Business Intelligence: Mea Culpa (Part III: Problem Solving)

Business Intelligence Series

I've been working for more than 20 years in BI and Data Analytics area, in combination with Software Engineering, ERP implementations, Project Management, IT services and several other areas, which allowed me to look at many recurring problems from different perspectives. One of the things I learnt is that problems are more complex and more dynamic than they seem, respectively that they may require tailored dynamic solutions. Unfortunately, people usually focus on one or two immediate perspectives, ignoring the dynamics and the multilayered character of the problems!

Sometimes, a quick fix and limited perspective is what we need to get started and fix the symptoms, and problem-solvers usually stop there. When left unsupervised, the problems tend to kick back, build up momentum and appear under more complex forms in various places. Moreover, the symptoms can remain hidden until is too late. To this also adds the political agendas and the further limitations existing in organizations (people, money, know-how, etc.).

It seems much easier to involve external people (individual experts, consultancy companies) to solve the problem(s), though unless they get a deep understanding of the business and the issues existing in it, the chances are high that they solve the wrong problems and/or implement the wrong solutions. Therefore, it's more advisable to have internal experts, when feasible, and that's the point where business people with technical expertise and/or IT people with business expertise can help. Ideally, one should have a good mix and the so called competency centers can do a great job in handling the challenges of organizations. 

Between business and IT people there's a gap that can be higher or lower depending on resources know-how or the effort made by organizations to reduce it. To this adds the nature of the issues existing in organizations, which can vary considerable across departments, organizations or any other form of establishment. Conversely, the specific skillset can be transmuted where needed, which might happen naturally, though upon case also considerable effort needs to be involved in the process.

Being involved in similar tasks, one may get the impression that one can do whatever the others can do. This can happen in IT as well on the business side. There can be activities that can be done by parties from the other group, though there are also many exceptions in both directions, especially when one considers that one can’t generalize the applicability and/or transmutation of skillset. 

A more concrete example is the know-how needed by a businessperson to use the BI infrastructure for answering business questions, and ideally for doing all or at least most of the activities a BI professional can do. Ideally, as part of the learning path, it would be helpful to have a pursuable path in between the two points. The mastery of tools helps in the process though there are different mindsets involved.

Unfortunately, the data-related fields are full of overconfident people who get the problem-solving process wrong. Data-based problem-solving resumes in gathering the right facts and data, building the right conceptual model, identifying the right questions to ask, collecting more data, refining methods and solutions, etc. There’s aways an easy wrong way to solve a problem!

The mastery of tools doesn’t imply the mastery of business domains! What people from the business side can bring is deeper insight in the business problems, though getting from there to implementing solutions can prove a long way, especially when problems require different approaches, different levels of approximations, etc. No tool alone can bridge such gaps yet! Frankly, this is the most difficult to learn and unfortunately many data professionals seem to get this wrong!

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Business Intelligence: Data Culture (Part II: Leadership, Necessary but not Sufficient)

Business Intelligence Series

Continuing the idea from the previous post on Brent Dykes’ article on data culture and Generative AI [1], it’s worth discussing about the relationship between data culture and leadership. Leadership belongs to a list of select words everybody knows about but fails to define them precisely, especially when many traits are associated with leadership, respectively when most of the issues existing in organizations ca be associated with it directly or indirectly.

Take for example McKinsey’s definition: "Leadership is a set of behaviors used to help people align their collective direction, to execute strategic plans, and to continually renew an organization." [2] It gives an idea of what leadership is about, though it lacks precision, which frankly is difficult to accomplish. Using modifiers like strong or weak with the word leadership doesn’t increase the precision of its usage. Several words stand out though: direction, strategy, behavior, alignment, renewal.

Leadership is about identifying and challenging the status quo, defining how the future will or could look like for the organization in terms of a vision, a mission and a destination, translating them into a set of goals and objectives. Then, it’s about defining a set of strategies, focusing on transformation and what it takes to execute it, adjusting the strategic bridge between goals and objectives, or, reading between the lines, identifying and doing the right things, being able to introduce a new order of things, reinventing the organization, adapting the organization to circumstances.

Aligning resumes in aligning the various strategies, aligning people with the vision and mission, while renewal is about changing course in response to new information or business context, identifying and transforming weaknesses into strengths, risks into opportunities, respectively opportunities into certitudes, seeing possibilities and multiplying them.

Leadership is also about working on the system, addressing the systemic failure, addressing structural and organizational issues, making sure that the preconditions and enablers for organizational change are in place, that no barriers exist or other factors impact negatively the change, that the positive aspects of complex systems like emergence or exponential growth do happen in time.

And leadership is about much more - interpersonal influence, inspiring people, Inspiring change, changing mindsets, assisting, motivating, mobilizing, connecting, knocking people out of their comfort zones, conviction, consistency, authority, competence, wisdom, etc. Leadership seems to be an idealistic concept where too many traits are considered, traits that ideally should apply to the average knowledge worker as well.

An organization’s culture is created, managed, nourished, and destroyed through leadership, and that’s a strong statement and constraint. By extension this statement applies to the data culture as well. It’s about leading by example and not by words or preaching, and many love to preach, even when no quire is around. It’s about demanding the same from the managers as managers demand from their subalterns, it’s about pushing the edges of culture. As Dykes mentions, it should be about participating in the data culture initiatives, making expectations explicit, and sharing mental models.

Leadership is a condition necessary but not sufficient for an organizations culture to mature. Financial and other type of resources are needed, though once a set of behaviors is seeded, they have the potential to grow and multiply when the proper conditions are met. Growth occurs also by being aware of what needs to be done and doing it day by day consciously, through self-mastery. Nowadays there are so many ways to learn and search for support, one just needs a bit of curiosity and drive to learn anything. Blaming in general the lack of leadership is just a way of passing the blame one level above on the command chain.

Resources:
[1] Forbes (2024) Why AI Isn’t Going To Solve All Your Data Culture Problems, by Brent Dykes (link)
[2] McKinsey (2022) What is leadership? (link)

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Performance Management: First Time Right (The Aim toward Operational Excellence)

 

Rooted in Six Sigma methodology as a step toward operational excellence, First Time Right (FTR) implies that any procedure is performed in the right manner the first time and every time. It equates to minimizing the waste in its various forms (inventory, motion, overprocessing, overproduction, waiting, transportation, defects). Like many quality concepts from the manufacturing industry, the concept was transported in the software development process as principle, process, goal and/or metric. Thus, it became part of Software Engineering, Project Management, Data Science, and any other similar endeavors whose outcome results in software products. 

Besides the quality aspect, FTR is rooted also in the economic imperative – the need to achieve something in the minimum amount of time with the minimum of effort. It’s about being efficient in delivering a product or achieving a given target. It can be associated with continuous improvement, learning and mastery, the aim being to encompass FTR as part of organization’s culture. 

Even if not explicitly declared, FTR lurks in each task planned. It seems that it became common practice to plan with the FTR in mind, however between this theoretical aim and practice there’s as usual an important gap. Unfortunately, planners, managers and even tasks' performers often forget that mistakes are made, that several iterations are needed to get the job done. It starts with the communication between people in clarifying the requirements and ends with the formal sign off. All the deviations from the FTR add up in the deviations between expected and actual effort, though probably more important are the deviations from the plan and all the consequences deriving from it. Especially in complex projects this adds up into a spiral of issues that can easily reinforce themselves. 

Many of the jobs that imply creativity, innovation, research or exploration require at least several iterations to get the job done and this is independent of participants’ professionalism and experience. Moreover, the more quality one needs, the higher the effort, the 80/20 being sometimes a good approximation of the effort needed. In extremis, aiming for perfection instead of excellence can make certain tasks a never-ending story. 

Achieving FTR requires practice - the more novelty, the higher the complexity, the communication or the synchronization needs, the more practice is needed. It starts with the individual to master the individual tasks and ends with the team, where communication, synchronization and other aspects need to be considered. The practice is usually achieved on hands-on work as part of the daily duties, project work, and so on. Unfortunately, it’s based primarily on individual experience, and seldom groomed in advance, as preparation for future tasks. That’s why sometimes when efficiency is needed in performing critical complex tasks, one also needs to consider the learning curve in achieving the required quality. 

Of course, many organizations demand from job applicants experience and, when possible, they hire people with experience, however the diversity, complexity and changing nature of tasks require further practice. This aspect is somehow recognized in the implementation in organizations of the various forms of DevOps, though how many organizations adopt it and enforce it on a regular basis? Moreover, a major requirement of nowadays businesses is to be agile, and besides the mere application of methodologies, being agile means to have also a FTR mindset. 

FTR starts with the wish for mastery at individual and team level and, with the right management attention, by allocating time for learning, self-development in the important areas, providing relevant feedback and building an infrastructure for knowledge sharing and harnessing, FTR can become part of organization’s culture. It’s up to each of us to do it!

Business Intelligence: Power BI’s Learning Curve - Part I

A learning curve attempts depicting the (average) time it takes a person to learn how to use a method, tool, or technique, tracing the path from newbie to mastery. A common definition of the learning curve is based on the correlation between a learner’s performance on a task or activity and the number of attempts or amount of time required to complete it.

There are several diagrams in circulation which depict the correlation between the difficulty of Power BI concepts and probably their implementation as functionality. Even if they reflect to some degree the rate of learning, their simplicity and fuzziness can easily make one question their accuracy in reflecting the reality.

Researchers tend to categorize the curves associated with the learning process in simple idealized patterns like S-curve (aka sigmoid), exponential growth, exponential rise and fall to limit, or power law, however the learning process in IT-based endeavors is seldom characterized by a linear or exponential curve, given that the tasks seldom allow a steady path. The jumps of knowledge between tasks can be wide enough to appear insurmountable, and they can prove to be quite of a challenge without some help.

Like a baby’s first steps, we, as learners, must learn first to crawl, before making some unsteady steps, and it can take long time until visible progress is made. It’s a slow progress until we suddenly hit a (tipping) point from which everything seems easy, fact that increases our confidence in us. On the other side, when we find that we make no visible progress for a long period, it’s easy to arrive to the opposite, a critical zone, which in extremis could make one lose interest.

As beginners, after the first tipping point on the learning journey, it’s easy to arrive at a plateau in which there seem no need to learn new things, the current knowledge allowing to handle a range of tasks of small to average complexity. This can last for a long time, and then, a big thing comes our way – a hard problem to solve or a concept hard to understand. It’s the point where we stagnate, and the deeper we go, and the more such challenges are thrown in our way, the more difficult the learning seems to be. However, with new understanding, small steps are made, one step after the other, the pace makes us to evolve faster until we reach again a critical point from which the process increases smoothly until we seem to stagnate again. We meet again a hard limit to growth, which seems to be more solid than the previous one.

Both limits to growth can appear to be hard, however, considering that the knowledge in the field expands, more opportunity for growth appear, thus, the limits are apparent. Even if knowledge tends to increase ‘indefinitely’, the limits are there in terms of complexity, time available, knowledge quality (incl. availability) or any other dimension of the learning process. Moreover, these successions of tipping points, growth limits, plateaus, critical, steady and fast progress zones can occur in several iterations in the learning path. Thus, the path seems to resemble a snakelike curve with many ups and downs.

For the learner is important to be aware of this last aspect, there are always ups and downs, taking effort, patience and maybe an expert’s help to bridge the gaps in between. The chances are high that the gap between what we think we know and what we know is considerable, therefore a reality check is useful from time to time. A new problem to tackle will provide that occasion!

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Business Intelligence: New Technologies, Old Challenges I (Introduction)

Each important technology has the potential of creating divides between the specialists from a given field. This aspect is more suggestive in the data-driven fields like BI/Analytics or Data Warehousing. The data professionals (engineers, scientists, analysts, developers) skilled only in the new wave of technologies tend to disregard the role played by the former technologies and their role in the data landscape. The argumentation for such behavior is rooted in the belief that a new technology is better and can solve any problem better than previous technologies did. It’s a kind of mirage professionals and customers can easily fall under.

Being bigger, faster, having new functionality, doesn’t make a tool the best choice by default. The choice must be rooted in the problem to be solved and the set of requirements it comes with. Just because a vibratory rammer is a new technology, is faster and has more power in applying pressure, this doesn’t mean that it will replace a hammer. Where a certain type of power is needed the vibratory rammer might be the best tool, while for situations in which a minimum of power and probably more precision is needed, like driving in a nail, then an adequately sized hammer will prove to be a better choice.

A technology is to be used in certain (business/technological) contexts, and even if contexts often overlap, the further details (aka requirements) should lead to the proper use of tools. It’s in a professional’s duties to be able to differentiate between contexts, requirements and the capabilities of the tools appropriate for each context. In this resides partially a professional’s mastery over its field of work and of providing adequate solutions for customers’ needs. Especially in IT, it’s not enough to master the new tools but also have an understanding about preceding tools, usage contexts, capabilities and challenges.

From an historical perspective each tool appeared to fill a demand, and even if maybe it didn’t manage to fill it adequately, the experience obtained can prove to be valuable in one way or another. Otherwise, one risks reinventing the wheel, or more dangerously, repeating the failures of the past. Each new technology seems to provide a deja-vu from this perspective.

Moreover, a new technology provides new opportunities and requires maybe to change our way of thinking in respect to how the technology is used and the processes or techniques associated with it. Knowledge of the past technologies help identifying such opportunities easier. How a tool is used is also a matter of skills, while its appropriate use and adoption implies an inherent learning curve. Having previous experience with similar tools tends to reduce the learning curve considerably, though hands-on learning is still necessary, and appropriate learning materials or tutoring is upon case needed for a smoother transition.

In what concerns the implementation of mature technologies, most of the challenges were seldom the technologies themselves but of non-technical nature, ranging from the poor understanding/knowledge about the tools, their role and the implications they have for an organization, to an organization’s maturity in leading projects. Even the most-advanced technology can fail in the hands of non-experts. Experience can’t be judged based only on the years spent in the field or the number of projects one worked on, but on the understanding acquired about implementation and usage’s challenges. These latter aspects seem to be widely ignored, even if it can make the difference between success and failure in a technology’s implementation.

Ultimately, each technology is appropriate in certain contexts and a new technology doesn’t necessarily make another obsolete, at least not until the old contexts become obsolete.

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Project Management: The Choice of Tools in Project Management

“Beware the man of one book” (in Latin, “homo unius libri”), a warning generally attributed to Thomas Aquinas and having a twofold meaning. In its original interpretation it was referring to the people mastering a single chosen discipline, however the meaning degenerated in expressing the limitations of people who master just one book, and thus having a limited toolset of perspectives, mental models or heuristics. This later meaning is better reflected in Abraham Maslow adage: “If the only tool you have is a hammer, you tend to see every problem as a nail”, as people tend to use the tools they are used to also in situations in which other tools are more appropriate.

It’s sometimes admirable people and even organizations’ stubbornness in using the same tools in totally different scenarios, expecting though the same results, as well in similar scenarios expecting different results. It’s true, Mathematics has proven that the same techniques can be used successfully in different areas, however a mathematician’s universe and models are idealistically fractionalized to a certain degree from reality, full of simplified patterns and never-ending approximations. In contrast, the universe of Software Development and Project Management has a texture of complex patterns with multiple levels of dependencies and constraints, constraints highly sensitive to the initial conditions.

Project Management has managed to successfully derive tools like methodologies, processes, procedures, best practices and guidelines to address the realities of projects, however their use in praxis seems to be quite challenging. Probably, the challenge resides in stubbornness of not adapting the tools to the difficulties and tasks met. Even if the same phases and multiple similarities seems to exist, the process of building a house or other tangible artefact is quite different than the approaches used in development and implementation of software.

Software projects have high variability and are often explorative in nature. The end-product looks totally different than the initial scaffold. The technologies used come with opportunities and limitations that are difficult to predict in the planning phase. What on paper seems to work often doesn’t work in praxis as the devil lies typically in details. The challenges and limitations vary between industries, businesses and even projects within the same organization.

Even if for each project type there’s a methodology more suitable than another, in the end project particularities might pull the choice in one direction or another. Business Intelligence projects for example can benefit from agile approaches as they enable to better manage and deliver value by adapting the requirements to business needs as the project progresses. An agile approach works almost always better than a waterfall process. In contrast, ERP implementations seldom benefit from agile methodologies given the complexity of the project which makes from planning a real challenge, however this depends also on an organization’s dynamicity.

Especially when an organization has good experience with a methodology there’s the tendency to use the same methodology across all the projects run within the organization. This results in chopping down a project to fit an ideal form, which might be fine as long the particularities of each project are adequately addressed. Even if one methodology is not appropriate for a given scenario it doesn’t mean it can’t be used for it, however in the final equation enter also the cost, time, effort, and the quality of the end-results.

In general, one can cope with complexity by leveraging a broader set of mental models, heuristics and set of tools, and this can be done only though experimentation, through training and exposing employees to new types of experiences, through openness, through adapting the tools to the challenges ahead.

Performance Management: Mastery (Just the Quotes)

"Excellence is an art won by training and habituation. We do not act rightly because we have virtue or excellence, but we rather have those because we have acted rightly. We are what we repeatedly do. Excellence, then, is not an act but a habit." (Aristotle)

"With regard to excellence, it is not enough to know, but we must try to have and use it." (Aristotel, "Nochomachean Ethics", cca. 340 BC)

"It takes a long time to bring excellence to maturity." (Publilius Syrus, "Moral Sayings", cca. 1st century BC)

"One has attained to mastery when one neither goes wrong nor hesitates in the performance." (Friedrich Nietzsche, "Thoughts on the Prejudices of Morality", 1881)

"Order and simplification are the first steps toward the mastery of a subject - the actual enemy is the unknown." (Thomas Mann, "The Magic Mountain", 1924)

"To improve is to change; to be perfect is to change often." (Winston Churchill, [Speech, House of Commons] 1925)

"Creating a new theory is not like destroying an old barn and erecting a skyscraper in its place. It is rather like climbing a mountain, gaining new and wider views, discovering unexpected connections between our starting point and its rich environment. But the point from which we started out still exists and can be seen, although it appears smaller and forms a tiny part of our broad view gained by the mastery of the obstacles on our adventurous way up." (Albert Einstein & Leopold Infeld, "The Evolution of Physics", 1938)

"Civilization is that mode of conduct which points out to man the path of duty. Performance of duty and observance of morality are convertible terms. To observe morality is to attain mastery over our mind and our passions. So doing, we know ourselves." (Mahatma Gandhi, "Hindu Dharma", 1950)

"Leaders value learning and mastery, and so do people who work for leaders. Leaders make it clear that there is no failure, only mistakes that give us feedback and tell us what to do next." (Warren G Bennis, Training and Development Journal, 1984)

"No talent in management is worth more than the ability to master facts - not just any facts, but the ones that provide the best answers. Mastery thus involves knowing what facts you want; where to dig for them; how to dig; how to process the mined ore; and how to use the precious nuggets of information that are finally in your hand. The process can be laborious - which is why it is so often botched." (Robert Heller, "The Supermanagers", 1984)

"The source of good management is found in the imagination of leaders, persons who form new visions and manifest them with a high degree of craft. The blending of vision and craft communicates the purpose. In the arts, people who do that well are masters. In business, they are leaders." (Henry M. Boettinger, Harvard Business Review on Human Relations, 1986)

"People with a high level of personal mastery are able to consistently realize the results that matter most deeply to them-in effect, they approach their life as an artist would approach a work of art. The do that by becoming committed to their own lifelong learning." (Peter M Senge, "The Fifth Discipline: The Art and Practice of the Learning Organization", 1990)

"Personal mastery is the discipline of continually clarifying and deepening our personal vision, of focusing our energies, of developing patience, and of seeing reality objectively." (Peter M Senge, "The Fifth Discipline: The Art and Practice of the Learning Organization", 1990)

"The discipline of personal mastery [...] starts with clarifying the things that really matter to us (and) living our lives in the service of our highest aspirations." (Peter M Senge, "The Fifth Discipline: The Art and Practice of the Learning Organization", 1990)

"Mastery means responsibility, ability to respond in real time to the need of the moment." (Stephen Nachmanovitch, "Free Play: Improvisation in Life and Art", 1991

"At the heart of it, mastery is practice. Mastery is staying on the path." (George Leonard, "Mastery: The Keys to Success and Long-Term Fulfillment", 1992)

"Find the heart of it. Make the complex simple, and you can achieve mastery." (Dan Millman, "Living on Purpose: Straight Answers to Life's Tough Questions", 2000)

"Change always implies abandonment. What you're abandoning is an old way of doing things. You're abandoning it because it's old, because time has made it no longer the best way. But it is also (again because it's old) a familiar way. And more important, it is an approach that people have mastered. So the change you are urging upon your people requires them to abandon their mastery of the familiar, and to become novices once again, to become rank beginners at something with self-definitional importance." (Tom DeMarco, "Slack: Getting Past Burnout, Busywork, and the Myth of Total Efficiency", 2001)

"Mastery is an elusive concept. You never know when you achieve it absolutely and it may not help you to feel you've attained it. We can recognize it more readily in others than we can in ourselves. We have to discover our own definition of it." (Twyla Tharp, 'The Creative Habit: Learn It and Use It for Life", 2003)

"Leaders should be aware of how their mental models affect their thinking and may cause 'blind spots' that limit understanding. Becoming aware of assumptions is a first step toward shifting one’s mental model and being able to see the world in new and different ways. Four key issues important to expanding and developing a leader’s mind are independent thinking, open-mindedness, systems thinking, and personal mastery." (Richard L Daft, "The Leadership Experience" 4th Ed., 2008)

"Mastery is not a function of genius or talent. It is a function of time and intense focus applied to a particular field of knowledge." (Robert Greene, "Mastery" 2012)

"Models are formal structures represented in mathematics and diagrams that help us to understand the world. Mastery of models improves your ability to reason, explain, design, communicate, act, predict, and explore." (Scott E Page, "The Model Thinker", 2018)

"Art calls for complete mastery of techniques, developed by reflection within the soul." (Bruce Lee)

"In the pursuit of excellence, there is no finish line." (Robert H Farman)

"Only one who devotes himself to a cause with his whole strength and soul can be a true master. For this reason mastery demands all of a person." (Albert Einstein)

"The performance of public duty is not the whole of what makes a good life; there is also the pursuit of private excellence." (Bertrand Russell)

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)