🧭Business Intelligence: Perspectives (Part XIII: From Data to Storytelling I)

Business Intelligence Series

Data is an amalgam of signs, words, numbers and other visual or auditory elements used together to memorize, interpret, communicate and do whatever operation may seem appropriate with them. However, the data we use is usually part of one or multiple stories - how something came into being, what it represents, how is used in the various mental and non-mental processes - respectively, the facts, concepts, ideas, contexts places or other physical and nonphysical elements that are brought in connection with.

When we are the active creators of a story, we can in theory easily look at how the story came into being, the data used and its role in the bigger picture, respective the transformative elements considered or left out, etc. However, as soon we deal with a set of data, facts, or any other elements of a story we are not familiar with, we need to extrapolate the hypothetical elements that seem to be connected to the story. We need to make sense of these elements and consider all that seems meaningful, what we considered or left out shaping the story differently. 

As children and maybe even later, all of us dealt with stories in one way or another, we all got fascinated by metaphors' wisdom and felt the energy that kept us awake, focused and even transformed by the words coming from narrator's voice, probably without thinking too much at the whole picture, but letting the words do their magic. Growing up, the stories grew in complexity, probably became richer in meaning and contexts, as we were able to decipher the metaphors and other elements, as we included more knowledge about the world around, about stories and storytelling.

In the professional context, storytelling became associated with our profession - data, information, knowledge and wisdom being created, assimilated and exchanged in more complex processes. From, this perspective, data storytelling is about putting data into a (business) context to seed cultural ground, to promote decision making and better understanding by building a narrative around the data, problems, challenges, opportunities, and further organizational context.

Further on, from a BI's perspective, all these cognitive processes impact on how data, information and knowledge are created, (pre)processed, used and communicated in organizations especially when considering data visualizations and their constituent elements (e.g. data, text, labels, metaphors, visual cues), the narratives that seem compelling and resonate with the auditorium. 

There's no wonder that data storytelling has become something not to neglect in many business contexts. Storytelling has proved that words, images and metaphors can transmit ideas and knowledge, be transformative, make people think, or even act without much thinking. Stories have the power to seed memes, ideas, or more complex constructs into our minds, they can be used (for noble purposes) or misused. 

A story's author usually takes compelling images, metaphors, and further elements, manipulates them to the degree they become interesting to himself/herself, to the auditorium, to the degree they are transformative and become an element of the business vocabulary, respectively culture, without the need to reiterate them when needed to bring more complex concepts, ideas or metaphors into being.  

A story can be seen as a replication of the constituting elements, while storytelling is a set of functions that operate on them and change the initial structure and content into something that might look or not like the initial story. Through retelling and reprocessing in any form, the story changes independently of its initial form and content. Sometimes, the auditorium makes connections not recognized or intended by the storyteller. Other times, the use and manipulation of language makes the story change as seems fit. 

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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)

💼Project Management: Lean Management (Part I: Between Value and Waste I - An Introduction)

 

Independently on whether Lean Management is considered in the context of Manufacturing, Software Development (SD), Project Management (PM) or any other business-related areas, there are three fundamental business concepts on which the whole scaffolding of the Lean philosophies is built upon, namely the ones of value, value stream and waste. 

From an economic standpoint, value refers to the monetary worth of a product, asset or service (further referred as product) to an organization, while from a qualitative perspective, it refers to the perceived benefit associated with its usage. The value is thus reflected in the costs associated with a product’s delivery (producer’s perspective), respectively the price paid on acquiring it and the degree to which the product can fulfill a demand (customer’s perspective).

Without diving too deep into theory of product valuation, the challenges revolve around reducing the costs associated with a product’s delivery, respectively selling it to a price the customer is willing to pay for, typically to address a given set of needs. Moreover, the customer is willing to pay only for the functions that satisfy the needs a product is thought to cover. From this friction of opposing driving forces, a product is designed and valued.

The value stream is the sequence of activities (also steps or processes) needed to deliver a product to customers. This formulation includes value-added and non-value-added activities, internal and external customers, respectively covers the full lifecycle of products and/or services in whatever form it occurs, either if is or not perceived by the customers.  

Waste is any activity that consumes resources but creates no value for the customers or, generally, for the stakeholders, be it internal or external. The waste is typically associated with the non-added value activities, activities that don’t produce value for stakeholders, and can increase directly or indirectly the costs of products especially when no attention is given to it and/or not recognized as such. Therefore, eliminating the waste can have an important impact on products’ costs and become one of the goals of Lean Management. Moreover, eliminating the waste is an incremental process that, when put in the context of continuous improvement, can lead to processes redesign and re-engineering.

Taiichi Ohno, the ‘father’ of the Toyota Production System (TPS), originally identified seven forms of waste (Japanese: muda): overproduction, waiting, transporting, inappropriate processing, unnecessary inventory, unnecessary/excess motion, and defects. Within the context of SD and PM, Tom and Marry Poppendieck [1] translated the types of wastes in concepts closer to the language of software developers: partially done work, extra processes, extra features, task switching, waiting, motion and, of course, defects. To this list were added later further types of waste associated with resources, confusion and work conditions.

Defects in form of errors and bugs, ineffective communication, rework and overwork, waiting, repetitive activities like handoffs or even unnecessary meetings are usually the visible part of products and projects and important from the perspective of stakeholders, which in extremis can become sensitive when their volume increases out of proportion.

Unfortunately, lurking in the deep waters of projects and wrecking everything that stands in their way are the other forms of waste less perceivable from stakeholders’ side: unclear requirements/goals, code not released or not tested, specifications not implemented, scrapped code, overutilized/underutilized resources, bureaucracy, suboptimal processes, unnecessary optimization, searching for information, mismanagement, task switching, improper work condition, confusion, to mention just the important activities associated to waste.

Through their elusive nature, independently on whether they are or not visible to stakeholders, they all impact the costs of projects and products when the proper attention is not given to them and not handled accordingly.

References:
[1] Mary Poppendieck & Tom Poppendieck (2003) Lean Software Development: An Agile Toolkit, Addison Wesley, ISBN: 0-321-15078-3

Data Science: Data Strategy (Part I: Big Data vs. Business Strategies)

A strategy, independently on whether applied to organizations, chess, and other situations, allows identifying the moves having the most promising results from a range of possible moves that can change as one progresses into the game. Typically, the moves compete for same or similar resources, each move having at the respective time a potential value expressed in quantitative and/or qualitative terms, while the values are dependent on the information available about one’s and partners’ positions into the game. Therefore, a strategy is dependent on the decision-making processes in place, the information available about own business, respective the concurrence, as well about the game.

Big data is not about a technology but an umbrella term for multiple technologies that support in handling data with high volume, veracity, velocity or variety. The technologies attempt helping organizations in harnessing what is known as Big data (data having the before mentioned characteristics), for example by allowing answering to business questions, gaining insight into the business or market, improving decision-making. Through this Big data helps delivering value to businesses, at least in theory.

Big-data technologies can harness all data of an organization though this doesn’t imply that all data can provide value, especially when considered in respect to the investments made. Data bring value when they have the potential of uncovering hidden trends or (special) patterns of behavior, when they can be associated in new meaningful ways. Data that don’t reflect such characteristics are less susceptible of bringing value for an organization no matter how much one tries to process the respective data. However, looking at the data through multiple techniques can help organization get a better understanding of the data, though here is more about the processes of attempting understanding the data than the potential associated directly with the data.

Through active effort in understanding the data one becomes aware of the impact the quality of data have on business decisions, on how the business and processes are reflected in its data, how data can be used to control processes and focus on what matters. These are aspects that can be corroborated with the use of simple BI capabilities and don’t necessarily require more complex capabilities or tools. Therefore allowing employees the time to analyze and play with the data, can in theory have a considerable impact on how data are harnessed within an organization.

If an organization’s decision-making processes is dependent on actual data and insight (e.g. stock market) then the organization is more likely to profit from it. In opposition, organizations whose decision-making processes hand handle hours, days or months of latency in their data, then more likely the technologies will bring little value. Probably can be found similar examples for veracity, variety or similar characteristics consider under Big data.

The Big data technologies can make a difference especially when the extreme aspects of their characteristics can be harnessed. One talks about potential use which is different than the actual use. The use of technologies doesn’t equate with results, as knowledge about the tools and the business is mandatory to harness the respective tools. For example, insight doesn’t necessarily imply improved decision-making because it relies on people’s understanding about the business, about the numbers and models used.

That’s maybe one of the reasons why organization fail in deriving value from Big data. It’s great that companies invest in their Big data, Analytics/BI infrastructures, though without working actively in integrating the new insights/knowledge and upgrading people’s skillset, the effects will be under expectations. Investing in employees’ skillset is maybe one of the important decisions an organization can make as part of its strategy.

Note:
Written as answer to a Medium post on Big data and business strategies. 

𖣯Strategic Management: Strategy Definition (Part II: Defining the Strategy)

In a previous post an organization’s strategy was defined as a set of coordinated and sustainable actions following a set of well-defined goals, actions devised into a plan and designed to create value and overcome an organization’s challenges. In what follows are described succinctly the components of the strategy.

A strategy’s definition should start with the identification of organization’s vision, where the organization wants to be in the future, its mission statement, a precise description of what an organization does in turning the vision from concept to reality, its values - traits and qualities that are considered as representative, and its principles – the guiding laws and truths for action. All these components have the purpose at defining at high-level the where (the vision), the why (the mission), the what (the core values) and by which means (the principles) of the strategy.

One of the next steps that can be followed in parallel is to take inventory of the available infrastructure: systems, processes, procedures, practices, policies, documentation, resources, roles and their responsibilities, KPIs and other metrics, ongoing projects and initiatives. Another step resumes in identifying the problems (challenges), risks and opportunities existing in the organization as part of a SWOT analysis adjusted to organization’s internal needs. One can extend the analysis to the market and geopolitical conditions and trends to identify further opportunities and risks. Within another step but not necessarily disconnected from the previous steps is devised where the organization could be once the problems, risks, threats and opportunities were addressed.

Then the gathered facts are divided into two perspectives – the “IS” perspective encompasses the problems together with the opportunities and threats existing in organization that define the status quo, while the “TO BE” perspective encompasses the wished state. A capability maturity model can be used to benchmark an organization’s current maturity in respect to industry practices, and, based on the wished capabilities, to identify organization’s future maturity.

Based on these the organization can start formulating its strategic goals – a set of long-range aims for a specific time-frame, from which are derived a (hierarchical) set of objectives, measurable steps an organization takes in order to achieve the goals. Each objective carries with it a rational, why the objective exists, an impact, how will the objective change the organization once achieved, and a target, how much of the objective needs to be achieved. In addition, one can link the objectives to form a set of hypothesis - predictive statements of cause and effect that involve approaches of dealing with the uncertainty. In order to pursue each objective are devised methods and means – the tactics (lines of action) that will be used to approach the various themes. It’s important to prioritize the tactics and differentiate between quick winners and long-term tactics, as well to define alternative lines of actions.

Then the tactics are augmented in a strategy plan (roadmap) that typically covers a minimum of 3 to 5 years with intermediate milestones. Following the financial cycles the strategy is split in yearly units for each objective being assigned intermediate targets. Linked to the plan are estimated the costs, effort and resources needed. Last but not the least are defined the roles, management and competency structures, with their responsibilities, competencies and proper level of authority, needed to support strategy’s implementation. Based on the set objectives are devised the KPIs used to measure the progress (success) and stir the strategy over its lifecycle.

By addressing all these aspects is created thus a first draft of the strategy that will need several iterations to mature, further changes deriving from the contact with the reality.

♟️Strategic Management: Values (Just the Quotes)

"The published objectives of a company will never reflect all the goals and values of the corporation as an institution or its management as human beings."(Richard Eells, California Management Review, 1959)

"The leadership and other processes of the organization must be such as to ensure a maximum probability that in all interactions and all interactions and all relationships with the organization each member will, in the light of his background, values, and expectations, view the experience as supportive and one which builds and maintains his sense of personal worth and importance." (Rensis Likert, "New patterns of management", 1961)

"Modern organization makes demands on the individual to learn something he has never been able to do before: to use organization intelligently, purposefully, deliberately, responsibly [...] to manage organization [...] to make [...] his job in it serve his ends, his values, his desire to achieve." (Peter F Drucker, The Age of Discontinuity, 1968)

"The advantages of having decisions made by groups are often lost because of powerful psychological pressures that arise when the members work closely together, share the same set of values and, above all, face a crisis situation that puts everyone under intense stress." (Irving Janis, "Victims of Groupthink", 1972) 

"Organizations tend to grow through stages, face and surmount crises, and along the way learn lessons and draw morals that shape values and future actions. Usually these developments influence assumptions and the way people behave. Often key episodes are recounted in 'war stories' that convey lessons about the firm's origins and transformations in dramatic form. Eventually, this lore provides a consistent background for action. New members are exposed to the common history and acquire insight into some of the subtle aspects of their company." (Richard T Pascale & Anthony G Athos, "The Art of Japanese Management", 1981)

"Organizational values are best transmitted when they are acted out, and not merely announced, by the people responsible for training, or by the people who become role-models for recruits. The manager of an organization is a role-model ex officio and may have an astonishing ability to communicate organizational values to recruits in fleeting contacts with them. That is the age-old secret of successful generalship, and it is applied every day by charismatic leaders in other fields, whose commitments to their roles is so dramatic that they strike awe into the recruits who observe them in action." (Theodore Caplow, "Managing an Organization", 1983)

"Someone adhering to the values of a corporate culture - an intelligent corporate citizen - will behave in consistent fashion under similar conditions, which means that managers don’t have to suffer the inefficiencies engendered by formal rules, procedures, and regulations. […] management has to develop and nurture the common set of values, objectives, and methods essential to the existence of trust. How do we do that? One way is by articulation, by spelling [them] out. […] The other even more important way is by example." (Andrew S Grove, "High Output Management", 1983)

"Change occurs only when there is a confluence of changing values and economic necessity." (John Naisbett & Patricia Aburdene, "Re-inventing the Corporation", 1985)

"A network is not a team. Nor is it a support system, which many women mistake it for. A man's network is the sum total of all those people with whom he barters. It is ever expanding among those of mutual interest and goals, not necessarily of mutual values and likes. They are the people with whom he does business, people who may join his team for some purpose, and others who may not." (Jinx Milea & Pauline Lyttle, "Why Jenny Can't Lead", 1986)

"Ethical pressures and decisions are viewed through the prism of one's own personal values. The distinction between personal and organizational values, however, often becomes blurred, especially the longer one stays with a particular organization and/or advances up the hierarchial ladder." (Warren H Schmidt & Barry Z Posner, Public Administration Review, 1986)

"The importance of top management commitment to organizational change is so well accepted that it is almost cliché to repeat the fact. We would therefore expect managerial values to be just as important in this area as in others that require strategic direction and leadership" (Thomas A Kochan,"The Mutual Gains Enterprise", 1994) 

"Values are social norms - they're personal, emotional, subjective, and arguable. All of us have values. [...] The question you must ask yourself is, Are your values based upon principles? In the last analysis, principles are natural laws - they're impersonal, factual, objective and self-evident. Consequences are governed by principles and behavior is governed by values; therefore, value principles." (Stephen R Covey, "The 8th Habit: From Effectiveness to Greatness", 2004)

"Clean code is not written by following a set of rules. You don’t become a software craftsman by learning a list of heuristics. Professionalism and craftsmanship come from values that drive disciplines." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Mental models are representations of reality built in people’s minds. These models are based on arrangements of assumptions, judgments, and values. A main weakness of mental models is that people’s assumptions and judgments change over time and are applied in inconsistent ways when building explanations of the world." (Luis F Luna-Reyes, "System Dynamics to Understand Public Information Technology", 2008)

"For values or guiding principles to be truly effective they have to be verbs. It's not 'integrity'," it's 'always do the right thing'. It's not 'innovation', it's 'look at the problem from a different angle'. Articulating our values as verbs gives us a clear idea - we have a clear idea of how to act in any situation." (Simon Sinek, "Start With Why: How Great Leaders Inspire Everyone to Take Action", 2009)

"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) 

♟️Strategic Management: Ethics (Just the Quotes)

"Neither by nature nor contrary to nature do the moral excellences arise in us, rather we are adapted by nature to receive them, and made perfect by habit." (Aristotle, "Nochomachean Ethics", cca. 340 BC)

"A low morality will not sustain leadership long, its influence quickly vanishes, it cannot produce its own succession." (Chester I Barnard, "The Functions of the Executive", 1938)

"One of the more disturbing aspects of this problem of moral conduct is the revelation that among so many influential people morality has become identified with legality. We are certainly in a tragic plight if the accepted standard by which we measure the integrity of a man in public life is that he keeps within the law." (Williarn Fulbright, [speech] 1967)

"The task of building an ethical environment where leaders and all personnel are instructed, encouraged, and rewarded for ethical behavior is a matter of first importance. All decisions, practices, goals, and values of the entire institutional structure which make ethical behavior difficult should be examined, beginning with the following: First, blatant or subtle forms of ethical relativism which blur the issue of what is right or wrong or which bury it as a subject of little or no importance. Second, the exaggerated loyalty syndrome, where people are afraid to tell the truth and are discouraged from it. Third, the obsession with image, where people are not even interested in the truth. And last, the drive for success, in which ethical sensitivity is bought off or sold because of the personal need to achieve." (Kermit D Johnson, "Ethical Issues of Military Leadership", 1974)

"Organizations tend to grow through stages, face and surmount crises, and along the way learn lessons and draw morals that shape values and future actions. Usually these developments influence assumptions and the way people behave. Often key episodes are recounted in 'war stories' that convey lessons about the firm's origins and transformations in dramatic form. Eventually, this lore provides a consistent background for action. New members are exposed to the common history and acquire insight into some of the subtle aspects of their company." (Richard T Pascale & Anthony G Athos, "The Art of Japanese Management", 1981)

"If managers are careless about basic things telling the truth, respecting moral codes, proper professional conduct - who can believe them on other issues?" (James L Hayes, "Memos for Management: Leadership", 1983)

"The key mission of contemporary management is to transcend the old models which limited the manager's role to that of controller, expert or morale booster. These roles do not produce the desired result of aligning the goals of the employees and the corporation. [...] These older models, vestiges of a bygone era, have served their function and must be replaced with a model of the manager as a developer of human resources." (Michael Durst, "Small Systems World", 1985)

"Ethical pressures and decisions are viewed through the prism of one's own personal values. The distinction between personal and organizational values, however, often becomes blurred, especially the longer one stays with a particular organization and/or advances up the hierarchical ladder." (Warren H Schmidt & Barry Z Posner, Public Administration Review, 1986)

"The practice of declaring codes of ethics and teaching them to managers is not enough to deter unethical conduct." (Saul W Gellerman, Harvard Business Review, 1986)

"Despite the codes of ethics, the ethics programs, and the special departments corporations don't make the ultimate decisions about ethics. Ethical choices are made by individuals." (M Euel Wade Jr., [speech] 1987)

"Executives have to start understanding that they have certain legal and ethical responsibilities for information under their control." (Jim Leeke, PC Week, 1987)

"The art of ethical management lies in unmixing the 'grey' areas to achieve clarity in resolution of ethical dilemmas." (Sheldon S Steinberg, "Workshop on Ethical Practices", 1987)

"Ethics must begin at the top of an organization. It is a leadership issue and the chief executive must set the example." (Edward L Hennessy Jr., The New York Times, 1988)

"As in the past, our service must rest upon a solid ethical base, because those who discharge such moral responsibilities must uphold and abide by the highest standards of behavior." (John A Wickham)

"Ethical and legal aren't the same. One can be dishonest, unprincipled, untrustworthy, unfair, and uncaring, without breaking the law." (Michael Josephson)

"For ethically committed persons, laws simply establish baseline standards of impropriety. Ultimately, these persons seek to do what is right in terms of universal moral principles such as honesty, integrity, loyalty, fairness, caring and respect for others, accountability and protection of the public trust. Laws cannot coerce these values." (Michael Josephson)

"On a practical level, there are two vital steps to ethical behavior: knowing what is right and doing it." (Michael Josephson)

♜Strategic Management: Value Stream Mapping [VSM] (Definitions)

"A process improvement tool that is used in lean manufacturing. The value stream map captures processes, material flows of a given product family and helps to identify waste in the system." (Bimal P Nepal & Leslie Monplaisir, "Lean and Global Product Development in Auto Industry", 2009)

"Value Stream Mapping (VSM) is one of the most known lean methods to analyze the current state of a production and to visualize and design a future state map. The VSM method is based on the use of specific symbols and icons for the visualization of the production sequences." (Dominik T Matt & Erwin Rauch, "Implementing Lean in Engineer-to-Order Manufacturing: Experiences from a ETO Manufacturer", 2014)

"A way to keep track of goods and material as they move through the product-creating process that helps a business boost productivity and reduces wastes." (Kijpokin Kasemsap, "Applying Lean Production and Six Sigma in Global Operations", 2016)

"A method to keep track of products and material as they move through the product-creating process that helps a business enhance productivity and reduce wastes." (Kijpokin Kasemsap, "Lean Thinking in Global Health Care: Theory and Applications", 2017)

"Value stream mapping is a technique used to analyze the flow of materials, services and information required to bring a service to a consumer." (Parminder Singh Kang et al, "Continuous Improvement Philosophy in Higher Education", 2020)

"Is a lean management technique for analyzing, designing, and managing the flow of data and information from an end-customer perspective to achieve value for them." (Anna Wiedemann et al, "Transforming Disciplined IT Functions: Guidelines for DevOps Integration", 2021)

"A pencil-and-paper tool used in two stages: a) Follow a product’s production path from beginning to end and draw a visual representation of every process in the material and information flows. b) Then draw a future state map of how value should flow. The most important map is the future state map." (Lean Enterprise Institute)

"Value stream mapping (VSM) is the process of charting out or visually displaying a value stream so that improvement activity can be effectively planned." (Gartner)

♜Strategic Management: Value Stream (Definitions)

"A value stream is a set of actions needed to bring a product to an organization's customers." (Andrew Pham et al, "From Business Strategy to Information Technology Roadmap", 2016)

"A value stream is a sequence of activities needed to design, produce and provide a specific service and along which information, material and value flow. A value chain is a set of linked activities that transform inputs into outputs that in turn add to at least one of the ecological, societal or economic bottom lines and help create competitive advantages. Linked to Six Sigma and Lean methodologies the goal is to create sustainable competitive advantages." (Rick Edgeman, "Lean and Six Sigma Innovation and Design", Encyclopedia of Information Science and Technology, Fourth Edition, 2018)

"Value Streams represent the series of steps that an organization uses to build Solutions that provide a continuous flow of value to a Customer. " (Dean Leffingwell, "SAFe 4.5 Reference Guide: Scaled Agile Framework for Lean Enterprises" 2nd Ed., 2018)

"A sequence of processes through which a product follows all the steps necessary for transformation and delivery to the customer." (Sorinel Căpușneanu et al, "Throughput Accounting: Decisional Informational Support for Optimizing Entity Profit", 2019)

"The set of all steps from the start of value creation until the delivery of the end result to customer." (Semra Birgün & Zeynep Altan, "A Managerial Perspective for the Software Development Process: Achieving Software Product Quality by the Theory of Constraints", 2019)

"All activities, both value added and non value added, required to bring a product from raw material into the hands of the customer, a customer requirement from order to delivery, and a design from concept to launch. Value stream improvement usually begins at the door-to-door level within a facility, and then expands outward to eventually encompass the full value stream." (Lean Enterprise Institute)

"An operating unit that controls one or more production flows." (Microsoft, "Dynamics for Finance and Operations Glossary")

"The value stream is defined as the specific activities within a supply chain required to design, order and provide a specific product or service." (Gartner)

♜Strategic Management: Value Proposition (Definitions)

"The benefit received for the investment made." (Janice M Roehl-Anderson, "IT Best Practices for Financial Managers", 2010)

"A three- to five-sentence statement that conveys to customers the value and benefits that a business brings to them. The value proposition should convey why the customer should purchase that business’s products and services over the competition’s." (Gina Abudi & Brandon Toropov, "The Complete Idiot's Guide to Best Practices for Small Business", 2011)

"The analysis of the benefits of using the specific model (tangible and intangible), including the customers' value proposition." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"The promise of value to be delivered by an organization. Typically addresses which customer needs the organization will meet and how it will price its offerings." (Andrew Pham et al, "From Business Strategy to Information Technology Roadmap", 2016)

"A statement about how customers will benefit from a product or service." (Duncan Angwin & Stephen Cummings, "The Strategy Pathfinder" 3rd Ed., 2017)

"A statement of the value that your product brings to your customer. The main reason that a customer should buy from you." (Pamela Schure & Brian Lawley, "Product Management For Dummies", 2017)

"A short statement (pre-project) that describes the tangible results or value a decision maker can expect from implementing a recommended course of action and its resulting benefit to the organization. It is expressed in a quantified fashion in the Business Case, where Value = Benefits – Cost (where Cost includes Risk). (See Business Case.) Vision Statement: It provides a view of the future desired state or condition of an organization. (A vision should stretch the organization to become the best that it can be.) The Vision Statement provides an effective tool to help develop objectives." (H James Harrington & William S Ruggles, "Project Management for Performance Improvement Teams", 2018)

"A statement that identifies clear, measurable, and demonstrable benefits consumers get when buying a particular product or service. It should convince consumers that this product or service is better than others on the market." (William Stallings, "Effective Cybersecurity: A Guide to Using Best Practices and Standards", 2018)

"The promise of value to be delivered by an organization. Typically addresses which customer needs the organization will meet and how it will price its offerings." (Tiffany Pham et al, "From Business Strategy to Information Technology Roadmap", 2018)

♜Strategic Management: Value Chain (Definitions)

"Sequence of processes that describe the movement of products or services through a pipeline from original creation to final sales." (Ralph Kimball & Margy Ross, "The Data Warehouse Toolkit 2nd Ed ", 2002)

"Framework for examining the strengths and weaknesses of an organization and for using the results of this analysis to improve performance." (Alan W Steiss, "Strategic Management for Public and Nonprofit Organizations", 2003)

"An end-to-end set of activities in support of customer needs, usually beginning with a customer request and ending with customer receipt of benefits." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Sequence of business processes in which value is added to a product or service. Encompasses customers and suppliers as well as, in some cases, the customers' customers and the suppliers' suppliers." (Leslie G Eldenburg & Susan K Wolcott, "Cost Management" 2nd Ed., 2011)

"A linked set of value-creating activities that begins with basic raw materials coming from suppliers and ends with distributors getting the final goods into the hands of the ultimate consumer." (Thomas L Wheelen & J David Hunger., "Strategic management and business policy: toward global sustainability 13th Ed.", 2012)

"Composed of all the stakeholders (designers, suppliers, manufacturers, customers, and others) who add value to or receive value from specific products or services." (Joan C Dessinger, "Fundamentals of Performance Improvement" 3rd Ed., 2012)

"The set of both primary and support activities or processes that an organization sets up to perform in order to achieve its mission and goals." (Andrew Pham et al, "From Business Strategy to Information Technology Roadmap", 2016)

"A value chain is a set of activities that an enterprise operating in a specific industry performs to deliver a valuable product or service for the market." (by Brian Johnson & Leon-Paul de Rouw, "Collaborative Business Design", 2017)

"The linked set of activities/functions within a firm that interact to enable the final value-creating offering (product/service) of the firm. At the industry level, it can also mean the total set of value-adding links from the first supplier to the final user of a product/service." (Duncan Angwin & Stephen Cummings, "The Strategy Pathfinder 3rd Ed.", 2017)

"A sequence of vertically related activities undertaken by a single firm or by a number of vertically related firms in order to produce a product or service." (Robert M Grant, "Contemporary Strategy Analysis" 10th Ed., 2018)

"A value chain is a set of linked activities that transform inputs into outputs that in turn add to at least one of the ecological, societal or economic bottom lines and help create competitive advantages." (Rick Edgeman, "Lean and Six Sigma Innovation and Design", Encyclopedia of Information Science and Technology" 4th Ed., 2018)

"sequence of processes that creates a product/service that is of value to a customer" (ITIL)

🌡️Performance Management: Competency (Definitions)

"An ability to perform business processes, which are supported by necessary available resources, practices, and activities, allowing the organization to offer products/services." (Jiri Hodík et al, "e-Cat for Partner Profiling and Competency Management Tool", 2008)

"Present or target capacity of a group or an individual to perform a cognitive, affective, social or psychomotor skill with regard to certain area of knowledge and in a specific context. The context consists in defining whether the skill can be attributed to the knowledge in a guided or autonomous way, in simple or complex, familiar or new situations, in a global or partial, persistent or sporadic manner." (Gilbert Paquette et al, "Principled Construction and Reuse of Learning Designs", Handbook of Research on Learning Design and Learning Objects: Issues, Applications, and Technologies, 2009)

"The ability to do something successfully or efficiently, often broken down into skills, knowledge, and attitude." (Alfonso Urquiza, "Competency Management Information Systems", 2009)

"The underlying characteristics of an individual (a motive, trait, skill, aspect of one’s self image or social role, or a body of knowledge) which underlie performance or behavior at work." (Jorge Valdés-Conca & Lourdes Canós-Darós, "B2E Relationships, Intranets, and Competency Management", 2009)

"A cluster of knowledges, understandings, skills, attitudes, values, and interests that are required for the performance of a function. In this case the function would be to be competent in counseling adult learners." (John A Henschke, "Counseling in an Andragogical Approach", 2012)

"A specific, identifiable, definable, and measurable knowledge, skill, ability, and/or other deployment-related characteristic (e.g., attitude, behavior, physical ability) which a human resource may possess and which is necessary for, or material to, the performance of an activity within a specific business context." Nancy B Hastings & Karen L Rasmussen, "Designing and Developing Competency-Based Education Courses Using Standards", 2017)

"Competency is the ability to demonstrate a specified level of knowledge or skill." (Christine K S Irvine & Jonathan M Kevan, "Competency-Based Education in Higher Education", 2017)

"Expected capacity the learner should build to be successful in his/her career. Competency is written in broader terms and are not directly measurable." (Devrim Ozdemir & Carla Stebbins, "A Framework for the Evaluation of Competency-Based Curriculum", 2017)

"Competencies are specific knowledge-based skills, abilities, or expertise in a subject area. When these skillsets are shared across a profession, they are said to have core competencies." (Valerie A Storey et al, "Developing a Clinical Leadership Pipeline: Planning, Operation, and Sustainability", 2019)

"Multidimensional construct which represents what a person is capable of doing. It includes knowledge, skills, experience, abilities, values, attitudes, personality traits, among others." (Geraldina Silveyra et al, "Proposal of a Comprehensive Model of Teachable Entrepreneurship Competencies (M-TEC): Literature Review and Theoretical Foundations", 2019)

"The ability to act successfully on the basis of practical experience, skill, and knowledge in solving professional problems. Is understood as a formal system characteristic, which is described as a set of requirements for the knowledge, skills and qualities of the employee for a function, position or role in the organization." (Vitaly V Martynov et al, "CSRP: System Design Technology of Training Information Support of Competent Professionals", 2019)

"A guiding tool including knowledge, abilities, distinguished personal attributes, and behaviours for higher performance contributing to achieving strategic goals of the company." (Mustafa K Topcu, "Competency Framework for the Fourth Industrial Revolution", 2020)

"Proficiency or mastery of identified knowledge, skills or abilities." (Ernst Jan van Weperen et al, "Sustainable Entrepreneurial Thinking: Developing Pro-Active, Globally Aware Citizens", 2020)

"Capacity to perform something in an effective manner. Involves individual attitudes, knowledge, and skills necessary, and behaviors." (Christiane Molina, "Management Education for a Sustainable World: Aiming for More Than Business as Usual", 2021)

"Competency refers to observable and measurable skills that integrate the knowledge, skills, values, and attitudes required of a professional in the practice of his specialty." (Maria M P Calimag, "The ePortfolio: Technology-Enhanced Authentic Assessment in the Continuum of Medical Education", 2021)

"The sum of knowledge, skills, values, attitudes, and individual characteristics that enable a person to perform actions successfully." (Almudena Eizaguirre et al, "A Methodological Proposal to Analyse the Process for Implementing Competency-Based Learning (CBL) in a Business School", 2021)

📉Graphical Representation: Values (Just the Quotes)

"By [diagrams] it is possible to present at a glance all the facts which could be obtained from figures as to the increase, fluctuations, and relative importance of prices, quantities, and values of different classes of goods and trade with various countries; while the sharp irregularities of the curves give emphasis to the disturbing causes which produce any striking change." (Arthur L Bowley, "A Short Account of England's Foreign Trade in the Nineteenth Century, its Economic and Social Results", 1905)

"To summarize - with the ordinary arithmetical scale, fluctuations in large factors are very noticeable, while relatively greater fluctuations in smaller factors are barely apparent. The logarithmic scale permits the graphic representation of changes in every quantity without respect to the magnitude of the quantity itself. At the same time, the logarithmic scale shows the actual value by reference to the numbers in the vertical scale. By indicating both absolute and relative values and changes, the logarithmic scale combines the advantages of both the natural and the percentage scale without the disadvantages of either." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919)

"With the ordinary scale, fluctuations in large factors are very noticeable, while relatively greater fluctuations in smaller factors are barely apparent. The semi-logarithmic scale permits the graphic representation of changes in every quantity on the same basis, without respect to the magnitude of the quantity itself. At the same time, it shows the actual value by reference to the numbers in the scale column. By indicating both absolute and relative value and changes to one scale, it combines the advantages of both the natural and percentage scale, without the disadvantages of either." (Allan C Haskell, "How to Make and Use Graphic Charts", 1919)

"An important rule in the drafting of curve charts is that the amount scale should begin at zero. In comparisons of size the omission of the zero base, unless clearly indicated, is likely to give a misleading impression of the relative values and trend." (Rufus R Lutz, "Graphic Presentation Simplified", 1949)

"The function of the regression lines, as approximate representations of means of arrays, is to isolate the mean value of one variable corresponding to any given value of the other; the variation of the first variable about its mean is ignored. A regression line is an average relation, and with it there is a variation of values about the average. In the regression of y on x, the variation ignored is in the vertical direction, a variation of y up and down about the line." (Roy D G Allen, "Statistics for Economists", 1951)

"First, it is generally inadvisable to attempt to portray a series of more than four or five categories by means of pie charts. If, for example, there are six, eight, or more categories, it may be very confusing to differentiate the relative values portrayed, especially if several small sectors are of approximately the same size. Second, the pie chart may lose its effectiveness if an attempt is made to compare the component values of several circles, as might be found in a temporal or geographical series. In such case the one-hundred percent bar or column chart is more appropriate. Third, although the proportionate values portrayed in a pie chart are measured as distances along arcs about the circle, actually there is a tendency to estimate values in terms of areas of sectors or by the size of subtended angles at the center of the circle." (Calvin F Schmid, "Handbook of Graphic Presentation", 1954)

"The primary purpose of a graph is to show diagrammatically how the values of one of two linked variables change with those of the other. One of the most useful applications of the graph occurs in connection with the representation of statistical data." (John F Kenney & E S Keeping, "Mathematics of Statistics" Vol. I 3rd Ed., 1954)

"Where the values of a series are such that a large part the grid would be superfluous, it is the practice to break the grid thus eliminating the unused portion of the scale, but at the same time indicating the zero line. Failure to include zero in the vertical scale is a very common omission which distorts the data and gives an erroneous visual impression." (Calvin F Schmid, "Handbook of Graphic Presentation", 1954)

"In line charts the grid structure plays a controlling role in interpreting facts. The number of vertical rulings should be sufficient to indicate the frequency of the plottings, facilitate the reading of the time values on the horizontal scale. and indicate the interval or subdivision of time." (Anna C Rogers, "Graphic Charts Handbook", 1961)

"To be useful data must be consistent - they must reflect periodic recordings of the value of the variable or at least possess logical internal connections. The definition of the variable under consideration cannot change during the period of measurement or enumeration. Also. if the data are to be valuable, they must be relevant to the question to be answered." (Cecil H Meyers, "Handbook of Basic Graphs: A modern approach", 1970)

"Missing data values pose a particularly sticky problem for symbols. For instance, if the ray corresponding to a missing value is simply left off of a star symbol, the result will be almost indistinguishable from a minimum (i.e., an extreme) value. It may be better either (i) to impute a value, perhaps a median for that variable, or a fitted value from some regression on other variables, (ii) to indicate that the value is missing, possibly with a dashed line, or (iii) not to draw the symbol for a particular observation if any value is missing." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

"[...] error bars are more effectively portrayed on dot charts than on bar charts. […] On the bar chart the upper values of the intervals stand out well, but the lower values are visually deemphasized and are not as well perceived as a result of being embedded in the bars. This deemphasis does not occur on the dot chart." (William S. Cleveland, "Graphical Methods for Data Presentation: Full Scale Breaks, Dot Charts, and Multibased Logging", The American Statistician Vol. 38 (4) 1984)

"The logarithm is an extremely powerful and useful tool for graphical data presentation. One reason is that logarithms turn ratios into differences, and for many sets of data, it is natural to think in terms of ratios. […] Another reason for the power of logarithms is resolution. Data that are amounts or counts are often very skewed to the right; on graphs of such data, there are a few large values that take up most of the scale and the majority of the points are squashed into a small region of the scale with no resolution." (William S. Cleveland, "Graphical Methods for Data Presentation: Full Scale Breaks, Dot Charts, and Multibased Logging", The American Statistician Vol. 38 (4) 1984)

"It is common for positive data to be skewed to the right: some values bunch together at the low end of the scale and others trail off to the high end with increasing gaps between the values as they get higher. Such data can cause severe resolution problems on graphs, and the common remedy is to take logarithms. Indeed, it is the frequent success of this remedy that partly accounts for the large use of logarithms in graphical data display." (William S Cleveland, "The Elements of Graphing Data", 1985)

"Use a reference line when there is an important value that must be seen across the entire graph, but do not let the line interfere with the data." (William S Cleveland, "The Elements of Graphing Data", 1985)

"Scatter charts show the relationships between information, plotted as points on a grid. These groupings can portray general features of the source data, and are useful for showing where correlationships occur frequently. Some scatter charts connect points of equal value to produce areas within the grid which consist of similar features." (Bruce Robertson, "How to Draw Charts & Diagrams", 1988)

"A coordinate is a number or value used to locate a point with respect to a reference point, line, or plane. Generally the reference is zero. […] The major function of coordinates is to provide a method for encoding information on charts, graphs, and maps in such a way that viewers can accurately decode the information after the graph or map has been generated."  (Robert L Harris, "Information Graphics: A Comprehensive Illustrated Reference", 1996) 

"Area graphs are generally not used to convey specific values. Instead, they are most frequently used to show trends and relationships, to identify and/or add emphasis to specific information by virtue of the boldness of the shading or color, or to show parts-of-the-whole." (Robert L Harris, "Information Graphics: A Comprehensive Illustrated Reference", 1996) 

"Grouped area graphs sometimes cause confusion because the viewer cannot determine whether the areas for the data series extend down to the zero axis. […] Grouped area graphs can handle negative values somewhat better than stacked area graphs but they still have the problem of all or portions of data curves being hidden by the data series towards the front." (Robert L Harris, "Information Graphics: A Comprehensive Illustrated Reference", 1996)

"If you want to show the growth of numbers which tend to grow by percentages, plot them on a logarithmic vertical scale. When plotted against a logarithmic vertical axis, equal percentage changes take up equal distances on the vertical axis. Thus, a constant annual percentage rate of change will plot as a straight line. The vertical scale on a logarithmic chart does not start at zero, as it shows the ratio of values (in this case, land values), and dividing by zero is impossible." (Herbert F Spirer et al, "Misused Statistics" 2nd Ed, 1998)

"Estimating the missing values in a dataset solves one problem - imputing reasonable values that have well-defined statistical properties. It fails to solve another, however - drawing inferences about parameters in a model fit to the estimated data. Treating imputed values as if they were known (like the rest of the observed data) causes confidence intervals to be too narrow and tends to bias other estimates that depend on the variability of the imputed values (such as correlations)." (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"Use a logarithmic scale when it is important to understand percent change or multiplicative factors. […] Showing data on a logarithmic scale can cure skewness toward large values." (Naomi B Robbins, "Creating More effective Graphs", 2005)

"A useful feature of a stem plot is that the values maintain their natural order, while at the same time they are laid out in a way that emphasizes the overall distribution of where the values are concentrated (that is, where the longer branches are). This enables you easily to pick out key values such as the median and quartiles." (Alan Graham, "Developing Thinking in Statistics", 2006)

"Tables work in a variety of situations because they convey large amounts of data in a condensed fashion. Use tables in the following situations: (1) to structure data so the reader can easily pick out the information desired, (2) to display in a chart when the data contains too many variables or values, and (3) to display exact values that are more important than a visual moment in time." (Dennis K Lieu & Sheryl Sorby, "Visualization, Modeling, and Graphics for Engineering Design", 2009)

"Given the important role that correlation plays in structural equation modeling, we need to understand the factors that affect establishing relationships among multivariable data points. The key factors are the level of measurement, restriction of range in data values (variability, skewness, kurtosis), missing data, nonlinearity, outliers, correction for attenuation, and issues related to sampling variation, confidence intervals, effect size, significance, sample size, and power." (Randall E Schumacker & Richard G Lomax, "A Beginner’s Guide to Structural Equation Modeling" 3rd Ed., 2010)

"The biggest difference between line graphs and sparklines is that a sparkline is compact with no grid lines. It isnʼt meant to give precise values; rather, it should be considered just like any other word in the sentence. Its general shape acts as another term and lends additional meaning in its context. The driving forces behind these compact sparklines are speed and convenience." (Brian Suda, "A Practical Guide to Designing with Data", 2010)

"Color can modify - and possibly even contradict - our intuitive response to value, because of its own powerful connotations." (Joel Katz, "Designing Information: Human factors and common sense in information design", 2012)

"Histograms are often mistaken for bar charts but there are important differences. Histograms show distribution through the frequency of quantitative values (y axis) against defined intervals of quantitative values (x axis). By contrast, bar charts facilitate comparison of categorical values. One of the distinguishing features of a histogram is the lack of gaps between the bars [...]" (Andy Kirk, "Data Visualization: A successful design process", 2012)

"After you visualize your data, there are certain things to look for […]: increasing, decreasing, outliers, or some mix, and of course, be sure you’re not mixing up noise for patterns. Also note how much of a change there is and how prominent the patterns are. How does the difference compare to the randomness in the data? Observations can stand out because of human or mechanical error, because of the uncertainty of estimated values, or because there was a person or thing that stood out from the rest. You should know which it is." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"Upon discovering a visual image, the brain analyzes it in terms of primitive shapes and colors. Next, unity contours and connections are formed. As well, distinct variations are segmented. Finally, the mind attracts active attention to the significant things it found. That process is permanently running to react to similarities and dissimilarities in shapes, positions, rhythms, colors, and behavior. It can reveal patterns and pattern-violations among the hundreds of data values. That natural ability is the most important thing used in diagramming." (Vasily Pantyukhin, "Principles of Design Diagramming", 2015)

"A scatterplot reveals the strength and shape of the relationship between a pair of variables. A scatterplot represents the two variables by axes drawn at right angles to each other, showing the observations as a cloud of points, each point located according to its values on the two variables. Various lines can be added to the plot to help guide our search for understanding." (Forrest W Young et al, "Visual Statistics: Seeing data with dynamic interactive graphics", 2016)

"The simplest and most common way to represent the empirical distribution of a numerical variable is by showing the individual values as dots arranged along a line. The main difficulty with this plot concerns how to treat tied values. We usually don't want to represent them by the same point, since that means that the two values look like one. What we can do is 'jitter' the points a bit (i.e., move them back and forth at right angles to the plot axis) so that all points are visible. […] In addition to permitting you to identify individual points, dotplots allow you to look into some of the distributional properties of a variable. […] Dotplots can also be good for looking for modality. " (Forrest W Young et al, "Visual Statistics: Seeing data with dynamic interactive graphics", 2016)

"The most accurate but least interpretable form of data presentation is to make a table, showing every single value. But it is difficult or impossible for most people to detect patterns and trends in such data, and so we rely on graphs and charts. Graphs come in two broad types: Either they represent every data point visually (as in a scatter plot) or they implement a form of data reduction in which we summarize the data, looking, for example, only at means or medians." (Daniel J Levitin, "Weaponized Lies", 2017)

"A time series is a sequence of values, usually taken in equally spaced intervals. […] Essentially, anything with a time dimension, measured in regular intervals, can be used for time series analysis." (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018)

"Data is dirty. Let's just get that out there. How is it dirty? In all sorts of ways. Misspelled text values, date format problems, mismatching units, missing values, null values, incompatible geospatial coordinate formats, the list goes on and on." (Ben Jones, "Avoiding Data Pitfalls: How to Steer Clear of Common Blunders When Working with Data and Presenting Analysis and Visualizations", 2020) 

"Another cardinal sin of data visualization is what is called 'breaking the bar' - that is, using a squiggly line or shape to show that you've cropped one or more of the bars. It's tempting to do this when you have an outlier, but it distorts the relative values between the bars." (Jonathan Schwabish, "Better Data Visualizations: A guide for scholars, researchers, and wonks", 2021)

🖌️Stephen Covey - Collected Quotes

"Habit is the intersection of knowledge (what to do), skill (how to do), and desire (want to do)." (Stephen R Covey & Warren Bennis, "The Seven Habits of Highly Effective People", 1989)

"Management is doing things right; leadership is doing the right things." (Stephen R Covey & Warren Bennis, "The Seven Habits of Highly Effective People", 1989)

"Our behavior is a function of our decisions, not our conditions." (Stephen R Covey & Warren Bennis, "The Seven Habits of Highly Effective People", 1989)

"We see the world, not as it is, but as we are - or, as we are conditioned to see it." (Stephen R Covey & Warren Bennis, "The Seven Habits of Highly Effective People", 1989)

"In effective personal leadership, visualization and affirmation techniques emerge naturally out of a foundation of well thought through purposes and principles that become the center of a person's life." (Stephen Covey, "Daily Reflections for Highly Effective People", 1994)

"Management is clearly different from leadership. Leadership is primarily a high-powered, right-brain activity. It's more of an art it's based on a philosophy. You have to ask the ultimate questions of life when you're dealing with personal leadership issues. (Stephen Covey, "Daily Reflections for Highly Effective People", 1994)

"You basically get what you reward. If you want to achieve the goals and reflect the values in your mission statement, then you need to align the reward system with these goals and values." (Stephen Covey, "The 7 Habits of Highly Effective People Personal Workbook", 2000)

"Values are social norms - they're personal, emotional, subjective, and arguable. All of us have values. [...] The question you must ask yourself is, Are your values based upon principles? In the last analysis, principles are natural laws - they're impersonal, factual, objective and self-evident. Consequences are governed by principles and behavior is governed by values; therefore, value principles." (Stephen R Covey, "The 8th Habit: From Effectiveness to Greatness", 2004)

"Effective leadership is putting first things first. Effective management is discipline, carrying it out." (Stephen Covey)

"Management is efficiency in climbing the ladder of success; leadership determines whether the ladder is leaning against the right wall." (Stephen R Covey)

"Management works in the system; Leadership works on the system." (Stephen R. Covey)