16 November 2016

♟️Strategic Management: Trust (Just the Quotes)

"Organizations are social beings and their success depends on trust, subtlety and intimacy." (William Ouchi, "Theory Z", 1981)

"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)

"You cannot prevent a major catastrophe, but you can build an organization that is battle-ready, where people trust one another. In military training, the first rule is to instill soldiers with trust in their officers - because without trust, they won't fight." (Peter Drucker, "Managing the Non-Profit Organization", 1990)

"Trust is the glue of life. It's the most essential ingredient in effective communication. It's the foundational principle that holds all relationships - marriages, families, and organizations of every kind - together." (Stephen Covey, "First Things First", 1994)

"An ecology provides the special formations needed by organizations. Ecologies are: loose, free, dynamic, adaptable, messy, and chaotic. Innovation does not arise through hierarchies. As a function of creativity, innovation requires trust, openness, and a spirit of experimentation - where random ideas and thoughts can collide for re-creation." (George Siemens, "Knowing Knowledge", 2006)

"In leadership, there are no words more important than trust. In any organization, trust must be developed among every member of the team if success is going to be achieved." (Mike Krzyzewski, "Leading with the Heart: Coach K's Successful Strategies for Basketball, Business, and Life", 2010)

"Truly human leadership protects an organization from the internal rivalries that can shatter a culture. When we have to protect ourselves from each other, the whole organization suffers. But when trust and cooperation thrive internally, we pull together and the organization grows stronger as a result." (Simon Sinek, "Leaders Eat Last: Why Some Teams Pull Together and Others Don't", 2014)

"Leadership means that a group, large or small, is willing to entrust authority to a person who has shown judgement, wisdom, personal appeal, and proven competence." (Walt Disney)

♟️Strategic Management: Success (Just the Quotes)

"And it ought to be remembered that there is nothing more difficult to take in hand, more perilous to conduct, or more uncertain in its success, than to take the lead in the introduction of a new order of things. Because the innovator has for enemies all those who have done well under the old conditions, and lukewarm defenders in those who may do well under the new." (Nicolo Machiavelli, cca. 1505)

"But when one comes to the effect of the engagement, where material successes turn into motives for further action, the intellect alone is decisive. In brief, tactics will present far fewer difficulties to the theorist than will strategy." (Carl von Clausewitz, "On War", 1832)

"We learn wisdom from failure much more than from success. We often discover what will do, by finding out what will not do; and probably he who never made a mistake never made a discovery." (Samuel Smiles, "Facilities and Difficulties", 1859)

"We live in a system of approximations. Every end is prospective of some other end, which is also temporary; a round and final success nowhere. We are encamped in nature, not domesticated." (Ralph W Emerson, "Essays", 1865)

"The manager must never be lacking in knowledge of the special profession which is characteristic of the undertaking: the technical profession in industry, commercial in commerce, political in the State, military in the Army, religious in the Church, medical in the hospital, teaching in the school, etc. The technical function has long been given the degree of importance which is its due, and of which we must not deprive it, but the technical function by itself cannot endure the successful running of a business; it needs the help of the other essential functions and particularly of that of administration. This fact is so important from the point of view of the organization and management of a business that I do not mind how often I repeat it in order that it may be fully realized." (Henri Fayol, "Industrial and General Administration", 1916)

"Failure to succeed greatly in management usually occurs not so much from lack of knowledge of the important principles of the science of management as from failure to apply them. Most of the principles of successful management are old, and many of them have received sufficient publicity to be well known, but managers are curiously prone to look upon managerial success as a personal attribute that is slightly dependent on principles or laws." (Allan C Haskell, "How to Make and Use Graphic Charts", 1919)

"The making of decisions, as everyone knows from personal experience, is a burdensome task. Offsetting the exhilaration that may result from correct and successful decision and the relief that follows the termination of a struggle to determine issues is the depression that comes from failure, or error of decision, and the frustration which ensues from uncertainty." (Chester I Barnard, "The Functions of the Executive", 1938)

"Success in solving the problem depends on choosing the right aspect, on attacking the fortress from its accessible side." (George Polya, "How to Solve It", 1944)

"[System dynamics] is an approach that should help in important top-management problems [...] The solutions to small problems yield small rewards. Very often the most important problems are but little more difficult to handle than the unimportant. Many [people] predetermine mediocre results by setting initial goals too low. The attitude must be one of enterprise design. The expectation should be for major improvement [...] The attitude that the goal is to explain behavior; which is fairly common in academic circles, is not sufficient. The goal should be to find management policies and organizational structures that lead to greater success." (Jay W Forrester, "Industrial Dynamics", 1961)

"Business is a process which converts a resource, distinct knowledge, into a contribution of economic value in the market place. The purpose of a business is to create a customer. The purpose is to provide something for which an independent outsider, who can choose not to buy, is willing to exchange his purchasing power. And knowledge alone (excepting only the case of the complete monopoly) gives the products of any business that leadership position on which success and survival ultimately depend." (Peter F Drucker, "Managing for Results: Economic Tasks and Risk-taking Decisions", 1964)

"The successful manager must be a good diagnostician and must value a spirit of inquiry." (Edgar H Schein, "Organizational Psychology", 1965)

"As in war, strategic success depends on tactical effectiveness, and no degree of planning can lessen management's tactical imperatives. The first responsibility of the executive, anyway, is to the here and now. If he makes a shambles of the present, there may be no future; and the real purpose of planning - the one whose neglect is common, but poisonous - is to safeguard and sustain the company in subsequent short-run periods." (Robert Heller, "The Naked Manager: Games Executives Play", 1972)

"The dogma of delegation is simple - the Sixth Truth of Management again: either the delegatee is capable of running the operation successfully by himself or he isn't. This handy formula relieves the top executive of any responsibility except that of finding, supervising, and (at the appropriate time) moving the men who are doing all the work. He Can then truly manage by exception: he does not get worked up over operations that are going well, but concentrates on the plague spots, where everything, including the management, is going badly." (Robert Heller, "The Naked Manager: Games Executives Play", 1972)

"Effectiveness is the foundation of success - efficiency is a minimum condition for survival after success has been achieved. Efficiency is concerned with doing things right. Effectiveness is doing the right things." (Peter Drucker, "Management: Tasks, Responsibilities, Challenges", 1973)

"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 are social beings and their success depends on trust, subtlety and intimacy." (William Ouchi, "Theory Z", 1981)

"No matter how difficult or unprecedented the problem, a breakthrough to the best possible solution can come only from a combination of rational analysis, based on the real nature of things, and imaginative reintegration of all the different items into a new pattern, using nonlinear brainpower. This is always the most effective approach to devising strategies for dealing successfully with challenges and opportunities, in the market arena as on the battlefield." (Kenichi Ohmae, "The Mind Of The Strategist", 1982)

"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)

"Most managers are reluctant to comment on ineffective or inappropriate interpersonal behavior. But these areas are often crucial for professional task success. This hesitancy is doubly felt when there is a poor relationship between the two. [...] Too few managers have any experience in how to confront others effectively; generally they can more easily give feedback on inadequate task performance than on issues dealing with another's personal style." (David L Bradford & Allan R Cohen, "Managing for Excellence", 1984)

"No other area offers richer opportunities for successful innovation than the unexpected success." (Peter Drucker, "Innovation and Entrepreneurship", 1985)

"The key to successful leadership today is influence, not authority." (Kenneth H Blanchard, "Managing By Influence", 1986)

"The opportunities and threats existing in any situation always exceed the resources needed to exploit the opportunities or avoid the threats. Thus, strategy is essentially a problem of allocating resources. If strategy is to be successful, it must allocate superior resources against a decisive opportunity." (William Cohen, "Winning on the Marketing Front: The corporate manager's game plan", 1986)

"Setting goals can be the difference between success and failure. [...] Goals must not be defined so broadly that they cannot be quantified. Having quantifiable goals is an essential starting point if managers are to measure the results of their organization's activities. [...] Too often people mistake being busy for achieving goals." (Philip D Harvey & James D Snyder, Harvard Business Review, 1987)

"[Successful organizations] comprehend uncertainty. They set direction, not detailed strategy. They are the best strategists precisely because they are suspicious of forecasts and open to surprise. They think strategic planning is greatas long as no one takes the plans too seriously." (Robert H Waterman, "The Renewal Factor", 1987)

"The most important reason for our success is we set our objectives and make sure we follow through on them." (Annette B Roux, The New York Times, 1987)

"The tendency to hide unfavorable information often occurs in companies that are quick to reward success and equally quick to punish failure." (Robert M Tomasko, "Downsizing", 1987)

"[…] the most successful strategies are visions, not plans. Strategic planning isn’t strategic thinking. One is analysis, and the other is synthesis." (Henry Mintzberg, "The Fall and Rise of Strategic Planning", Harvard Business Review, 1994) [source]

"Organizations need the capacity for double-loop learning. Double-loop learning occurs when managers question their underlying assumptions and reflect on whether the theory under which they were operating remains consistent with current evidence, observations, and experience. Of course, managers need feedback about whether their planned strategy is being executed according to plan-the single-loop learning process. But even more important, they need feedback about whether the planned strategy remains a viable and successful strategy - the double-loop learning process. Managers need information so that they can question whether the fundamental assumptions made when they launched the strategy are valid." (Robert S Kaplan & David P Norton, "The Balanced Scorecard", Harvard Business Review, 1996)

"Strategy is creating fit among a company’s activities. The success of a strategy depends on doing many things well - not just a few - and integrating among them. If there is no fit among activities, there is no distinctive strategy and little sustainability. Management reverts to the simpler task of overseeing independent functions, and operational effectiveness determines an organization’s relative performance."  (Michael E Porter, "What is Strategy?", Harvard Business Review, 1996)

"The Balanced Scorecard translates mission and strategy into objectives and measures, organized into four different perspectives: financial, customer, internal business process, and learning and growth. The scorecard provides a framework, a language, to communicate mission and strategy; it uses measurement to inform employees about the drivers of current and future success." (Robert S Kaplan & David P Norton, "The Balanced Scorecard", Harvard Business Review, 1996)

"Business success contains the seeds of its own destruction. The more Successful you are, the more people want a chunk of your business and then another chunk and then another until there is nothing." (Andrew S Grove, "Only the Paranoid Survive", 1998)

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

"Business success is less a function of grandiose predictions than it is a result of being able to respond rapidly to real changes as they occur." (Jack Welch, "Jack: Straight from the Gut", 2001)

"I've learned that mistakes can often be as good a teacher as success." (Jack Welch, "Jack: Straight from the Gut", 2001)

"Organizations must know and understand the current organizational culture to be successful at implementing change. We know that it is the organization’s culture that drives its people to action; therefore, management must understand what motivates their people to attain goals and objectives. Only by understanding the current organizational culture will it be possible to begin to try and change it." (Margaret Y Chu, "Blissful Data", 2004)

"The most basic issue for organizational success is correctly matching a system’s complexity to its environment. When we want to accomplish a task, the complexity of the system performing that task must match the complexity of the task. In order to perform the matching correctly, one must recognize that each person has a limited level of complexity. Therefore, tasks become difficult because the complexity of a person is not large enough to handle the complexity of the task. The trick then is to distribute the complexity of the task among many individuals." (Yaneer Bar-Yam, "Making Things Work: Solving Complex Problems in a Complex World", 2004)

"Whereas strategy is abstract and based on long-term goals, tactics are concrete and based on finding the best move right now. Tactics are conditional and opportunistic, all about threat and defense. No matter what pursuit you’re engaged in - chess, business, the military, managing a sports team - it takes both good tactics and wise strategy to be successful." (Garry Kasparov, "How Life Imitates Chess", 2007)

"Strategy is the serious work of figuring out how to translate vision and mission into action. Strategy is a general plan of action that describes resource allocation and other activities for dealing with the environment and helping the organization reach its goals. Like vision, strategy changes, but successful companies develop strategies that focus on core competence, develop synergy, and create value for customers. Strategy is implemented through the systems and structures that are the basic architecture for how things get done in the organization." (Richard L Daft, "The Leadership Experience" 4th Ed., 2008)

"Truly successful decision-making relies on a balance between deliberate and instinctive thinking." (Malcolm Gladwell, "Blink: The Power of Thinking Without Thinking", 2008)

"A blame culture is corrosive, eroding the team ethos that is vital for success. If they fear that they will be pilloried or punished for their mistakes, your colleagues will start worrying more about how to protect their back than doing what’s best for the team and wider organization. In the worst cases, this can even lead to lying, setting up fall guys, and other dysfunctional behavior." (Paul Butcher, "Debug It! Find, Repair, and Prevent Bugs in Your Code", 2009)

"A culture that believes that it is better to ask forgiveness afterward rather than permission before, that rewards people for success but gives them permission to fail, has removed one of the main obstacles to the formation of new ideas." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009) 

"In leadership, there are no words more important than trust. In any organization, trust must be developed among every member of the team if success is going to be achieved." (Mike Krzyzewski, "Leading with the Heart: Coach K's Successful Strategies for Basketball, Business, and Life", 2010)

"[Executives] make decisions based on delusional optimism rather than on a rational weighting of gains, losses, and probabilities. They overestimate benefits and underestimate costs. They spin scenarios of success while overlooking the potential for mistakes and miscalculations. As a result, they pursue initiatives that are unlikely to come in on budget or on time or to deliver the expected returns - ​​​​​​or even to be completed." (Daniel Kahneman, "Thinking, Fast and Slow", 2011)

"Key performance indicators (KPIs) are the vital navigation instruments used by managers to understand whether their business is on a successful voyage or whether it is veering off the prosperous path. The right set of indicators will shine light on performance and highlight areas that need attention. ‘What gets measured gets done’ and ‘if you can’t measure it, you can’t manage it’ are just two of the popular sayings used to highlight the critical importance of metrics. Without the right KPIs managers are sailing blind." (Bernard Marr, "Key Performance Indicators (KPI): The 75 measures every manager needs to know", 2011)

"An organization's strategy is simply its plan for success. It's nothing more than the collection of intentional decisions a company makes to give itself the best chance to thrive and differentiate from competitors." (Patrick Lencioni, "The Advamtage: Why Organizational Health Trumps Everything Else In Business", 2012)

"Ultimately, leadership is not about glorious crowning acts. It's about keeping your team focused on a goal and motivated to do their best to achieve it, especially when the stakes are high and the consequences really matter. It is about laying the groundwork for others' success, and then standing back and letting them shine." (Chris Hadfield, "An Astronaut's Guide to Life on Earth", 2013)

"Culture is an emergent phenomenon produced by structures, practices, leadership behavior, incentives, symbols, rituals, and processes. All those levers have to be pulled to have any chance of success. However, one driver of culture change is more important than the others. Culture change fails when the most visible symbols of it fail to change. Those key symbols are almost always the top leader’​​​​​​s behavior, which speaks much louder than anything they might say." (Paul Gibbons, "The Science of Successful Organizational Change", 2015)

"Key performance indicators (KPIs) are those indicators that focus on the aspects of organizational performance that are the most critical for the current and future success of the organization." (David Parmenter, "Key Performance Indicators: Developing, implementing, and using winning KPIs" 3rd Ed., 2015)

"Key Performance Indicators (KPIs) in many organizations are a broken tool. The KPIs are often a random collection prepared with little expertise, signifying nothing. [...] KPIs should be measures that link daily activities to the organization’s critical success factors (CSFs), thus supporting an alignment of effort within the organization in the intended direction." (David Parmenter, "Key Performance Indicators: Developing, implementing, and using winning KPIs" 3rd Ed., 2015)

"Governance and leadership are the yin and the yang of successful organisations. If you have leadership without governance you risk tyranny, fraud and personal fiefdoms. If you have governance without leadership you risk atrophy, bureaucracy and indifference." (Mark Goyder, "What Matters in Corporate Governance?", 2015)

"No methodology can guarantee success. But a good methodology can provide a feedback loop for continual improvement and learning." (Ash Maurya, "Scaling Lean: Mastering the Key Metrics for Startup Growth", 2016)

"The field of big-data analytics is still littered with a few myths and evidence-free lore. The reasons for these myths are simple: the emerging nature of technologies, the lack of common definitions, and the non-availability of validated best practices. Whatever the reasons, these myths must be debunked, as allowing them to persist usually has a negative impact on success factors and Return on Investment (RoI). On a positive note, debunking the myths allows us to set the right expectations, allocate appropriate resources, redefine business processes, and achieve individual/organizational buy-in." (Prashant Natarajan et al, "Demystifying Big Data and Machine Learning for Healthcare", 2017) 

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

"Strategy is a style of thinking, a conscious and deliberate process, an intensive implementation system, the science of insuring future success." (Pete Johnson)

13 November 2016

🎯Pradeep Menon - Collected Quotes

"A central node acts as a hub in the hub-spoke pattern, and many edge nodes act as the spoke. The hub is the central node that orchestrates and governs the data sharing between each of the spokes. Each spoke node can have its own data lakehouse. The way the spoke nodes are constructed depends on the organizational structure. It can be at a department level, a separate organizational unit, or even a sub-department within a large department." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"A data sharing data service shares data, in any format and any size, from multiple sources within an organization or other organizations. This type of service provides the required control to share data and allows data-sharing policies to be created. It also enables data sharing in a structured manner and offers complete visibility into how the data is shared and how it is used. A data-sharing system uses APIs for data sharing." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"A data lakehouse stores a lot of data. It stores data in the data lake layer and the serving layer in structured and unstructured formats. The data needs to be processed with different types of compute engines. It can be a batch-based compute or a stream-based compute. A tightly coupled compute and storage layer strips off the flexibility required in a data lakehouse. Decoupling compute and storage also has a cost implication - storage is cheap and persistent but compute is expensive and ephemeral. It gives you the flexibility to spin up compute services on-demand and scale them as required, and also gives better cost control and cost predictability." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"A data warehouse service provides cleansed and transformed data that can be used for multiple purposes. First, it serves as a layer for reporting and BI. Second, it is a platform to query data for business or data analysis. Third, it serves as a repository to store historical data that needs to be online and available. Finally, it also acts as a source of transformed data for other downstream data marts that may cater to specific departmental requirements." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"Advanced analytics pivots around machine learning methods. Machine learning employs statistical learning methods to perform analysis on data. These statistical methods utilize algorithms that predict what may happen based on historical data or extract complex mathematical relationships from data to generate insights." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"An API is an interface that allows applications to interact with an external service using a simple set of commands. Data can also be served as part of API interaction. As the data is exposed to multiple external services, API-based methods can scale to share data securely with external services. Data through an API is served in JSON format, therefore the technology used to serve the data using APIs should be able to support JSON formats. For example, a NoSQL database can store such data." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"Any change in the reporting requirement had to go through a long-winded process of data model changes, ETL code changes, and respective changes to the reporting system. Often, the ETL process was a specialized skill and became a bottleneck for reducing data to insight turnover time. The nature of analytics is unique. The more you see the output, the more you demand. Many EDW projects were deemed a failure. The failure was not from a technical perspective, but from a business perspective. Operationally, the design changes required to cater to these fast-evolving requirements were too difficult to handle." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"Data architecture is the structure that enables the storage, transformation, exploitation, and governance of data." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"Descriptive analytics takes the form of on-demand queries, standard reporting, self-service business intelligence, and data lake exploration. This category of analytics performs analytics on historical data by providing different points of view on the data. The points of view are created by aggregating and filtering the quantitative data (also called the measure), and slicing the data across attributes of functional dimensions, such as sales, customers, and products. We can deliver descriptive analytics in multiple formats such as files, cross tab reports, visual reports, or dashboards." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"Each domain data lakehouse may opt to have its data catalog. However, the critical component in this architecture is the data mesh catalog. The data mesh catalog is the master catalog used to discover the data elements available in different nodes. Each domain-oriented node will donate its metadata to the data mesh catalog. This donation of metadata determines the effectiveness of the data mesh architecture. Once the metadata is contributed, other nodes can browse through the data mesh catalog. They can select the data of interest and mutually share data between the nodes through a governed data sharing process. The critical point to note here is that, unlike the hub-spoke architecture, the data mesh architecture enables data sharing between the 'spoke nodes'. There is no hub node in a data mesh architecture." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"The AI-ML service allows data scientists to build, train and deploy production-ready AI-ML models. This layer also provides the framework to maintain and monitor such models. In addition, it gives the ability for teams to collaborate as they go about building these models. This service should be able to scale up and down as required and should be able to facilitate automatic model deployment and operations." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"The data mesh pattern doesn't feature a central node and is loosely coupled compared to a hub-spoke architecture. It has different data lakehouse nodes that are independent of each other. The node data lakehouses are domain-driven. A domain can be oriented in multiple ways. The original idea of data mesh alludes to a source-oriented domain aligning to business processes. However, a more practical approach would be to define a domain based on the organizational setup and practicality; for example, a domain can be a product group, it can be separate organizational entities, and it can also be a specific business process, such as marketing. Each domain has its own data lakehouse that is managed and maintained by that domain." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"A Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is without having first to structure the data and run different types of analytics  - from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"A Data Lakehouse provides a unified platform for various data workloads, such as descriptive, predictive, and prescriptive analytics. It can handle structured and unstructured data and enforce schema at both read and write times, enabling traditional business intelligence tasks and advanced analytics on the same platform." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"A defining attribute of the domain-oriented ownership principle is the focus on context preservation in Data Management. This aspect accentuates the importance of keeping data within its native domain environment, allowing it to retain its original context, value, and meaning. When data is managed close to its source, its contextual richness is preserved. This sharply contrasts centralized models, where data is often abstracted from its source, leading to potential loss of signal or context. When data remains within its generating domain, it retains the nuances and specificities unique to its activities, challenges, and goals." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"A transformative perspective offered by the Data Mesh is envisioning data as a product. This section underscores the significance of curating data with the meticulousness and vision akin to product development, ensuring it delivers tangible value to its consumers. The ripple effects of this paradigm shift, spanning roles, processes, and technologies, are also meticulously unpacked." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"Centralized governance structures often have an abstract view of data, focusing more on uniformity and compliance than context and relevance. While these are essential elements, the nuance often needs to be noticed. Decentralized governance flips the script by giving data ownership to the domain that generates it. The domain has the richest understanding of the data’s context, relevance, and potential impact, thereby being well-positioned to enforce governance policies that improve data quality." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"Data Mesh also emphasizes aligning data products with business domains and use- cases to ensure that the data serves a clear business purpose and provides tangible value. Beforehand, we define the value proposition, target audience, quality attributes, and KPIs of each data product to ensure that it meets or exceeds the expectations of its consumers." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"Data Mesh emphasizes ensuring reliable, consistent, and interoperable data products. When data is treated as a product, quality is non-negotiable. High-quality data must meet the expectations and requirements of its users, both internally and externally. Additionally, data products must be designed with other products in mind, adhering to principles like loose coupling for easy interchangeability and high cohesion for strong functional relatedness. This feature enables the integration of different data products, ensuring seamless interoperability and greater usability. Data products should be reliable, complete, accurate, and accurate. They should also be integrated, compatible, and consistent rather than isolated, incompatible, or conflicting." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"[...] domain-oriented ownership empowers individual domains to create and adapt their data strategies with agility, with a thorough understanding of their business needs and market demands. Whether pivoting due to a new competitor’s actions or adjusting to a sudden change in consumer behavior, domains can independently and swiftly modify their data strategies, providing them with a unique edge in the marketplace." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"Domain-oriented ownership is a core principle of data mesh. It entails that data producers, experts in their business domains, are responsible for the entire lifecycle of their produced data. Specifically, they take ownership of the data from the point of ingestion through transformation, serving, quality assurance, and governance. Moreover, they are responsible for the data products created from their data, which serve as units of data consumption for other domains or users." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"Domain autonomy should not be mistaken for a lack of governance or accountability. Autonomy, in this context, implies a higher level of responsibility. Domains are free to act and accountable for their actions, especially regarding how well their data strategies align with domain-specific and broader organizational objectives." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"Empowering with self-serve data infrastructure: The Data Mesh champions the ethos of self-reliance. By empowering teams to construct and oversee their data infrastructure, organizations can foster a culture of speed, autonomy, and accountability." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"Governance refers to an organization’s framework to exercise direction and control over a specific domain. In the context of data, it could include rules, protocols, and systems to manage data quality, security, and accessibility."  (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"In a centralized model, changes to data strategy often require navigating through bureaucratic layers and rigid governance structures. This delays adaptability and increases the risk of misalignment between what the data strategy aims to achieve and what the business needs. Centralized models are typically disconnected from the ground realities of individual business units, leading to a generic, one-size-fits-all approach that seldom caters to unique market challenges or opportunities." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"Promoting domain-oriented ownership is to combat the common problem of organizational silos. Silos can significantly hinder the free flow of data and expertise, making decision-making and innovation more challenging. We aim to break down these barriers by advocating for domain-oriented ownership and creating a more dynamic and collaborative data management landscape." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"The allure of Data Lakes was their ability to store vast amounts of raw data. However, this advantage can become counterproductive without stringent governance and management protocols. In their zeal to harness the power of Big Data, some organizations indiscriminately dump data into their lakes. Without proper classification, curation, and quality checks, these lakes can become swamps - murky repositories filled with valuable data, redundant information, and outdated datasets. Navigating these data swamps becomes a significant challenge, leading to prolonged data retrieval times, increased chances of using obsolete or incorrect data, and a decline in the agility and efficiency of data-driven decision-making processes rather than facilitating quick and insightful analytics." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"When data is considered a product, it creates opportunities for collaboration across different domains. This collaboration involves working with other teams to create, share, and use data products that span multiple areas of expertise, interest, or value. Data Mesh promotes cross-domain collaboration by focusing on the consumers rather than the producers. Data products are made available through standardized interfaces and protocols that support various modes of consumption and are governed by domain experts who understand the context and nuances of their data." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

11 November 2016

🔭Data Science: Standards (Just the Quotes)

"At the present time there is a total lack of standardization in the form of diagram to use for nearly all classes of representation. This makes it difficult to compare reports of different investigators on the same subject because their diagrams are not constructed alike." (William C Marshall,Graphical methods for schools, colleges, statisticians, engineers and executives", 1921)

"Precision is expressed by an international standard, viz., the standard error. It measures the average of the difference between a complete coverage and a long series of estimates formed from samples drawn from this complete coverage by a particular procedure or drawing, and processed by a particular estimating formula." (W Edwards Deming,On the Presentation of the Results of Sample Surveys as Legal Evidence", Journal of the American Statistical Association Vol 49 (268), 1954)

"The relevant question is not whether ANOVA assumptions are met exactly, but rather whether the plausible violations of the assumptions have serious consequences on the validity of probability statements based on the standard assumptions." (Gene V Glass et al,Consequences of Failure to Meet Assumptions Underlying the Fixed Effects Analyses of Variance and Covariance", Review of Educational Research Vol. 42 (3), 1972)

"Exploratory data analysis, EDA, calls for a relatively free hand in exploring the data, together with dual obligations: (•) to look for all plausible alternatives and oddities - and a few implausible ones, (graphic techniques can be most helpful here) and (•) to remove each appearance that seems large enough to be meaningful - ordinarily by some form of fitting, adjustment, or standardization [...] so that what remains, the residuals, can be examined for further appearances." (John W Tukey,Introduction to Styles of Data Analysis Techniques", 1982)

"The conditions under which many data graphics are produced - the lack of substantive and quantitative skills of the illustrators, dislike of quantitative evidence, and contempt for the intelligence of the audience-guarantee graphic mediocrity. These conditions engender graphics that (1) lie; (2) employ only the simplest designs, often unstandardized time-series based on a small handful of data points; and (3) miss the real news actually in the data." (Edward R Tufte,The Visual Display of Quantitative Information", 1983)

"It would help if the standard statistical programs did not generate t statistics in such profusion. The programs might be written to ask, 'Do you really have a probability sample?', 'By what standard would you judge a fitted coefficient large or small?' Or perhaps they could merely say, printed in bold capitals beside each equation, 'So What Else Is New?'" (Donald M McCloskey,The Loss Function Has Been Mislaid: The Rhetoric of Significance Tests", American Economic Review Vol. 75, 1985)

"When evaluating a model, at least two broad standards are relevant. One is whether the model is consistent with the data. The other is whether the model is consistent with the ‘real world.’" (Kenneth Bollen,Structural Equations with Latent Variable", 1989)

"With each pattern, small piecework is standardized into a larger chunk or unit. Patterns become the building blocks for design and construction. Finding and applying patterns indicates progress in a field of human endeavor." (Peter Coad,Object-Oriented Pattern", 1992)

"One important aspect of reality is improvisation; as a result of special structure in a set of data, or the finding of a visualization method, we stray from the standard methods for the data type to exploit the structure or the finding." (William S Cleveland,Visualizing Data", 1993)

"When the distributions of two or more groups of univariate data are skewed, it is common to have the spread increase monotonically with location. This behavior is monotone spread. Strictly speaking, monotone spread includes the case where the spread decreases monotonically with location, but such a decrease is much less common for raw data. Monotone spread, as with skewness, adds to the difficulty of data analysis. For example, it means that we cannot fit just location estimates to produce homogeneous residuals; we must fit spread estimates as well. Furthermore, the distributions cannot be compared by a number of standard methods of probabilistic inference that are based on an assumption of equal spreads; the standard t-test is one example. Fortunately, remedies for skewness can cure monotone spread as well." (William S Cleveland,Visualizing Data", 1993)

"While some social problems statistics are deliberate deceptions, many - probably the great majority - of bad statistics are the result of confusion, incompetence, innumeracy, or selective, self-righteous efforts to produce numbers that reaffirm principles and interests that their advocates consider just and right. The best response to stat wars is not to try and guess who's lying or, worse, simply to assume that the people we disagree with are the ones telling lies. Rather, we need to watch for the standard causes of bad statistics - guessing, questionable definitions or methods, mutant numbers, and inappropriate comparisons." (Joel Best,Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists", 2001)

"The definition of a ‘good model’ is when everything inside it is visible, inspectable and testable. It can be communicated effortlessly to others. A ‘bad model’ is a model that does not meet these standards, where parts are hidden, undefined or concealed and it cannot be inspected or tested; these are often labelled black box models." (Hördur V Haraldsson & Harald U Sverdrup,Finding Simplicity in Complexity in Biogeochemical Modelling" [inEnvironmental Modelling: Finding Simplicity in Complexity", Ed. by John Wainwright and Mark Mulligan, 2004])

"The inevitability of variability complicates the evaluation and use of data. It must be recognized that many uses require data quality that may be difficult to achieve. There are minimum quality standards required for every measurement situation (sometimes called data quality objectives). These standards should be established in advance and both the producer and the user must be able to determine whether they have been met. The only way that this can be accomplished is to attain statistical control of the measurement process and to apply valid statistical procedures in the analysis of the data." (Cheryl Cihon & John K Taylor, "Statistical Techniques for Data Analysis" 2nd. ed., 2005)

"Regularization works because it is the sum of the coefficients of the predictor variables, therefore it’s important that they’re on the same scale or the regularization may find it difficult to converge, and variables with larger absolute coefficient values will greatly influence it, generating an infective regularization. It’s good practice to standardize the predictor values or bind them to a common min‐max, such as the [‐1,+1] range." (Luca Massaron & John P Mueller,Python for Data Science For Dummies", 2015)

"The closer that sample-selection procedures approach the gold standard of random selection - for which the definition is that every individual in the population has an equal chance of appearing in the sample - the more we should trust them. If we don’t know whether a sample is random, any statistical measure we conduct may be biased in some unknown way." (Richard E Nisbett,Mindware: Tools for Smart Thinking", 2015)

"Measurements must be standardized. There must be clear, replicable, and precise procedures for collecting data so that each person who collects it does it in the same way." (Daniel J Levitin,Weaponized Lies", 2017)

"The danger of overfitting is particularly severe when the training data is not a perfect gold standard. Human class annotations are often subjective and inconsistent, leading boosting to amplify the noise at the expense of the signal. The best boosting algorithms will deal with overfitting though regularization. The goal will be to minimize the number of non-zero coefficients, and avoid large coefficients that place too much faith in any one classifier in the ensemble." (Steven S Skiena,The Data Science Design Manual", 2017)

"There is often no one 'best' visualization, because it depends on context, what your audience already knows, how numerate or scientifically trained they are, what formats and conventions are regarded as standard in the particular field you’re working in, the medium you can use, and so on. It’s also partly scientific and partly artistic, so you get to express your own design style in it, which is what makes it so fascinating." (Robert Grant,Data Visualization: Charts, Maps and Interactive Graphics", 2019) 

09 November 2016

♟️Strategic Management: Customers (Just the Quotes)

"A business process is a collection of activities that takes one or more kinds of input and creates an output that is of value to the customer. A business process has a goal and is affected by events occurring in the external world or in other processes." (James A Champy & Michael M Hammer, "Reengineering the Corporation", 1993)

"Reengineering posits a radical new principle: that the design of work must be based not on hierarchical management and the specialization of labor but on end-to-end processes and the creation of value for the customer." (James A Champy & Michael M Hammer, "Reengineering the Corporation", 1993)

"This is what systems thinking is all about: the idea of building an organization in which each piece, and partial solution of the organization has the fit, alignment, and integrity with your overall organization as a system, and its outcome of serving the customer." (Stephen G Haines, "The Systems Thinking Approach to Strategic Planning and Management", 2000)

"In business, as in game theory and chess, all great strategies start with a vision of the future. In one sense, the recipe is simple: it should include a sense of where the organization should go, what customers are likely to pay for, and how the organization can offer a unique product or service that customers will buy. The devil, of course, lies in the details." (David B Yoffie & Michael A Cusumano, "Strategy Rules", 2015)

"Thinking strategically is the fun part of business. Great strategists think big thoughts about the purpose of their enterprises, the long-run visions for their firms, the big bets they plan to make, and the products, platforms, and ecosystems they hope to build. But it is not enough to think big thoughts. To become a great strategist, you must turn your vision and high-level ideas into tactics, actions, and organizations that reach the customer and fend off the competition." (David B Yoffie & Michael A Cusumano, "Strategy Rules", 2015)

"We need indicators of overall performance that need only be reviewed on a monthly or bimonthly basis. These measures need to tell the story about whether the organization is being steered in the right direction at the right speed, whether the customers and staff are happy, and whether we are acting in a responsible way by being environmentally friendly. These measures are called key result indicators (KRIs)." (David Parmenter, "Key Performance Indicators: Developing, implementing, and using winning KPIs" 3rd Ed., 2015)

"A clear, thoughtful mission statement, developed collaboratively with and shared with managers, employees, and often customers, provides a shared sense of purpose, direction, and opportunity." (Philip Kotler & Kevin L Keller, "Marketing Management" 15th Ed., 2016) 

"Evidence is freely available which demonstrates a gap between what the company thinks is important to customers and what customers actually deem to be the most important when it comes to making their choices. The failure to understand what is really important leads to customers receiving a sub-optimal experience and the company sub-optimising its commercial position." (Alan Pennington, "The Customer Experience Book", 2016)

"Data from the customer interactions is the lifeblood for any organisation to view, understand and optimise the customer experience both remotely and on the front line! In the same way that customer experience experts understand that it’s the little things that count, it’s the small data that can make all the difference." (Alan Pennington, "The Customer Experience Book", 2016)

"[…] deliver a customer experience where the customer sees real value from how you use the data that they share with you and they will keep interacting/sharing that data and their consent for you to use it!" (Alan Pennington, "The Customer Experience Book", 2016)

"Somebody once told me, 'Manage the top line, and the bottom line will follow.' What's the top line? It's things like, why are we doing this in the first place? What's our strategy? What are customers saying? How responsive are we? Do we have the best products and the best people? Those are the kind of questions you have to focus on." (Steve Jobs, "Motivating Thoughts of Steve Jobs", 2016)

"The bad news is that companies tend to focus on three out of the four elements of the balanced scorecard and emphasis is skewed away from the customer component, which is the least understood and believed by many to be the least quantifiable." (Alan Pennington, "The Customer Experience Book", 2016)

06 November 2016

💠🛠️SQL Server: Administration (System.OutOfMemoryException in SQL Server Management Studio and other 32-bit Drawbacks)

I was playing this week with a few datasets downloaded from the web on various topics, trying to torture the data until they’ll confess something. A few of the datasets were prepared for load into a MySQL database as individual INSERT INTO statements. They were containing between 100000 and a few millions of records. While looking at the big but slim datasets in SSMS (SQL Server Management Studio) and reconciling the differences between MySQL and SQL Server I got several times the System.OutOfMemoryException exception, SSMS crashing one or two times. That should be ok, given the number of records, though I was surprised that I got the same error message while executing the INSERT INTO statements for one of the smallest datasets which had about 300000 records:
"An error occurred while executing batch. Error message is: Exception of type 'System.OutOfMemoryException' was thrown"
Kb 2874903 brings some light into the topic – SSMS is still a 32-bit process and thus limited to 2GB of memory. The Kb offers three methods to avoid this issue. The first two, outputting the query results to text or file didn’t worked. The third method based on using sqlcmd utility worked smoothly with a syntax like the one below:
sqlcmd -i “<file_name.sql>” -d “<database name>”

So it doesn’t matter that you’re having a supercomputer and that working with big datasets becomes a necessity nowadays, this limitation can make data loading just a little bit more complicated. On one side, it’s true that when dealing with such datasets is probably recommended to use directly sqlcmd to execute the scripts. On the other side, independently from this type of problem, even if understandable from the need of keeping backwards compatibility with 32-bit platforms/solutions, it’s hard to digest the fact that Microsoft keeps some of its products 32-bit based when SQL Server is targeting 64-bit platforms. One has same problem when using BIDS (Business Intelligence Development Studio), developing SSRS, SSIS or SSAS solutions under 32-bit and having maybe to deploy the code as 64-bit (e.g. SQL Server Agent). From my point of view most of the issues I had were when dealing with proprietary drivers like the ones for Oracle or even for MS Office. In addition in SSIS there could be features that are only available in 32-bit versions, or have limitations on 64-bit computers (see [5]). As it seems also the SQL Server Data Tools (SSDT) will have similar drawbacks…

Anyway, sqlcmd utility saved the day with a minimum of overhead. Unfortunately it’s not always that easy to solvethe compatibility issues between 32-bit and 64-bit software and platforms.

Update 20.06.2017: One can synchronize the runtime version between BIDS and SQL Server Agent pretty easy. In BIDS under "Configuration Properties/Debug Option" at Project level, there is the “Run64BitRuntime” Property. Set to false it will run your package on 32-bit version. In a SQL Server Agent Package, there is the “Use 32 bit runtime” Checkbox under “Execution options” at step level. Checking this checkbox will run your package on 32-bit version.

A hint that the two values might be out of synch is the following error message raised when running the package:
"Attempt to load Oracle client libraries threw BadImageFormatException. This problem will occur when running in 64 bit mode with the 32 bit Oracle client components installed."
Resources:
[1] Microsoft Support (2013) Kb 2874903: "System.OutOfMemoryException" exception when you execute a query in SQL Server Management Studio [link]
[2] MSDN (2016) SQL Server 2016: sqlcmd Utility [link]
[3] MSDN (2016) SQL Server 2016: Use the sqlcmd Utility [link]
[4] MSDN (2012) Introducing Business Intelligence Development Studio  [link]
[5] SQL Server 2008 R2: 64 bit Considerations for Integration Services [link

02 November 2016

♟️Strategic Management: Integration (Just the Quotes)

"By integration we mean the process of achieving unity of effort among the various subsystems in the accomplishment of the organization's tasks." (Paul R Lawrence, "Organization and environment: Managing differentiation and integration", 1967)

"No matter how difficult or unprecedented the problem, a breakthrough to the best possible solution can come only from a combination of rational analysis, based on the real nature of things, and imaginative reintegration of all the different items into a new pattern, using nonlinear brainpower. This is always the most effective approach to devising strategies for dealing successfully with challenges and opportunities, in the market arena as on the battlefield." (Kenichi Ohmae, "The Mind Of The Strategist", 1982)

"Culture [is] a pattern of basic assumptions invented, discovered, or developed by a given group as it learns to cope with its problems of external adaptation and internal integration that has worked well enough to be considered valid and, therefore, to be taught to new members as the correct way to perceive, think, and feel in relation to those problems." (Edgar H Schein, "Organizational Culture and Leadership", 1985)

"To keep the business from disintegrating, the concept of information systems architecture is becoming less of an option and more of a necessity." (John Zachman, "A Framework for Information Systems Architecture", 1987)

"Conventional process structures are fragmented and piecemeal, and they lack the integration necessary to maintain quality and service. They are breeding grounds for tunnel vision, as people tend to substitute the narrow goals of their particular department for the larger goals of the process as a whole. When work is handed off from person to person and unit to unit, delays and errors are inevitable. Accountability blurs, and critical issues fall between the cracks." (Michael M Hammer, "Reengineering Work: Don't Automate, Obliterate", Magazine, 1990) [source]

"But the net effect of increasing scale, centralization of capital, vertical integration and diversification within the corporate form of enterprise has been to replace the 'invisible hand' of the market by the 'visible hand' of the managers." (David Harvey, "The Limits To Capital", 2006)

01 November 2016

🔭Data Science: Puzzles (Just the Quotes)

"While the individual man is an insoluble puzzle, in the aggregate he becomes a mathematical certainty. You can, for example, never foretell what anyone man will be up to, but you can say with precision what an average number will be up to. Individuals vary, but percentages remain constant. So says the statistician." (Sir Arthur C Doyle, "The Sign of Four", 1890)

"The discovery which has been pointed to by theory is always one of profound interest and importance, but it is usually the close and crown of a long and fruitful period, whereas the discovery which comes as a puzzle and surprise usually marks a fresh epoch and opens a new chapter in science." (Sir Oliver J Lodge, [Becquerel Memorial Lecture] Journal of the Chemical Society, Transactions 101(2), 1912) 

"[while] the traditional way is to regard the facts of science as something like the parts of a jig-saw puzzle, which can be fitted together in one and only one way, I regard them rather as the tiny pieces of a mosaic, which can be fitted together in many ways. A new theory in an old subject is, for me, a new mosaic pattern made with the pieces taken from an older pattern. [...] Theories come into fashion and theories go out of fashion, but the facts connected with them stay." (William H George, "The Scientist in Action", 1936)

"The laws of science are the permanent contributions to knowledge - the individual pieces that are fitted together in an attempt to form a picture of the physical universe in action. As the pieces fall into place, we often catch glimpses of emerging patterns, called theories; they set us searching for the missing pieces that will fill in the gaps and complete the patterns. These theories, these provisional interpretations of the data in hand, are mere working hypotheses, and they are treated with scant respect until they can be tested by new pieces of the puzzle." (Edwin P Whipple, "Experiment and Experience", [Commencement Address, California Institute of Technology] 1938)

"The methods of science may be described as the discovery of laws, the explanation of laws by theories, and the testing of theories by new observations. A good analogy is that of the jigsaw puzzle, for which the laws are the individual pieces, the theories local patterns suggested by a few pieces, and the tests the completion of these patterns with pieces previously unconsidered." (Edwin P Hubble, "The Nature of Science and Other Lectures", 1954)

"[…] the progress of science is a little like making a jig-saw puzzle. One makes collections of pieces which certainly fit together, though at first it is not clear where each group should come in the picture as a whole, and if at first one makes a mistake in placing it, this can be corrected later without dismantling the whole group." (Sir George Thomson, "The Inspiration of Science", 1961)

"One often hears that successive theories grow ever closer to, or approximate more and more closely to, the truth. Apparently, generalizations like that refer not to the puzzle-solutions and the concrete predictions derived from a theory but rather to its ontology, to the match, that is, between the entities with which the theory populates nature and what is ‘really there’." (Thomas S Kuhn, "The Structure of Scientific Revolutions", 1970)

"Owing to his lack of knowledge, the ordinary man cannot attempt to resolve conflicting theories of conflicting advice into a single organized structure. He is likely to assume the information available to him is on the order of what we might think of as a few pieces of an enormous jigsaw puzzle. If a given piece fails to fit, it is not because it is fraudulent; more likely the contradictions and inconsistencies within his information are due to his lack of understanding and to the fact that he possesses only a few pieces of the puzzle. Differing statements about the nature of things […] are to be collected eagerly and be made a part of the individual's collection of puzzle pieces. Ultimately, after many lifetimes, the pieces will fit together and the individual will attain clear and certain knowledge." (Alan R Beals, "Strategies of Resort to Curers in South India" [contributed in Charles M Leslie (ed.), Asian Medical Systems: A Comparative Study", 1976]) 

"Data, seeming facts, apparent asso­ciations-these are not certain knowledge of something. They may be puzzles that can one day be explained; they may be trivia that need not be explained at all." (Kenneth Waltz, "Theory of International Politics", 1979)

"This remarkable state of affairs [overuse of significance testing] is analogous to engineers’ teaching (and believing) that light consists only of waves while ignoring its particle characteristics - and losing in the process, of course, any motivation to pursue the most interesting puzzles and paradoxes in the field." (Geoffrey R Loftus, "On the tyranny of hypothesis testing in the social sciences", Contemporary Psychology 36, 1991)

"The art of science is knowing which observations to ignore and which are the key to the puzzle." (Edward W Kolb, "Blind Watchers of the Sky", 1996)

"Accurate estimates depend at least as much upon the mental model used in forming the picture as upon the number of pieces of the puzzle that have been collected." (Richards J. Heuer Jr, "Psychology of Intelligence Analysis", 1999)

"[…] most earlier attempts to construct a theory of complexity have overlooked the deep link between it and networks. In most systems, complexity starts where networks turn nontrivial. No matter how puzzled we are by the behavior of an electron or an atom, we rarely call it complex, as quantum mechanics offers us the tools to describe them with remarkable accuracy. The demystification of crystals-highly regular networks of atoms and molecules-is one of the major success stories of twentieth-century physics, resulting in the development of the transistor and the discovery of superconductivity. Yet, we continue to struggle with systems for which the interaction map between the components is less ordered and rigid, hoping to give self-organization a chance." (Albert-László Barabási, "Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life", 2002)

"This is the classic epistemological puzzle of modeling, a puzzle which arises when models are contrasted with the realist ideal for a scientific theory. Good theories are true, or veridically represent the world, and it is in virtue of this representation that they succeed in explaining natural phenomena. In contrast, models (frequently) fail to veridically represent the causal structure of the world, so how can they explain? A realist strategy for resolving this puzzle might attempt to justify the impoverished representational features of models, perhaps beginning with an analysis of the explan atory properties of idealizations, or an account of how false models converge on true ones." (Alistair M C Isaac, "Modeling without representation", Synthese Vol. 190 (16), 2013)

"Pure data science is the use of data to test, hypothesize, utilize statistics and more, to predict, model, build algorithms, and so forth. This is the technical part of the puzzle. We need this within each organization. By having it, we can utilize the power that these technical aspects bring to data and analytics. Then, with the power to communicate effectively, the analysis can flow throughout the needed parts of an organization." (Jordan Morrow, "Be Data Literate: The data literacy skills everyone needs to succeed", 2021)

04 August 2016

♟️Strategic Management: Executives (Just the Quotes)

"Every business has its own particular sort of rat holes, through which its profits are carried piecemeal, and in quantities hardly noticeable at the time, but which aggregate thousands every year. The best way to plug these sources of loss is by accumulating data in regard to them and then keeping this data prominently before the executive."  (Allan C Haskell, "How to Make and Use Graphic Charts", 1919)

"Business executives cannot afford to ignore the merits of graphical representation which have for so long been accepted by the engineer and man of science. They must look behind the graphical method and study the conditions leading to the picture along with the picture itself. No business is too small to profit by an examination which shall analyze and scrutinize nor too large to ignore its possibilities. Each business must adjust the graphical methods to its own peculiarities and each diagram must be adjusted to the individual for whom it is prepared or the individual must be educated up to the use and importance of these methods of analysis." (William C Marshall, "Graphical methods for schools, colleges, statisticians, engineers and executives", 1921)

"The fine art of executive decision consists in not deciding questions that are not now pertinent, in not deciding prematurely, in not making decision that cannot be made effective, and in not making decisions that others should make. Not to decide questions that are not pertinent at the time is uncommon good sense, though to raise them may be uncommon perspicacity. Not to decide questions prematurely is to refuse commitment of attitude or the development of prejudice. Not to make decisions that cannot be made effective is to refrain from destroying authority. Not to make decisions that others should make is to preserve morale, to develop competence, to fix responsibility, and to preserve authority.
From this it may be seen that decisions fall into two major classes, positive decisions - to do something, to direct action, to cease action, to prevent action; and negative decisions, which are decisions not to decide. Both are inescapable; but the negative decisions are often largely unconscious, relatively nonlogical, "instinctive," "good sense." It is because of the rejections that the selection is good." (Chester I Barnard, "The Functions of the Executive", 1938)

"Centralized controls are designed to ensure that the chief executive can find out how well the delegated authority and responsibility are being exercised." (Ernest Dale, "Management: Theory and practice", 1965)

"In large-scale organizations, the factual approach must be constantly nurtured by high-level executives. The more layers of authority through which facts must pass before they reach the decision maker, the greater the danger that they will be suppressed, modified, or softened, so as not to displease the 'brass"' For this reason, high-level executives must keep reaching for facts or soon they won't know what is going on. Unless they make visible efforts to seek and act on facts, major problems will not be brought to their attention, the quality of their decisions will decline, and the business will gradually get out of touch with its environment." (Marvin Bower, "The Will to Manage", 1966)

"As in war, strategic success depends on tactical effectiveness, and no degree of planning can lessen management's tactical imperatives. The first responsibility of the executive, anyway, is to the here and now. If he makes a shambles of the present, there may be no future; and the real purpose of planning - the one whose neglect is common, but poisonous - is to safeguard and sustain the company in subsequent short-run periods." (Robert Heller, "The Naked Manager: Games Executives Play", 1972)

"How executives plan or what numbers they choose doesn't count; what does is the standard of performance they are ready to exact. The essence of any objective is that reaching it should be reasonable. The precondition is that you expect it to be met." (Robert Heller, "The Naked Manager: Games Executives Play", 1972)

"The dogma of delegation is simple - the Sixth Truth of Management again: either the delegatee is capable of running the operation successfully by himself or he isn't. This handy formula relieves the top executive of any responsibility except that of finding, supervising, and (at the appropriate time) moving the men who are doing all the work. He Can then truly manage by exception: he does not get worked up over operations that are going well, but concentrates on the plague spots, where everything, including the management, is going badly." (Robert Heller, "The Naked Manager: Games Executives Play", 1972)

"Management theory is obsessed with risks. Top executives bemoan the lack of risk-taking initiative among their young. Politicians and stockholders are advised (by directors) to make directors rich, so that they can afford to take risks. Theorists teach how to construct decision trees, heraldic devices of scientific management; and how to marry the trees with probability theory, so that the degree of risk along each branch (each branch and twig representing alternative results of alternative courses of action) can be metered. But the measuring is spurious, and, anyway, the best management doesn't take risks. It avoids them. It goes for the sure thing.(Robert Heller, "The Naked Manager: Games Executives Play", 1972)

"The acceptance of project management has not been easy, however. Many executives are not willing to accept change and are inflexible when it comes to adapting to a different environment." (Harold Kerzner, "Project Management", 1979)

"[Organizational] change is intervention, and intervention even with good intentions can lead to negative results in both the short and long run. For example, a change in structure in going from application of one theory to another might cause the unwanted resignation of a key executive, or the loss of an important customer. [...] the factor of change, acts as an overriding check against continual organizational alterations. It means that regardless of how well meant a change is, or how much logic dictates this change, its possible negative effects must be carefully weighed against the hoped-for benefits." (William A Cohen, "Principles of Technical Management", 1980)

"Superordinate goals - the goals above all others [..] play a pragmatic role by influencing implementation at the operational level. Because an executive cannot be everywhere at once, many decisions are made without his knowledge. What superordinate goals do, in effect, is provide employees with a "compass" and point their footsteps in the right direction [... to] independent decisions." (Richard T Pascale & Anthony G Athos, "The Art of Japanese Management", 1981)

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

"Management: The definition that includes all the other definitions in this book and which, because of that, is the most general and least precise. Its concrete, people meaning - the board of directors and all executives with the power to make decisions - is no problem, except for the not-so-little matter of where to draw the line between managers who are part of 'the management' and managers who are not. (Robert Heller, "The Pocket Manager", 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)

"With a vision, the executive provides the all-important bridge from the present to the future of the organization." (Warren G Bennis, "Beyond Leadership: Balancing Economics, Ethics, and Ecology", 1994)

"There is no such thing as a standard enterprise architecture. Enterprise design is as unique as a human fingerprint, because enterprise differ in how they function. Adopting an enterprise architecture is therefore one of the most urgent tasks for top executive management. Fundamentally, and information framework is a political doctrine for specifying as to who will have what information to make timely decisions." (Paul A Strassmann, "The politics of information management: policy guidelines", 1995)

"The Balanced Scorecard has its greatest impact when it is deployed to drive organizational change. [...] The Balanced Scorecard is primarily a mechanism for strategy implementation, not for strategy formulation. It can accommodate either approach for formulating business unit strategy-starting from the customer perspective, or starting from excellent internal-business-process capabilities. For whatever approach that SBU senior executives use to formulate their strategy, the Balanced Scorecard will provide an invaluable mechanism for translating that strategy into specific objectives, measures, and targets, and monitoring the implementation of that strategy during subsequent periods." (Robert S Kaplan & David P Norton, "The Balanced Scorecard", Harvard Business Review, 1996)

"Without meaningful data there can be no meaningful analysis. The interpretation of any data set must be based upon the context of those data. Unfortunately, much of the data reported to executives today are aggregated and summed over so many different operating units and processes that they cannot be said to have any context except a historical one - they were all collected during the same time period. While this may be rational with monetary figures, it can be devastating to other types of data." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"Project failures are not always the result of poor methodology; the problem may be poor implementation. Unrealistic objectives or poorly defined executive expectations are two common causes of poor implementation. Good methodologies do not guarantee success, but they do imply that the project will be managed correctly." (Harold Kerzner, "Strategic Planning for Project Management using a Project Management Maturity Model", 2001)

"We all would like to know more and, at the same time, to receive less information. In fact, the problem of a worker in today's knowledge industry is not the scarcity of information but its excess. The same holds for professionals: just think of a physician or an executive, constantly bombarded by information that is at best irrelevant. In order to learn anything we need time. And to make time we must use information filters allowing us to ignore most of the information aimed at us. We must ignore much to learn a little." (Mario Bunge, "Philosophy in Crisis: The Need for Reconstruction", 2001)

"Organizations face challenges of all kinds after activating their new systems. To be sure, these challenges are typically not as significant as those associated with going live. Still, executives and end users should never assume that system activation means that everyone is home free. Systems are hardly self-sufficient, and issues always appear." (Phil Simon, "Why New Systems Fail: An Insider’s Guide to Successful IT Projects", 2010)

"[Executives] make decisions based on delusional optimism rather than on a rational weighting of gains, losses, and probabilities. They overestimate benefits and underestimate costs. They spin scenarios of success while overlooking the potential for mistakes and miscalculations. As a result, they pursue initiatives that are unlikely to come in on budget or on time or to deliver the expected returns - ​​​​​​or even to be completed." (Daniel Kahneman, "Thinking, Fast and Slow", 2011)

"Any chief executive who hires a consultant to give them strategy should be fired." (Duff McDonald, "The Firm", 2014)

"Most discussions of decision making assume that only senior executives make decisions or that only senior executives' decisions matter. This is a dangerous mistake. Decisions are made at every level of the organization, beginning with individual professional contributors and frontline supervisors. These apparently low-level decisions are extremely important in a knowledge-based organization." (Zach Gemignani et al, "Data Fluency", 2014)

"In order to cultivate a culture of accountability, first it is essential to assign it clearly. People ought to clearly know what they are accountable for before they can be held to it. This goes beyond assigning key responsibility areas (KRAs). To be accountable for an outcome, we need authority for making decisions, not just responsibility for execution. It is tempting to refrain from the tricky exercise of explicitly assigning accountability. Executives often hope that their reports will figure it out. Unfortunately, this is easier said than done." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

"Strategic coherence is more important than strategic precision in an uncertain world. It is impossible to get everything right because of market volatility, but we can ensure strategies do not collide. In large, complex organizations where many executives are empowered to launch major change, strategic incoherence can be a big problem." (Paul Gibbons, "The Science of Successful Organizational Change",  2015)

"Along with the important information that executives need to be data literate, there is one other key role they play: executives drive data literacy learning and initiatives at the organization." (Jordan Morrow, "Be Data Literate: The data literacy skills everyone needs to succeed", 2021)

"Standardization enables delegation of authority, allowing the top management and executives to have time to think about future plans and policy, which is their most important duty." (Kaoru Ishikawa)

24 June 2016

♜Strategic Management: SWOT Analysis (Definitions)

"A scan of the business environment to identify the organization's strengths and weaknesses and the opportunities and threats it faces." (Teri Lund & Susan Barksdale, "10 Steps to Successful Strategic Planning", 2006)

"A method that enables companies to view strengths, weaknesses, opportunities, and threats together." (Annetta Cortez & Bob Yehling, "The Complete Idiot's Guide® To Risk Management", 2010)

"A planning method used to evaluate the strengths, weaknesses, opportunities, and threats involved in a particular strategic direction for your business." (Gina Abudi & Brandon Toropov, "The Complete Idiot's Guide to Best Practices for Small Business", 2011)

"Involves the evaluation of strengths and weaknesses, which are internal factors, and opportunities and threats, which are external factors." (Linda Volonino & Efraim Turban, "Information Technology for Management 8th Ed", 2011)

"Method of studying and identifying an organization's strengths, weaknesses, opportunities, and threats." (Leslie G Eldenburg & Susan K Wolcott, "Cost Management" 2nd Ed, 2011)

"A problem-solving or decision analysis technique in which strengths, weaknesses, opportunities, and threats to the project or organization are examined." (Bonnie Biafore & Teresa Stover, "Your Project Management Coach: Best Practices for Managing Projects in the Real World", 2012)

"An analysis process highlighting strengths, weaknesses, opportunities, and threats to an entity." (Joan C Dessinger, "Fundamentals of Performance Improvement" 3rd Ed, 2012)

"An input into a strategic-planning process defined by Michael Porter. It focuses on identifying an organization’s strengths, weaknesses, opportunities, and threats." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"Analysis of strengths, weaknesses, opportunities, and threats of an organization, project, or option." (For Dummies, "PMP Certification All-in-One For Dummies, 2nd Ed.", 2013)

"An analysis of the company’s strengths and weaknesses compared to the opportunities and threats in the market place." (Pamela Schure & Brian Lawley, "Product Management For Dummies", 2017)

"A technique that examines a project from each of the strengths, weaknesses, opportunities, and threats perspectives." (Cate McCoy & James L Haner, "CAPM Certified Associate in Project Management Practice Exams", 2018)

11 June 2016

♜Strategic Management: Resilience (Definitions)

"The ability to recover from challenges or to overcome obstacles. In a social-ecological context this refers to the innovation capacity of the organization to successfully address societal and environmental challenges." (Rick Edgeman & Jacob Eskildsen, "Social-Ecological Innovation", 2014)

"The quality of being able to absorb systemic 'shocks' without being destroyed even if recovery produces an altered state to that of the status quo ante." (Philip Cooke, "Regional Innovation Systems in Centralised States: Challenges, Chances, and Crossovers", 2015)

"The ability of an organization to quickly adapt to disruptions while maintaining continuous business operations and safeguarding people, assets, and overall brand equity. Business resilience goes a step beyond disaster recovery, by offering post-disaster strategies to avoid costly downtime, shore up vulnerabilities, and maintain business operations in the face of additional, unexpected breaches." (William Stallings, "Effective Cybersecurity: A Guide to Using Best Practices and Standards", 2018)

"A capability to anticipate, prepare for, respond to, and recover from significant multi-hazard threats with minimum damage to social well-being, the economy, and the environment." (Carolyn N Stevenson, "Addressing the Sustainable Development Goals Through Environmental Education", 2019)

"The ability of a project to readily resume from unexpected events, threats or actions." (Phil Crosby, "Shaping Mega-Science Projects and Practical Steps for Success", 2019)

"The ability of an infrastructure to resist, respond and overcome adverse events" (Konstantinos Apostolou et al, "Business Continuity of Critical Infrastructures for Safety and Security Incidents", 2020)

"The capacity to respond to, adapt and learn from stressors and changing conditions." (Naomi Borg & Nader Naderpajouh, "Strategies for Business Sustainability in a Collaborative Economy", 2020)

"The word resilience refers to the ability to overcome critical moments and adapt after experiencing some unusual and unexpected situation. It also indicates return to normal." (José G Vargas-Hernández, "Urban Socio-Ecosystems Green Resilience", 2021)

"Operational resilience is a set of techniques that allow people, processes and informational systems to adapt to changing patterns. It is the ability to alter operations in the face of changing business conditions. Operationally resilient enterprises have the organizational competencies to ramp up or slow down operations in a way that provides a competitive edge and enables quick and local process modification." (Gartner)

[Operational resilience:] "The ability of an organization to absorb the impact of any unexpected event without failing to deliver on its brand promise." (Forrester)

[Business resilience:] "The ability to thrive in the face of unpredictable events and circumstances without deteriorating customer experience or sacrificing the long-term viability of the company." (Forrester)

06 June 2016

♜Strategic Management: Risk Transfer/Transference (Definitions)

"Shifting currently or potentially risky activities to another company." (Annetta Cortez & Bob Yehling, "The Complete Idiot's Guide® To Risk Management", 2010)

"A form of risk treatment involving the agreed distribution of risk with other parties" (David Sutton, "Information Risk Management: A practitioner’s guide", 2014)

"A risk response strategy whereby the project team shifts the impact of a threat to a third party, together with ownership of the response." (Project Management Institute, "The Standard for Portfolio Management 3rd Ed.", 2012)

"Transferring all or part of the cost of a risk to a third party (most commonly an insurance provider)." (Mark Rhodes-Ousley, "Information Security: The Complete Reference" 2nd Ed., 2013)

"One of the risk treatment options is to transfer the risk to or to share it with a third party. Transferring or sharing the risk, however, does not change ownership of the risk, which remains with the organisation itself, regardless of who else shares the risk." (David Sutton, "Information Risk Management: A practitioner’s guide", 2014)

"Project team shifts the impact of a threat to a third party together with ownership of the response." (Cate McCoy & James L Haner, "CAPM Certified Associate in Project Management Practice Exams", 2018)

"A form of risk treatment involving the agreed distribution of risk with other parties." (ISO Guide 73:2009). 

♜Strategic Management: Control (Definitions)

"The process of comparing actual performance with planned performance, analyzing variances, evaluating possible alternatives, and taking appropriate corrective action as needed." (Timothy J  Kloppenborg et al, "Project Leadership", 2003)

"Comparing actual performance with planned performance, analyzing variances, assessing trends to effect process improvements, evaluating possible alternatives, and recommending appropriate corrective action as needed." (Cynthia Stackpole, "PMP® Certification All-in-One For Dummies®", 2011)

"Controls set out how you propose to stick to your plan in the face of the challenges of the real world, and what you will do when reality forces your project to deviate from plan." (Mike Clayton, "Brilliant Project Leader", 2012)

"The power to direct the management and policies of a business enterprise." (Mark L Zyla, "Fair Value Measurement", 2012)

"As per the IIA definition, any action taken by the management, the board, and other parties to manage risk and increase the likelihood that established objectives and goals will be achieved. Management plans, organizes, and directs the performance of sufficient actions to provide reasonable assurance that objectives and goals will be achieved." (Sally-Anne Pitt, "Internal Audit Quality", 2014)

"Controls can be strategic, tactical or operational. Strategic controls are very high level, such as risk avoidance, transfer, reduction and acceptance. Tactical controls determine a general course of action, such as detective, preventative, corrective and directive Operational controls determine the actual treatment, such as technical or logical, procedural or people and physical or environmental." (David Sutton, "Information Risk Management: A practitioner’s guide", 2014)

"Safeguards used to minimize the impact of threats." (Manish Agrawal, "Information Security and IT Risk Management", 2014)

"Actions or changes put in place to reduce a weakness or potential loss. A control is also referred to as a countermeasure." (Weiss, "Auditing IT Infrastructures for Compliance" 2nd Ed, 2015)

"Safeguard that is put in place to reduce a risk, also called a countermeasure." (Adam Gordon, "Official (ISC)2 Guide to the CISSP CBK" 4th Ed., 2015)

"Manual or automated mechanisms to ensure events or activities are tracked and potentially limited in their scope or impact." (Gregory Lampshire, "The Data and Analytics Playbook", 2016)

"A measure that is modifying risk." (ISO Guide 73:2009)

"Means of managing a risk, ensuring that business objectives are achieved, or ensuring that a process if followed" (ITIL)

05 June 2016

♜Strategic Management: Risk Analysis (Definitions)

"The evaluation, classification, and prioritization of risks." (Sandy Shrum et al, "CMMI®: Guidelines for Process Integration and Product Improvement", 2003)

"The process of identifying, characterizing, and prioritizing risks." (Richard D Stutzke, "Estimating Software-Intensive Systems: Projects, Products, and Processes", 2005)

"The process of assessing identified risks to estimate their impact and probability of occurrence (likelihood)." (Tilo Linz et al, "Software Testing Practice: Test Management", 2007)

"The process of measuring and analyzing the risks associated with financial and investment decisions. Risk refers to the variability of expected returns (earnings or cash flows)." (Jae K Shim & Joel G Siegel, "Budgeting Basics and Beyond", 2008)

"The process of assessing identified risks to estimate their impact and probability of occurrence (likelihood)." (Requirements Engineering Qualifications Board, "Standard glossary of terms used in Requirements Engineering", 2011)

"A formal definition of risks based on asset identification, threat enumeration, and consequence evaluation." (Mark Rhodes-Ousley, "Information Security: The Complete Reference, Second Edition, 2nd Ed.", 2013)

"Systematic use of available information to determine how often specified events may occur and the magnitude of their likely consequences." (Chartered Institute of Building, "Code of Practice for Project Management for Construction and Development" 5th Ed., 2014)

"The process to comprehend the nature of risk and to determine the level of risk." (David Sutton, "Information Risk Management: A practitioner’s guide", 2014)

"A process undertaken to comprehend the nature of risk and to determine the level of risk." (William Stallings, "Effective Cybersecurity: A Guide to Using Best Practices and Standards", 2018)

"The process to comprehend the nature of risk and to determine the level of risk" (ISO Guide 73:2009). 

"The process of assessing identified project or product risks to determine their level of risk, typically by estimating their impact and probability of occurrence (likelihood)" (ISTQB)

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Koeln, NRW, Germany
IT Professional with more than 25 years experience in IT in the area of full life-cycle of Web/Desktop/Database Applications Development, Software Engineering, Consultancy, Data Management, Data Quality, Data Migrations, Reporting, ERP implementations & support, Team/Project/IT Management, etc.