Welcome to our blog, the digital brainyard to fine tune "Digital Master," innovate leadership, and reimagine the future of IT.

The magic “I” of CIO sparks many imaginations: Chief information officer, chief infrastructure officer , Chief Integration Officer, chief International officer, Chief Inspiration Officer, Chief Innovation Officer, Chief Influence Office etc. The future of CIO is entrepreneur driven, situation oriented, value-added,she or he will take many paradoxical roles: both as business strategist and technology visionary,talent master and effective communicator,savvy business enabler and relentless cost cutter, and transform the business into "Digital Master"!

The future of CIO is digital strategist, global thought leader, and talent master: leading IT to enlighten the customers; enable business success via influence.

Sunday, May 31, 2015

Is Logic Prior to Language or Vice Versa?

Logic is part of the deep universal structure of all languages.

Logic is a thinking process where a result is derived from a thought. Thus, logic is a derivative thinking process, and both mathematics and languages are only a tool of expression. We should very much understand the boundaries of the terms we use and how we use them.

Logic began with Aristotelian logic. Aristotle discovered logic, the syllogism by analysis of language. Aristotle was perhaps the first philosopher of language (discovered philosophy of language). If logic was discovered through an analysis of language, logic must be part of the language. Language is larger than logic in that context; it can express illogical and irrational things as well as non-logical or non-propositional things (examples, interrogatives, and commands). When Frege improved Aristotelian logic with an actual mathematical-logical language, the predicate calculus, and quantificational predicate calculus and proved that true/false sentences of language can all be expressed in this propositional calculus, he proved that true/false statements in language are based on logic. Logic is a part of the language, a deep structure of language. Language as narrowly defined by academic linguists requires logic to comply with rules of grammar and syntax, thus, such languages depend upon logic, so logical abilities must exist upstream from every speech act depending on the logical structuring of language parts. Such language depends on logic, not the other way around. Language has a deep structure. All languages have subjects, objects, verbs, tenses, nouns, adjectives, adverbs, connectives, etc., and how you put those features together, the grammar of the language varies widely. Many languages are inflectional, and so endings of words indicate their function or case in the sentence, some are highly non-inflectional, are hence word order dependent. Connectives, which are a part of all languages are logical, if, and, or, not exists in all and helps give the propositional nature of sentences. Even in the area of computing called natural language understanding, sentences in a particular language are given a deeper abstract formulation. You need this propositional or deeper language representation to answer questions about texts. So in short, logic is part of the deep universal structure of all languages.

The answer to the puzzle depends on the meaning you give the word "logic." Formal logic, even reflective informal logic, is very much dependent on language, especially written the language. If you consider the periods following the introduction of written language, they were periods of rapid development of formal logic. Certain aspects of language such as the subject/predicate relationship seem embedded in language and subsequently only reflectively present in formal logic. If on the other hand, "logic" refers instead to unanalyzed elements of semantics or grammar, then the two, language and logic, would be codependent. By logic, it’s not necessarily mean a system of logic such as the formal logic. It logically constructed mind that is imposed on us and defines our way of thinking. The map of mind from which we can't escape. We can't "understand" a contradiction. This kind of logic is tried to be shaped and understood through the different logical systems such as formal logic.

You cannot conceive of a language without context. If we look at all languages, there are certain UNIVERSAL features of all languages, like subjects and objects, verbs, nouns, adjectives, and adverbs. These universal features say something about the world. Verbs describe ACTIONS, something in the world, nouns describe OBJECTS, something in the world, adjectives describe properties of objects and adverbs describe properties or qualities of actions. You cannot conceive of a language without logic, for how would such a language express true/false sentences? You cannot conceive of a natural language without logic, and you certainly cannot conceive of an artificial language like a programming language or a mathematical language like Fregean predicate calculus without logic. It seems that people generally take language are a way that enables it to be talked about like the activities of a machine. This is the descent into technicality and the associated empty talk. Humans are not machines. They are not of the same nature as machines. There, in their essence, an ontological totality and not just a calculating device. For practical purposes, people are modeled as machines, but they are not machines. It might be easier to understand humans as machines, but just because it is easier, it doesn't mean it can be justified.

Logic was discovered, much as we discover mathematical truths. Logic was discovered through an analysis of language. In that sense, language is prior to logic, but more precisely, logic is a PART of language, a deep part of the language that was always there and is discovered by an analysis of language, or doing philosophy of language as Aristotle did to discover the syllogism. You could say that Frege created the quantificational predicate calculus to express true/false statements of language in a mathematical form. But the expression is only possible because logic is embedded in, or a part of the language, a universal part of the language as are subjects and objects, verbs and nouns, true of ALL languages. Other modes of language: using terms, assertions, communication, logic, etc. are derivative from this essential nature. As this is the case, it makes no sense to say that logic is prior to language in any sense because logic is just a mode of language. Worse still, essential language starts disclosing with a contradiction and the contradiction is only later rectified. At its most essential level, language is the disclosing of things from the ontological totality that is our essence. If one wants to find the essential meaning of a term, one must be prepared to go beyond thinking that confines itself to just things; one must be prepared to go to the ontological ground about which that term speaks. In the case of language, we can start from a conventional meaning: language as a method of communicating thoughts. Remember, actions or words are said to be the second order. Thoughts are the first order. However, once thoughts have been accessible to others through language or action, we reach a stage to assess the logic of everything; thoughts, language, actions.

Logic is part of the deep universal structure of all languages. Once thoughts have been accessible to others through language or action, we reach a stage to assess the logic of everything; thoughts, language, actions.

A Learning Mind

 “if I am not young enough to know everything, I am certainly old enough to learn." (Oscar Wilde)

It’s important to be equipped with a learning mind because when you stop learning, you are stagnant; especially at the age of digital, information is only a couple of clicks away, but the knowledge life cycle is significantly shortened. There are so many things we don't know and we want to know them all. Unfortunately, there is never enough time to apply all the things we learn - we can pass some on to others, but we never know what applications will arise. Loving to learn and the passion to share, very much go together. What is a ‘learning mind’ thinking about and how to shape such a digital mind?

Digital learning is emerging: Social Learning is a concept wherein learning happens through networking on the social learning platform, you can learn anytime from anywhere. Such informal learning or virtual learning becomes complementary to formal learning and is emerged as the most popular learning method to make a learning experience more user-tailored and fun. A learning mind is always in the search for new things and a new way to learn! At today’s business dynamic with the information explosion, digital professionals have to adapt to a faster learning method and be truly convinced that online learning is the key option to learn anytime from anywhere and make learning a lifetime habit.

Unlearning and relearning is also a critical part of the learning cycle: It's important to make connections between pieces of knowledge. When these connections are structured in a meaningful way, we are better to retrieve and apply knowledge effectively and powerfully. Learning agility becomes one of the most important capabilities for digital professionals to compete for the future. Seasoned persons in life become aware when some of the long-acquired knowledge is no longer applicable in certain situations. You have learned to no longer apply that knowledge in those specific cases. This is unlearning. And then relearn the updated knowledge for gaining insight on the changing circumstance. Being learning agile means you can proactively manage learning, unlearning and relearning cycle more effectively.

A learning mind is humble and curious: The more one learns, the more one finds the vastness of knowledge, and how minuscule is one's own. Young minds are malleable and more fertile. Their attitude of "know it all" is at the surface which can be erased easily with a new thought. Youth has very little experience, therefore, they have to absorb knowledge, but it takes life experience and profound contemplation to gain wisdom. The mind of the older generation is usually more rigid. Their "know it all" attitude is difficult to erase because they are deep-seated with life's experiences and beliefs. If you only know two colors - Black and White; then perceptions are filtered into one of those two dimensions. You can only know what you know, but there are more dimensions which you don’t know; there’s known unknown and unknown unknown. Hence, the more you know, the more you feel humble, because even human as collective species, the things we know, compared to what we don’t know, is just the tip of iceberg.

Either formal learning or informal learning, learning is the means to the end, not the end. As we grow knowledge is acquired and accumulates within us. Learning is how to use knowledge through experiences. Learning is situational, though. Learning is exciting because your subconscious sees the potential. However, if you go on learning and not applying, your subconscious starts to tell you "ya, but you're not going to actually put this to use." Learning becomes less exciting, and eventually even becomes work. If you can do both, the learning cycle goes the other way, and learning becomes more and more exciting. Each thing you learn becomes potentially the thing to solve your latest failure, the potential secret to your next success!

Learning is a lifelong experience, It depends on who has an open mind to receive it. The future of learning will require us to break away from the current one-size-fits-all mold and move to a more customized learning platform to really encourage people in their strengths and interests. To that end, the current e-learning platforms and social learning concept can be quite advantageous if used correctly and make learning more personalized and flexible to fit your lifestyle. Lifelong learning is no longer a choice, but a necessity to compete for the future.

What Role should HR Play in a Business Continuity Program?

This is key part of Talent Management, a business can not continue without its assets - People.

Talent is always the most invaluable asset of an organization. So for companies that have a centrally managed Business Continuity program, or for those who are thinking of having a centrally managed Business Continuity program, how closely aligned should Human Resources be to the Business Continuity? Should HR be working with Business Continuity in ensuring human capital gaps are identified and hopefully planned out prior to an event?

This is part of Talent Management, a business can not continue without its assets - People: Therefore, it seems like a no-brainer for HR to play a pivotal role in Business Continuity. It is impossible to anticipate every gap and create strategies for every seen and unforeseen scenario, but there must be a solid plan in place for the future and present human capital, both skilled and unskilled so one's organization does not fall prey to the 'lack of talent' that so many sectors are facing. A Business Continuity team would comprise of different key members of the organization, not necessarily any or all are from senior management level, such as Contracts, Finance, Team Leads/Supervisors, Junior staff...etc., and working together with HR, a number of eventualities can be planned away to make sure business is continued, profits are still up and all employees are still getting paid.

It is important that Human Resources along with a cross functional team are involved in the business continuity planning: Human Resources professionals in an organization play an integral role in the creation of continuity plans, and they should be among the core team called on to enact a plan in the event of a disaster. The cross functional team can ensure there is a contingency for employees and any other key resources needed in an emergency. In addition, having all team members participate in exercise will show any gaps in the plan. HR plays a very important role in business continuity. Having plans and procedures in place before something happens is crucial to a business' ability to properly care for its personnel as well as its operations. Crisis preparedness is not new, but there are so many organizations - large and small - fail to plan for the unexpected.

It is also important to understand that HR is not the sole owner of this process: Each plan should reflect the company's specific human resource reserves and structure. Organizational growth is a result of team combined efforts of all the internal and external stakeholder, thereby encompassing the individual and collective input. It is imperative that occasionally the strategies, plans, formalities, and policies may not produce the projected outcome, thereby hampering the achievement of the desired objectives, needless to underrate the fact that the "Human capital" is the fundamental and the foremost driving engine. The desirable tools are needed. Therefore, towards this end, it is the efficient and effective placement and utilization of such tools. This fact is not and should not be 'counted' as the main function of the H.R Department. The role of the HR, goes beyond, to ensure and identify not only the stumble blocks, but also fix responsibilities after careful study of the specific circumstances and the individual and collective professional input. This scenario sufficiently enhances the role of the HR, beyond 'recruitment and performance appraisal.' It brings the HR to assess causes for a professional's default by the concerned and proscribe or subscribe to the "accountability' process. This can only be prudently and judiciously undertaken, if the HR department is professionally competent to intervene and opine on the such phases of the organization's growth. It thus becomes necessary that the HR professional should reach beyond its earlier defined prime role.

Best practices of business continuity program involve anticipating human needs, developing strategies and implementing resources to address these needs. Among the greatest need is timely, accurate information – what has occurred, who is accounted for and how this affects everyone involved. There is a need to communicate with staff, contractors, vendors, visitors, family, friends and the surrounding community. And it is the cross-functional collaboration and execution with long term planning.

Saturday, May 30, 2015

An Anti-digital Mind: An Excessively “Angry” Mind

Anger is not innate, but, it is a learned habit. Humans are not born to be angry.

It’s normal as humans, we have all sorts of emotions, but how you react to them decides your EQ maturity level. From example, according to American Psychology Association, “Anger is an emotion characterized by antagonism toward someone or something you feel has deliberately done you wrong. Anger can be a good thing. It can give you a way to express negative feelings, or motivate you to find solutions to problems. But excessive anger can cause problems. Increased blood pressure and other physical changes associated with anger make it difficult to think straight and harm your physical and mental health.” The point is what antidote are you using to counteract your angry reaction and your habit to pull the trigger?

Anger comes from frustration, impatience, and many other environmental factors. All of those are about control or lack of control. We spend a lot of energy trying to address those things that we cannot control raising frustration, etc. This energy is therefore not used on the lower stressed, less frustrating things that we can control. When the balance of control vs. out of control is significantly unbalanced toward "out of our control," anger is a natural and instinctive reaction. We need to be aware of the balance. Emotions are caused by neuropeptides in the brain which at their root are simply data. What we feel - anger, loathing, joy, love - is the physical manifestation of these neuropeptides. Each emotion has its own neuropeptides. If we nurture these feelings and our subsequent reactions to them, we create automatic behavioral responses. They become habitual responses - what fires together in a neurological sense, wires together.

Anger is not innate, but, it is a learned habit. Humans are not born to be angry. To what extent an individual feels and displays anger is highly dependent upon the extent to which this learned ability has been nurtured over time by beliefs and biases. Try examine those negative thoughts objectively and with detachment "as a cloud floating by." Viktor Frankl said it best when he wrote: "Between stimulus and response there is a space. In that space is our power to choose our response. In our response lies our growth and our freedom. "Everyone sees for themselves when they do reflect on the why and where of the origins the incendiary emotion we call anger. Why do I make myself angry, because I am trying to control what I cannot control other people and events, why am I trying to control other people and events, because I am carrying a 'viral belief' (nothing to do with chemicals) that other people and situations are responsible for my happiness. This is why whenever you make yourself angry, irritation and frustration are simply earlier forms of anger! Why because you are trying to control change, what you can never change other people and events. Fortunately, all anger always passes and sanity generally returns. You can't stay angry, try it and you'll start laughing after a while.

People need time to unplug, breathe, understand and label their emotions. Self-awareness is key. Some people will become addicted to the anger itself. But emotional addiction is also not rooted in chemicals. It has an effect on the chemicals in the brain for sure. But the cause is always in consciousness or unconsciousness. But few realize that until they practice some kind of meditation at which point the state of consciousness that is capable of being beyond time and space where the brain must always remain. You need to take the time to understand your triggers. You may not always be able to control them, but you can modify actions and respond vs react over time with practice. It is possible to learn new responses to these chemical triggers. The process is not simple or easy or one size fits all. Instead, each individual must work out his or her response through trial and error. When you find something that works, nurture it through practice and repetition and it will form a new habit. What fires together, wires together. The reverse is also true. If you are not repeating behaviors that create poor outcomes, the new behaviors will take their place and the old ones will be culled by the brain through a process called neuroplasticity.

Recharge. Rest. Renew. Know yourself. Learn from experience. Make any necessary change.
The spectrum of anger is an indicator light to circle back around and continue to learn from the whole experience, from self-awareness to self-control, practice patience, listening, observing, acceptance, vulnerability, and forgiveness. It matters that you take action to notice what you are doing in response to what you are experiencing as you are the only person who can change a negative habit. Any learned ability can be unlearned. It is in our power to do so. Rather than looking for the panacea or motivational advice, it can only be unlearned through total commitment. Total commitment requires desire, determination, and discipline. These three essentials are indispensable as well as complimentary to make change sustainable.

The Systems Thinking about ‘Complexity’ vs.‘Complicatedness’

Complicated systems can be simplified, and produce predictable outcomes, but Complex systems are non-linear, hard to predict.

The concept labeled ‘complicatedness’ is often confused with the concept labeled 'complexity.' From the dictionary, ‘complicated’ is difficult to understand. Complexity is not simple, which is composed of various elements and intertwined and interdependent. What’s the further Systems Thinking about a complex system and complicated system?

Complicated systems can be simplified and produce predictable outcomes; but Complex systems has a lot of interdependent relationships, hard to predict. Complicated systems produce predictable outcomes once the starting conditions are defined, even though there are many components and relationships. The elements behave the same way each time activities are set in motion. Complex systems, conversely, will produce differing outcomes although the starting conditions are also defined. Complex systems adjust to multiple stimuli and contain a large number of components, interdependencies, and diverse elements. These aspects behave differently depending on system influences. So Complication can be simplified, there are lots of components, lots of integration, but from a distant, something that can be disassembled or decomposed, and reassembled or recomposed fairly, logically or mechanically. And it will take time and will need expertise, but fundamentally, it is a sequence of lego blocks. Complexity is like mayonnaise, it does not matter how you slice and dice it, it remains mayonnaise. Until you put it through a very different chemical and heating process which unfortunately will not produce the eggs, salt etc that mayonnaise is made from. But will produce more basic or different things like sodium, chlorine, carbon etc. And you won't be able to put that "stuff" back together as mayonnaise without another very different process and a lot of help from "nature."

Both are challenges to management, but each has its own implications and potential results. The idea of complexity is often confused with the term complication. Complicated systems have many interacting parts, but operate in predictable ways. Their functions can be measured and studied to discern patterns that will assist in solving problems and facilitate decision making that produces reliable outcomes. Difficulties can be reduced to simplify solutions. For example, implementing process control practices will be complicated, but the dynamics and mathematics are well understood. The outcomes will, therefore, follow recognizable patterns that will assist in reducing process variability. Efficiencies will be gained because closeness to standards can be maintained. On the other hand, complexity has emergent properties that make operating patterns less predictable because interactions are continually changing. The factors that create such uncertainty are large due to quantity, interdependence, and diversity of components and relationships. So the system is not only complicated but has interdependencies that may not be continually connected. These elements may modify their associations as conditions change. In addition, the relationship between elements may not display a lot of homogeneities. The components are diverse in their make-up and behavior. Each may or may not contain the ability to control its actions and may be dependent on one set of conditions and not under another. For example, automatic control systems are designed to continually adjust as inputs and components change in relationship to one another.

Complexity provides basic explanations of concerning the behavior of complex systems - self-organization, emergence, pattern formation and systems theory. More Systems theory is studying real systems, like societies, nature, psychology - using Systems Thinking, Business Dynamics. Analyzing complex systems using conventional tools can be difficult because these methods assume that observations are independent and that it is possible to extrapolate from averages or medians to the entire population. But because of interconnectivity and the many potential options in a complex system, a small effect one place can cause a cascade of events that produce deceptive output measures that are highly variable; thus producing averages that can be misleading as indicators. Knowing which outliers to exclude can be a challenging call creating questionable models. This is often a problem when trying to predict human behavior before an election or when launching a new product. The feelings, relationships, and influences are complex and can be misread.

Systems Thinking seeks to define an ideal culture, and then define strategies to “close the gap.” Complexity works with the evolutionary potential of the present, it seeks to understand the “now,” find out what can be changed in a measurable way, and then take small evolutionary steps in a more positive direction without any assumption of the end destination. From the management perspective, it’s important to simplify complicated things and manage complexity accordingly. As Systems Thinking guru Russell Ackoff, said: "Managers are not confronted with problems that are independent of each other, but with dynamic situations that consist of complex systems of changing problems that interact with each other.”

Statistics. vs. Data Mining vs. Artificial Intelligence vs. Machine Learning

Statistics, Data Mining, Artificial Intelligence and Machine Learning are all inter-related concepts: Statistics is a field of mathematics. Statistics is at the base of Machine Learning and the application of Machine Learning enhances the field of Data Mining. Data Mining is a step of Knowledge Discovery from Data. Some peoples see Data Mining as a step of Machine Learning, other approaches Machine Learning just to algorithm/ search part of Knowledge Discovery from Data. Artificial Intelligence is an interdisciplinary field of science. "It attempts not just understand but also to build intelligent entities. Machine Learning is Applied Artificial Intelligence (AI). Machine Learning is also an interdisciplinary field of Artificial Intelligence. The Machine Learning target is specifically to develop ways to do a computer "Learning."

AI is not only about learning. AI is also about understanding language, planning, representing and reasoning with knowledge, etc. Statistics is a good tool, but it is still just one tool that is useful in many situations, especially in data mining and thus machine learning. Artificial Intelligence is a lot more, it is about building human-like intelligence. Now surely no one would seriously think that all we humans do is statistics! Surely our intelligence is not all due to some statistical module we have in our brains. We do lot more, we interpret, we do semantics and pragmatics, we reason with concepts and knowledge, we make plans, algorithms and we execute our plans (planning is a huge area in AI), and we adjust our knowledge based on the implications of our actions via framing problem in AI, etc...

Artificial Intelligence is a field whose purposes are creating computational models of natural intelligent systems; it is not necessary human intelligence, and it will apply these models to various real world problems. Of course, the most appealing challenge is to re-create an artificial human intelligence and consciousness. The empirical principles of Artificial Intelligence such as cognition and self-awareness usually limit the perception of intelligence to humans or living creatures. But let's not forget that the Universe itself is the greatest known natural proof of storing information, adaptability and decision making. And the purpose of Universe is not creating life, but developing methods to store more and more information, life and intelligence of life is a collateral effect of all processes in Universe. There is no universally accepted definition of an intelligent natural system. But such a system should have at least three fundamental features: (1). To store information about the experiences it's been through. (2). To process these information in order to adapt itself and (3). To take decisions based on its experience.

Machine learning is a science that involves development of self-learning algorithms in AI. Machine Learning is a field in Artificial Intelligence, dealing with methods to describe the three components of intelligence: Memory, Adaptation, Decision (MAD). All Machine Learning methods have different levels. Typically, the most important application of Machine Learning are pattern recognition (supervised and unsupervised classification) and prediction. -Statistics is the oldest data science. It is now accepted that Statistics and Machine Learning often do answer to the same type of questions, but in very different ways. Statistics uses linear or non-linear parametric models to explain causality and to make predictions, while Machine Learning typically uses non-linear and non-parametric approach who rarely explains causality but instead focus on performance of predictions.

The difference between classic Statistics and Data Mining: Classic Statistics studies small and moderate volumes of data sampled from populations, using asymptotic theory of convergence (hence distribution based methods), while Data mining uses moderate to large volumes of data with no or little parametric assumptions. One can see Data Mining as a continuity of Statistics to large data sets. The most important thing is that the Data Analyst/Scientist/ Researcher to know these level of appliance and to be aware of the most suitable techniques to be chosen for a specific problem and a specific data-set. If this is true, it requires a natural, fundamental and deterministic definition of intelligence. Because otherwise nobody knows what to implement.

Artificial intelligence can be viewed as the ability for a computer to learn and reason. Learning would be generating a hypothesis or output for a certain input data set, while reasoning can be seen as deciding whether or not to act upon those learned hypotheses. Statistics addresses to the study of uncertainty and is largely used in both machine learning and other artificial intelligence fields such as communication, planning and so on. Software learning is "adaptive," it becomes Artificial Intelligence only when the software embodies a partial model of human behavior which improves in accuracy with learning. Not many so called AI meet this requirement. Artificial Intelligence is an interdisciplinary field of science, so it is important to clarify the relevant concepts accordingly.

Friday, May 29, 2015

Is Kindness a Mindset?

Kindness is a state of mind, flows from within, not floating on the surface.
Kindness often starts as a thoughtful mind and deliberate action, but enough of it, and it becomes a habit. The true value of kindness is its consistency, to convey a person’s high quality as a human, the high professionalism as an employee; and the true nature as a creature. You don’t need to be a religious person to show kindness, just like you don’t have to go to college to gain knowledge. Kindness is a state of mind. Kindness can have different styles, it could be as hot as flame, or as cool as ice water; It’s something that you commit to before it becomes second nature which overcomes the negative psychology and drives human world progress.

Metaphorizing it in gardening. You plant an apple seed and you're not going to get an orange tree.
Kindness is exactly the same, it's a seed. Plant a lot of kindnesses and watch what happens to your experience of the world. People struggle with the "gap." That is the time it takes for the seed to germinate and grow. All the great teachers have said the same thing because it's a basic truth. You just can't get an orange tree from an apple seed. So simple…

Kindness is a philosophy that has deep roots in the soul. Real kindness requires thoughtful mind and consistent good behavior; it’s neither envy nor revenge. Being thoughtful takes wisdom, and having consistent action requires commitment. In the environment surrounding with negative cultures, kindness sometimes is being portrayed as a lack of competency or out of date. Though kindness doesn't mean one should give up principles and encourage mediocrity. If you are not committed to the practice of kindness, kindness is more or less a random action instead of a way of being. Nothing is wrong with random acts of kindness. However, imagine the possibilities if the citizens of the world committed to kindness as a way of being.

Kindness goes with compassion and empathy. Like wisdom tends to come from within. Compassion touches the heart, but empathy connects the minds. Certainly, you can practice kindness; however, you cannot really see it curing sociopathy. It's a thoughtful mind and deliberate action, it requires the person to commit to the action and consciously consider the result...just as the same would be required for the antonym of kindness, cruelty, prejudice or evil. Though being kind doesn't mean you can’t make tough decisions or challenge conventional wisdom, if only you think it’s the right choice to sow more seeds of kindness for the long run. 'Committing' to anything which you feel has quality, is the first step to developing or uncovering a sincere appreciation.

Kindness flows from within, not just floating on the surface: Kindness is a mindset that has to be put into practice on a daily basis in decision making before it becomes natural for some people. Each time we act with kindness, we are also giving a gift to ourselves... It feels good to be kind and to believe your kind actions will make a positive difference, not only to the person with whom you're interacting but also in the multiplying effect of your kindness, to make an influence on corporate or societal culture. People who are kind to others should receive the merit and credit for acting or doing something kind as it is more than just a second-nature response, kindness, empathy, and cooperation are innate differentiating characteristics of human beings, and it is a healthy and progressive mindset.

An Organization’s Culture is Extremely Difficult to Change. Why?

Thinking drives behavior which in turn drives results. Culture is the problem at mindset level.

Culture is often defined as “How the group of people thinking and do things here.” Getting all employees on the same line is always difficult because too many mindset are not willing to adapt and accept the reason to change the existing culture. Most want to stay in their established comfort zone. Much effort has gone into changing culture in organizations. For decades, practitioners have been trying various ways and means to achieve culture change. Similarly, decades of research and countless studies have produced millions of books, articles, etc. about culture change, from many perspectives, leading to many different ways of achieving it. An organization’s culture is extremely difficult to change, WHY?

The company has to have one common goal and all should work towards it: To create value for your customers, communication, setting targets, measuring these and leadership are key in the change process. The real company leaders have to lead the process since they can influence the employee. It is also important to remember that among the subgroups of corporate culture, that there are local and global aspects. Changing corporate culture should address not only the subgroups, but each singular value or behavior to be changed, or not changed. For example ethics, internal communications, punctuality, respect etc. Not to be forgotten is the need for empowerment so that the individuals in the organization believe that they can make enduring positive change.

Intercultural communication is the key. Understanding how cultures adapt to change is imperative. There are team, department, company, local, regional, national, cultural differences - for sure. But problems, uncertainties, fear and survival are fairly global -based, so why not get the basics (the 80 percent plus shared "standards") right before you focus on the more individual aspects? Doing that create a valuable shared global platform enabling economies of scale, shared communication, smaller accumulated costs on each area etc. and a much better chance of operating in the global village marked. Isn't that what almost of truly global company is trying to do? Think of how many companies are providing the same products and services and even the same global culture. Collaboration and partnership always starts where you have shared interests and then evolve slice by slice as you find more and more common grounds.

Being humans we should have one big advantage - knowing about our own nature. People have no problem with change! They have problems with uncertainty, risk and fear. If you know where you are in current situation, where you want to go in future situation, how to get from where you are to where you want to go ("travel" plan and its "obstacles as well as the benefits/gains of the "travel"), and then, most people actually accept or even prefer change. It's basic (reptile brain fear/survival) human nature which often is much less complicated than you wish to admit. Might you get unexpected benefits and successes if the effort associated with trying to change something, which is so fundamentally problematic, were redirected somewhere else? Perhaps something as simple as allowing more time for people to talk to each other might reap equal or greater rewards. People can be difficult, by nature or nurture, good leadership skills can be used to develop people within organizations to generate awareness and influences of the culture.

There are many different perspectives of culture are presented along with diverse ways and means of dealing with it. With all this effort, one might have thought that the problem of culture change would be reducing. One might also have thought by now we would have a clear and universally agreed means of measuring culture change and its success or otherwise. This isn't the case. The daily appearance of posts, articles and so on about culture change clearly demonstrates that it continues to be a problem and also the increasing diversity of views about how to deal with it. When the problem of culture change is raised, the response to the question is always very similar; many different perspectives are presented along with diverse ways and means of dealing with it. In these responses, there is a tendency towards diversity rather than consensus. Perhaps this indicates there is a problem with learning and practical application in relation to all these perspectives and approaches. 

Thinking drives behavior which in turn drives results. So prioritizing and investing in how people think about the future and why change is smart and right, remains a really good place to start, you could say that is a critical "upwards" place of culture influence.

CIO as Chief Investment Officer: How Much do you Trust your Budget Estimates?

The problem with complex problems is that they are open, with diverging views among stakeholders.

The industry study suggests that half of all large IT projects defined as those with initial price tags exceeding $15 million, massively blow their budgets. On average, large IT projects run 45 percent over budget and 7 percent over time, while delivering 55+ percent less value than predicted. So CIO as IT and business leader: How much do you trust your budget estimates? To which degree, can you say you actually have sort of method or approach that would enable you to systematically deliver proper (right, accurate, etc) estimates?

There is a clear difference between a budget and an estimate. Estimates are always ranges and are but one consideration in developing a budget. A budget is a single number used for tracking progress. But you first need to secure a proper budget, or you'll be in risk of adding to the failure statistics. Estimates are one thing. Budgets are another. Your terminology confuses the issue and thus increases the probability of poor estimates. Research shows that high underestimation rates are not due to lack of reasoning skills, but actually a bias, the same bias that makes you successful in the most routine task, but that fails to separate wrong gut estimates from reasoned, well - based statistical procedures.

Developing cost and duration estimates for a project is a challenge. But it is easier if you recognize that estimates are but one input to the budgeting process. And it's easier still if you have a proper project life-cycle and descent systems architecture. The real issue is the lack of validity of claims about the possibility of predicting the cost in a systematic way for really complex problems. Is it more information really useful regarding how much costs estimates you will need? Regardless the tool you choose for the estimate and the information you are provided (be it too much or too little), your estimate for complex problems will be nothing else than a guess backed by the tools and the fact that nobody else knows how much it should cost. Many would say, "if we had a full vision of what´s in the path for us, we could build much better estimates." But you cannot have a full vision of a large complex project from the start. It has never been possible to have that. It will never be possible to have that. So asking for it is unrealistic. But you can develop a full vision in steps with a well-defined project life-cycle.

Agile budget planning needs to be “hybrid” to mix top-down estimate with bottom-up adjustment. For many years, IT organizations know that complex projects should be broken up into smaller pieces, the phases of a project life-cycle, and sub-projects whose main purpose is to provide proof-of-concept or to otherwise develop a better understanding of the unknowns. However, Agile budgeting needs to take a mixed approach, besides bottoms-up adjustment, the top-down approach means that you determine how much you are willing to invest in solving a problem or delivering a capability, and then you deliver against that until enough of the problem is solved to satisfy your needs or the money runs out, at which point you have gotten your highest priority needs met for the money that you had. Many great organizations already deliver accurate estimates consistently, even in software development. It requires:
* A well-defined project life-cycle approach (this can include agile)
* Well-defined scope, consistent with what's possible for the current phase (or iteration)
* Reviewing the estimates at the end of each phase (or iteration)
* The willingness to spend some time learning how to estimate
* The willingness to spend some time developing the estimates
*The willingness to defend your estimates when subjected to political pressure

The structure of much of budget estimating for big IT projects will tend to under-estimate the time and cost.  if you had a full vision of what´s in the path for the complex problem solving, you could build much better estimates. The problem with complex problems is that they are open, with diverging views among stakeholders. Some stakeholders will not see a problem at all. You estimate the time and cost for all the project activities that you can think of and include a consideration of the interconnections. However, there are almost always things you did not consider or problems that pop up. In a sense, a critical path is a shortest possible time to completion, not the expected time to completion. All you need is a number of stages each with some probability of unanticipated problems to end up with some unanticipated problems being almost a certainty. So generally speaking, you can systematically define and estimate, thus plan any problem, project, and phenomenon, regardless of its size, but not always from the start. It has to be done in pieces. You have to recognize that you can't predict the final total with much accuracy at the start. And if you are in IT, you have to recognize that this phenomenon is NOT unique to large, complex IT projects. It is true for large, complex projects in EVERY domain.

The facts say that most big projects are losing budgetary grip and that the estimates didn´t take into account those slippages at estimation time. In hindsight, we will always find an explanation for the failure. The point is how to learn from the failure. You are not fighting against the lack of skills or best practices or intelligence but against your own lack of human ability to avoid your biases and to question if the best practices, concepts, and tools you used yesterday are applicable today. And it is the time to reflect and develop your next budget management practices to make continuous improvement.

Thursday, May 28, 2015

How to Map Agile with Business Capability

The value of capability mapping is to provide the useful tool to align products with business vision, or at the higher level, to visualize the enterprise.

The vision of the company direction, and therefore the direction of the different products, services, the functionality of business processes, and the ability of people etc. is generally expressed as business capabilities. Portfolio decisions should be made in the context of those capabilities. Epics, stories, features should all trace to the business capabilities. This not only gives you a better view of how and what you're doing to build out the system, it also allows you to better communicate on a regular basis of the benefits, the level of maturity, and other aspects of the system to those who care.

The asset stewardship is the ongoing work of the business to describe the vision and direction of products: The exact business capability needed and where in the business process should it reside, then drives the application; this capability should be put in and ultimately the technical architecture that drives its implementation. The business side is independent of a particular project or work effort, think of that as Asset Stewardship, and it is the ongoing work of the business to describe the vision and direction of a product, service offering, capital asset, etc. within the overall vision of the company. This is not what Agile thinks of as Product Owner. It is also not Portfolio Management. Asset Stewardship is considering the needs of today in the context of a 3-5 year vision that is not locked in stone but that does provide a direction.

Use the Objective/Key Results format for business capability mapping. Show how implemented features relate to business goals. Investment themes are used to categorize development by internal investment types. Use these to answer questions such as: How much effort did you spend this year on development for existing customers vs new market growth vs sustaining. The objective describes the business level problem you are trying to solve. For example: “Reduce cycle time for workflow X in order to increase revenue per seat.” The key results describe expected business results that will be delivered and when. For example: “Increase revenue per seat by 10% by EOY.”

You can also create data flow diagrams showing business goals, expressed as high-level processes; and then, to the degree, if detail necessary, decompose these business goals into subgoals, and then into supporting processes or projects. The good mapping mechanism allows you to create a capability model or some backlog of capabilities that you can then tie to specific features or even better, to the story level. This way, as you deliver stories, functionality, features, you can tie them directly back to the specific capabilities that are cared about by your customers (external or internal). Sometimes (depending on size), use Epics (WHY) -> Story (WHAT) -> Subtasks (HOW) where an Epic unifies all work needed for a singular business outcome and the collection of business outcomes within a release to denote the capability being built.
- release notification notes the specifying impact on the value streams/business areas
- motivation to understand the business, so you can speak the same language
- there is an incremental log of functionality mapped to the version and existing business process.

Therefore, the value of capability mapping is to provide the useful tool to align products with business vision, or at a higher level, to visualize the enterprise. It allows business leaders or Agile managers to establish important business goals or products/projects initiatives relevant to the capability and identify goal dependencies to both doing the right things and doing things right, and ultimately establish a high mature enterprise.