🗄️Data Management: Data Culture (Part III: A Tale of Two Cities)

One of the curious things is that as part of their change of culture organizations try to adopt a new language, to give new names to things, try to make distinction between the "AS IS" and "TO BE" states, insisting how the new image will replace the previous one. Occasionally, they even stress how bad things were in the past and how great will be in the future, trying to depict the future in vivid images. 

Even if this might work occasionally, it tends to confuse people and this not necessarily because of the language and the metaphors used, or the fact that same people were in the same positions, but the lack of belief or conviction, respectively half-hearted enthusiasm personified by the parties. To "convert" people to new philosophies one needs to believe in them or mimic that in similar terms. The lack of conviction can easily have a false effect that spreads within the organization. 

Dissociation from the past, from what an organization was, tends to increase the resistance against the new because two different images are involved. On one side there’s the attachment to the past, and even if there were mistakes made, or things didn’t go optimally, the experiences and decisions made are part of the organization, of the people who made them. People as individuals and as an organization should embrace their mistakes and good deeds altogether, learn from them, improve what is to improve and move forward. Conversely, there’s the resistance to the new, to the change, words they don’t believe in yet, the bigger picture is still fuzzy in their minds, and there can be many other reasons that don’t agree with one’s understanding. 

There are images, memories, views, decisions, objectives of the past and people need to recognize the road from what it was to what should be. One can hypothesize that embracing one’s mistake and understanding, the chain of reasoning from then and from now will help an organization transition towards the new. Awareness of one’s situation most probably will help in the transition process. Unfortunately, leaders and technology gurus tend to depict the past as negative, creating thus more negative emotions, respectively reactions in the process. The past is still part of the people, of the organization and will continue to be.

Conversely, the disassociation from the past can create more resistance to the new, and probably more unnecessary barriers. Probably, it’s easier for the gurus to build the new if the past weren’t there! Forgetting the past would be an error because there are many lessons that can be still useful. All the experience needs to be redirected in new directions. It’s more important to help people see the vision of the future, understand their missions, the paths to be followed and the challenges ahead, . 

It sounds more of a rambling from a psychology course, though organizations do have an image they want to change, to bring forth to cope with the various challenges, an image they want to reflect when needed. There are also organizations that want to change but keep their image intact, which leads to deeper conflicts. Unfortunately, changes of image involve conflicts that can become complex from what they bring forth.  

A data culture should increase people’s awareness of the present, respectively of the future, of what it takes to bridge the gap, the challenges ahead, how to embrace change, how to keep a realistic perspective, how to do a reality check, etc. Methodologies can increase people’s awareness and provide the theoretical basis, though walking the path will be a different story for everyone. 

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🧭Business Intelligence: Data Culture (Part I: Generative AI - No Silver Bullet)

Business Intelligence Series

Talking about holy grails in Data Analytics, another topic of major importance for an organization’s "infrastructure" is data culture, that can be defined as the collective beliefs, values, behaviors, and practices of an organization’s employees in harnessing the value of data for decision-making, operations, or insight. Rooted in data literacy, data culture is an extension of an organization’s culture in respect to data that acts as enabler in harnessing the value of data. It’s about thinking critically about data and how data is used to create value. 

The current topic was suggested by PowerBI.tips’s webcast from today [3] and is based on Brent Dykes’ article from Forbes ‘Why AI Isn’t Going to Solve All Your Data Culture Problems’ [1]. Dykes’ starting point for the discussion is Wavestone's annual data executive survey based on which the number of companies that reported they had "created a data-driven organization" rose sharply from 23.9 percent in 2023 to 48.1 percent in 2024 [2]. The report’s authors concluded that the result is driven by the adoption of Generative AI, the capabilities of OpenAI-like tools to generate context-dependent meaningful text, images, and other content in response to prompts. 

I agree with Dykes that AI technologies can’t be a silver bullet for an organization data culture given that AI either replaces people’s behaviors or augments existing ones, being thus a substitute and not a cure [1]. Even for a disruptive technology like Generative AI, it’s impossible to change so much employees’ mindset in a so short period of time. Typically, a data culture matures over years with sustained effort. Therefore, the argument that the increase is due to respondent’s false perception is more than plausible. There’s indeed a big difference between thinking about an organization as being data-driven and being data-driven. 

The three questions-based evaluation considered in the article addresses this difference, thinking vs. being. Changes in data culture don’t occur just because some people or metrics say so, but when people change their mental models based on data, when the interpersonal relations change, when the whole dynamics within the organization changes (positively). If people continue the same behavior and practices, then there are high chances that no change occurred besides the Brownian movement in a confined space of employees, that’s just chaotic motion.  

Indeed, a data culture should encourage the discovery, exploration, collaboration, discussions [1] respectively knowledge sharing and make people more receptive and responsive about environmental or circumstance changes. However, just involving leadership and having things prioritized and funded is not enough, no matter how powerful the drive. These can act as enablers, though more important is to awaken and guide people’s interest, working on people’s motivation and supporting the learning process through mentoring. No amount of brute force can make a mind move and evolve freely unless the mind is driven by an inborn curiosity!

Driving a self-driving car doesn’t make one a better driver. Technology should challenge people and expand their understanding of how data can be used in different contexts rather than give solutions based on a mass of texts available as input. This is how people grow meaningfully and how an organization’s culture expands. Readily available answers make people become dull and dependent on technology, which in the long-term can create more problems. Technology can solve problems when used creatively, when problems and their context are properly understood, and the solutions customized accordingly.

Unfortunately, for many organizations data culture will be just a topic to philosophy about. Data culture implies a change of mindset, perception, mental models, behavior, and practices based on data and not only consulting the data to confirm one’s biases on how the business operates!

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Resources:
[1] Forbes (2024) Why AI Isn’t Going To Solve All Your Data Culture Problems, by Brent Dykes (link)
[2] Wavestone (2024) 2024 Data and AI Leadership Executive Survey (link)
[3] Power BI tips (2024) Ep.299: AI & Data Culture Problems (link)

🔭Data Science: Belief (Just the Quotes)

"By degree of probability we really mean, or ought to mean, degree of belief [...] Probability then, refers to and implies belief, more or less, and belief is but another name for imperfect knowledge, or it may be, expresses the mind in a state of imperfect knowledge." (Augustus De Morgan, "Formal Logic: Or, The Calculus of Inference, Necessary and Probable", 1847)

"To a scientist a theory is something to be tested. He seeks not to defend his beliefs, but to improve them. He is, above everything else, an expert at ‘changing his mind’." (Wendell Johnson, 1946)

"A model can not be proved to be correct; at best it can only be found to be reasonably consistant and not to contradict some of our beliefs of what reality is." (Richard W Hamming, "The Art of Probability for Scientists and Engineers", 1991)

"Probability is not about the odds, but about the belief in the existence of an alternative outcome, cause, or motive." (Nassim N Taleb, "Fooled by Randomness", 2001)

"The Bayesian approach is based on the following postulates: (B1) Probability describes degree of belief, not limiting frequency. As such, we can make probability statements about lots of things, not just data which are subject to random variation. […] (B2) We can make probability statements about parameters, even though they are fixed constants. (B3) We make inferences about a parameter θ by producing a probability distribution for θ. Inferences, such as point estimates and interval estimates, may then be extracted from this distribution." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"The important thing is to understand that frequentist and Bayesian methods are answering different questions. To combine prior beliefs with data in a principled way, use Bayesian inference. To construct procedures with guaranteed long run performance, such as confidence intervals, use frequentist methods. Generally, Bayesian methods run into problems when the parameter space is high dimensional." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"Our inner weighing of evidence is not a careful mathematical calculation resulting in a probabilistic estimate of truth, but more like a whirlpool blending of the objective and the personal. The result is a set of beliefs - both conscious and unconscious - that guide us in interpreting all the events of our lives." (Leonard Mlodinow, "War of the Worldviews: Where Science and Spirituality Meet - and Do Not", 2011)

"The search for better numbers, like the quest for new technologies to improve our lives, is certainly worthwhile. But the belief that a few simple numbers, a few basic averages, can capture the multifaceted nature of national and global economic systems is a myth. Rather than seeking new simple numbers to replace our old simple numbers, we need to tap into both the power of our information age and our ability to construct our own maps of the world to answer the questions we need answering." (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

"One kind of probability - classic probability - is based on the idea of symmetry and equal likelihood […] In the classic case, we know the parameters of the system and thus can calculate the probabilities for the events each system will generate. […] A second kind of probability arises because in daily life we often want to know something about the likelihood of other events occurring […]. In this second case, we need to estimate the parameters of the system because we don’t know what those parameters are. […] A third kind of probability differs from these first two because it’s not obtained from an experiment or a replicable event - rather, it expresses an opinion or degree of belief about how likely a particular event is to occur. This is called subjective probability […]." (Daniel J Levitin, "Weaponized Lies", 2017)

"Bayesian statistics give us an objective way of combining the observed evidence with our prior knowledge (or subjective belief) to obtain a revised belief and hence a revised prediction of the outcome of the coin’s next toss. [...] This is perhaps the most important role of Bayes’s rule in statistics: we can estimate the conditional probability directly in one direction, for which our judgment is more reliable, and use mathematics to derive the conditional probability in the other direction, for which our judgment is rather hazy. The equation also plays this role in Bayesian networks; we tell the computer the forward  probabilities, and the computer tells us the inverse probabilities when needed." (Judea Pearl & Dana Mackenzie, "The Book of Why: The new science of cause and effect", 2018)

"The transparency of Bayesian networks distinguishes them from most other approaches to machine learning, which tend to produce inscrutable 'black boxes'. In a Bayesian network you can follow every step and understand how and why each piece of evidence changed the network’s beliefs." (Judea Pearl & Dana Mackenzie, "The Book of Why: The new science of cause and effect", 2018)

♟️Strategic Management: Belief (Just the Quotes)

"Instinct, intuition, or insight is what first leads to the beliefs which subsequent reason confirms or confutes; [...]" (Bertrand Russell, Our Knowledge of the External World", 1914)

"For imagination sets the goal picture which our automatic mechanism works on. We act, or fail to act, not because of will, as is so commonly believed, but because of imagination." (Maxwell Maltz, "Psycho-Cybernetics", 1960)

"Most of our beliefs about complex organizations follow from one or the other of two distinct strategies. The closed-system strategy seeks certainty by incorporating only those variables positively associated with goal achievement and subjecting them to a monolithic control network. The open-system strategy shifts attention from goal achievement to survival and incorporates uncertainty by recognizing organizational interdependence with environment. A newer tradition enables us to conceive of the organization as an open system, indeterminate and faced with uncertainty, but subject to criteria of rationality and hence needing certainty." (James D Thompson, "Organizations in Action", 1967)

"The degree of confirmation assigned to any given hypothesis is sensitive to properties of the entire belief system [...] simplicity, plausibility, and conservatism are properties that theories have in virtue of their relation to the whole structure of scientific beliefs taken collectively. A measure of conservatism or simplicity would be a metric over global properties of belief systems." (Jerry Fodor, "Modularity of Mind", 1983)

"Action often creates the orderly relations that originally were mere presumptions summarized in a cause map. Thus language trappings of organizations such as strategic plans are important components in the process of creating order. They hold events together long enough and tightly enough in people's heads so that they act in the belief that their actions will be influential and make sense." (Karl E Weick, "Organizational culture as a source of high reliability", 1987)

"The obsession with methodologies in the workplace is another instance of the high-tech illusion. It stems from the belief that what really matters is the technology. [...] Whatever the technological advantage may be, it may come only at the price of a significant worsening of the team's sociology." (Tom DeMarco & Timothy Lister, "Peopleware: Productive Projects and Teams", 1987)

"A culture may be conceived as a network of beliefs and purposes in which any string in the net pulls and is pulled by the others, thus perpetually changing the configuration of the whole. If the cultural element called morals takes on a new shape, we must ask what other strings have pulled it out of line. It cannot be one solitary string, nor even the strings nearby, for the network is three-dimensional at least." (Jacques Barzun, "The Culture We Deserve", 1989)

"A model can not be proved to be correct; at best it can only be found to be reasonably consistant and not to contradict some of our beliefs of what reality is." (Richard W Hamming, "The Art of Probability for Scientists and Engineers", 1991)

"Enterprise Engineering is based on the belief that an enterprise, as any other complex system can be designed or improved in an orderly fashion thus giving a better overall result than ad hoc organisation and design." (Peter Bernus et al, "Possibilities and limitations of reusing enterprise models", 1994)

"Beliefs are those ideas we take as true and use to guide our actions. We all have beliefs about what sort of people we are and what we are capable of. These beliefs act as permissions for or limitations on what we do. When we believe something is possible, we will try it; if we believe it impossible, we will not." (Joseph O’Connor, "Leading With NLP: Essential Leadership Skills for Influencing and Managing People", 1998) 

"Managers are incurably susceptible to panacea peddlers. They are rooted in the belief that there are simple, if not simple-minded, solutions to even the most complex of problems. And they do not learn from bad experiences. Managers fail to diagnose the failures of the fads they adopt; they do not understand them. […] Those at the top feel obliged to pretend to omniscience, and therefore refuse to learn anything new even if the cost of doing so is success." (Russell L Ackoff, "A Lifetime Of Systems Thinking", Systems Thinker, 1999)

"Probability is not about the odds, but about the belief in the existence of an alternative outcome, cause, or motive." (Nassim N Taleb, "Fooled by Randomness", 2001)

"The danger arises when a culture takes its own story as the absolute truth, and seeks to impose this truth on others as the yardstick of all knowledge and belief." (F David Peat, "From Certainty to Uncertainty", 2002)

"Enterprise architecture (EA) promotes the belief that an enterprise, as a complex system, can be designed or improved in an orderly fashion achieving better overall results than ad-hoc organisation and design. EA is a co-operative effort of designers, analysts and managers and uses enterprise models in the process [...] enterprise models carry meaning. This resulted in requirements for the enterprise engineering process, which - if not met - can limit the viability of the process. The analysis of the same factors resulted in requirements for improved Enterprise Modelling Tools." (Peter Bernus, "Enterprise models for enterprise architecture and ISO9000: 2000", 2003) 

"The less sure managers are of their opinions, the more vigorously they defend them. Managers do not waste their time defending beliefs they hold strongly – they just assert them. Nor do they bother to refute what they strongly believe is false." (Russell L Ackoff, "A Little Book of F-laws: 13 common sins of management", 2006)

"An organization’s culture is the underlying set of key values, beliefs, understandings, and norms shared by employees. These underlying values and norms may pertain to ethical behavior, commitment to employees, efficiency, or customer service, and they provide the glue to hold organization members together. An organization’s culture is unwritten but can be observed in its stories, slogans, ceremonies, dress, and office layout." (Richard L Daft, "Organization Theory and Design", 3rd Ed., 2010)

"Thorough rethinking of all business processes, job definitions, management systems, organizational structure, work flow, and underlying assumptions and beliefs. BPR’s main objective is to break away from old ways of working, and effect radical (not incremental) redesign of processes to achieve dramatic improvements in critical areas (such as cost, quality, service, and response time) through the in-depth use of information technology." (Elvira Rolón, "Healthcare Process Development with BPMN", 2010)

"Image theory is an attempt to describe decision making as it actually occurs. […] The concept of images is central to the theory. They represent visions held by individuals and organisations that constitute how they believe the world should exist. When considering individuals, the theory refers to these images as the value image, trajectory image and strategic image. The value image is based on an individual’s ethics, morals and beliefs. The trajectory images encompass the decision maker’s goals and aspirations. Finally, for each trajectory image, a decision maker may have one or more strategic images that contain their plans, tactics and forecasts for their goal. […] In an organisational decision-making setting, these images are referred to as culture, vision and strategy." (Christopher B Stephenson, "What causes top management teams to make poor strategic decisions?", 2012)

"A belief model is clung to not because it is 'correct'  - there is no way to know this - but rather because it has worked in the past and must cumulate a record of failure before it is worth discarding. In general, there may be a constant slow turnover of hypotheses acted upon. One could speak of this as a system of temporarily fulfilled expectations - beliefs or models or hypotheses that are temporarily fulfilled (though not perfectly), which give way to different beliefs or hypotheses when they cease to be fulfilled." (W Brian Arthur, "Complexity and the Economy", 2015)

"Our beliefs are based on our experience, which gives us a very incomplete picture of the world, and it's easy to jump to false conclusions." (Pedro Domingos, "The Master Algorithm", 2015)