announcement of HP’s findings.

I believe there is a huge opportunity to strengthen the world’s relationship with work in ways that are both good for people and good for business. And it’s critically important that we do – because the world’s relationship with work today is strained.[10]

References

1.         Maturana, H. and P. Bunnell. Biosphere, Homosphere and Robosphere. 1998.

2.         Piaget, J., Biology and knowledge. 1971, Chicago: University of Chicago Press. 383.

3.         Shewhart, W.A., Statistical method from the viewpoint of quality control, ed. W. Deming, Edwards. 1939, Washington, DC: The Graduate School, Department of Agriculture. 155.

4.         Sandow, D., Dynatron Incorporated Biennial Report. 1980, Dyantron Incorporated: Bend, OR.

5.         Sandow, D., Dynatron Incorporated Biennial Report. 1982, Dynatron Incorporated: Bend, OR.

6.         Oregon Legislative Assembly, House Bill 3232, Oregon House of Representatives. Resources, Editor. 1981, Oregon Legislative Assembly: Salem, OR.

7.         Bandura, A., Self-efficacy: Toward a Unifying Theory of Behavioral Change. Psychological Review, 1977. 84(2): p. 191-215.

8.         HP Development Company, L.P., Work Relationship Index. 2023.

9.         Public and Commercial Services Union, Work stress and health: the Whitehall II study. 2004, Council of Civil Service Unions/Cabinet Office: London.

10.       Lores, E. HP CEO: The world has an unhealthy relationship with work. Fast Company, 2023.