Business Ethics Text-book for Indian students – Preface

Yours truly, the freelancer is collaborating with a senior professor to write a text-book on Business Ethics for Indian students. The idea came while the learned Professor brought my attention to the poor quality of textbooks (in most cases) that students are condemned to read. Here is the preface of that yet to be written book :

Young students who study business courses in India are generally young and the core objective for them to enroll into these courses (sometimes quite expensive) is to get a better job or to borrow from Mill – to be in a better station in life. The methodology deployed to teach ‘core business subjects’ like Marketing, Organizational Dynamics, and Corporate Finance are taught cannot be used in toto to teach Business Ethics effectively and in an interesting manner.

The purpose of this textbook, if is to be told in the most simple manner is to make the topic interesting and engaging for students of a particular age, background and cultural context.

The methodology followed in the book tries to make the study of Business Ethics as a lively and exciting thing and also to demonstrate to the students that ancient teachers and writers of Ethics were not dealing with some ‘dead and dated’ issues but something which are just dressed in contemporary dresses. As soon as we remove the veneer of our contemporary time, we find them remarkably similar to the issues raised and discussed by the thinkers, centuries and sometimes millennia ago. This discovery itself can cause sufficient delight in the mind of a young student.

In most of the surveyed textbooks, we find that the book talks about history of Ethics more like a historical commentary to be remembered rather than an integral part of business since a time long back where both business and ethics can trace their origin.

            It is also surprising that in many core business topics, case studies and debate are common but most of the Business Ethics textbooks appear to have remained in a pedagogical time-warp where a young student cannot be too much chastised for declaring the whole subject as some kind of ‘benign preaching’.

            It is the secondary purpose of this book to make the subject intellectually exciting for those young students who are just entering life as business professionals or are upgrading their skills to take more responsible positions in their career.

            The book is structured with theory and practice. Selected writings are produced, followed by Cases related to the area the writings cover. The essay can be a pre-class reading and Case Study can be the class-work where groups debate and also do role-play. The teacher’s role will be to contemporize the writing and bring the tension and dilemmas involved. We have followed the content structure of a classic in this subject – Ethical Issues in Business, edited by Thomas Donaldson and Patricia Werhane.

            Another aspect of Business Ethics books for Indian students is being ‘distant’ and ‘alien’ because of lion’s share of the case material being of companies of non-Indian origin. This is not an insurmountable lack for some hard sciences like physics or mathematics. However, for business ethics, the same cannot be said. It is for the simple reason that just like culture has a deep influence on the ways and direction of conduct of business, by implication, culture of a place determines many aspects which are core to the subject of business ethics, both in terms of studying it as a subject and also applying these studies in practice.

The plan is to make the book interesting for young students. Another understated purpose is to have the students get a critical look at the fundamental business concepts – both of theory and practice.

The problem of business ethics is the same what Plato discussed two thousands of years back – ‘Until philosophers are kings of business’

Big Data Guidebook : Chapter VIII : How to prepare for a Job in BIG DATA environment

In the last post, I have told you the tier or job levels this architecture will create. I shall paste here and advise what are the skills you need for each segment

Transformers : This will be the army. Thousands and Hundreds of thousands will be in this segment. They will simply transform data from unstructured to structured or from one format to another. A machine could do this but investment on small bio-computers will be cheaper for the time being. 

You need not do much to be here. This is the age old data entry or transformation. 

Editors : They will edit the structured data as per pre-set guidelines. This resembles the army of sub-editors you see in a newspaper office. A newspaper is actually a product that handles Big Data without knowing it and hence are utterly miserable from suffering from inferiority complex.

Communication and disciplined. Need to be an avid reader of human behaviour

Fitment Architect : This is a small group and they will make abstract models (like children play with plastic blocks) and will visualize as how these blocks fit and change with time. 

Analytically thinking, mathematics, statistics, visualization 

Business Decision Maker : Extremely few – can count in two hands in any organization. They will use this abstract models and 2 dimensional plans and will be able to see the business future. They can be more aptly termed as Data Astrologers. Please see their working process and Process Diagram here . Also see one of the existing job profile advertised sometime back here for such kind of a Data Astrologer

Genetically advantaged, Practical, Adventurous, Happy go lucky, non-egoist, Not Data slave

I shall go deeper into the skills needed for 2 job levels in the next post.

Big Data Guidebook : Chapter VII : The BIG Job Explosion

This chapter is the most important chapter for those who are interested to be near a Job Explosion site or having some benefit to be gathered from the explosion. Please refer to the post explaining the pyramid structure of jobs in case of Big Data. 

There are two kinds of people you should focus while looking (or changing) for a job. First is that which can give you a job. Second is those who have the map with them as where jobs are available. The former are called employers and the latter ones are recruiters / talent hunting organizations. They are fishes of the same pond. 

You are a small fish in this pond who is interested to get into the back of a large fish. You can either do this directly and can ride on the back of smaller fishes. A Big fish is surrounded so much by smaller fishes and fishes like you that unless you know what this Big Fish is looking for, it will break your little body and spirit to approach. 

Hence, one of the ways is to anticipate the mind of the big fish and recruiters / consultant / talent hunt companies specialize in understanding the mind of big fishes.

In 1996, I interviewed a young graduate and he was immediately recruited because he knew ms-word, excel, ppt, basic design and emailing. Excellent fellow !

In 2006, A young fellow was immediately recruited because he knew mail-merge, could handle most of search and gathering information, could write structured emails, could maintain the databases and knew how to handle them meaningfully and wonder of wonder was having knowledge of social networking.

Now, all these skills for a job seeker is considered as basic as a write to alphabet. Everyone, as a ad shows a newborn also knows this things. 

In 2014, the same fellow while being recruited by a small business will be asked whether he can do all of the above and then can be an alchemist, i..e whether he can transmute business leads from all the data flow going on. In essence, he will be asked whether he has access to data of all types, whether he can analyse them and get meaningful information quickly and in a proper format (so that boss can read easily and understand) and finally whether he is able to be stable when the analysis becomes broader and broader in scope. 

This will be the fall out of the Job Explosion.

Unlike Call Centre Job application, there will be more vertical growth available here. I propose the following functions below

Transformers : This will be the army. Thousands and Hundreds of thousands will be in this segment. They will simply transform data from unstructured to structured or from one format to another. A machine could do this but investment on small bio-computers will be cheaper for the time being. 

Editors : They will edit the structured data as per pre-set guidelines. This resembles the army of sub-editors you see in a newspaper office. A newspaper is actually a product that handles Big Data without knowing it and hence are utterly miserable from suffering from inferiority complex.

Fitment Architect : This is a small group and they will make abstract models (like children play with plastic blocks) and will visualize as how these blocks fit and change with time. 

Business Decision Maker : Extremely few – can count in two hands in any organization. They will use this abstract models and 2 dimensional plans and will be able to see the business future. They can be more aptly termed as Data Astrologers. Please see their working process and Process Diagram here . Also see one of the existing job profile advertised sometime back here for such kind of a Data Astrologer

The good news is that unlike Call Centre, vertical movement will be easier and someone joining as transformer can very rapidly go up if he has the attributes and will be noticed more easily. 

In the next post, I shall go deeper into the requirements for rapid growth in the BIG POND of BIG DATA. 

 

 

 

 

 

 

BIG DATA Guidebook – Chapter VI : Concrete BIG DATA talent profile

This is for P and S class of Wordsmith PSEL framework. Since all of you are having the necessary talent and temperament, please read below the profile of a man who can handle BIG DATA the way everyone (and every Government and Financier) wants you to do. His name is Microft (not Microsoft) Holmes. His younger brother did significant work in the domain of Big Data and used his talent in fighting crimes.

The talent profile is complete below. Do not ignore this at all cost. Still, here is the convoluted and compressed summary

All men are specialists. His specialism is omniscience. 

( The adventures of the Bruce-Partington Plans)

 

“You told me that he had some small office under the British government.”

Holmes chuckled.

“I did not know you quite so well in those days. One has to be discreet when one talks of high matters of state. You are right in thinking that he is under the British government. You would also be right in a sense if you said that occasionally he IS the British government.”

“My dear Holmes!”

“I thought I might surprise you. Mycroft draws four hundred and fifty pounds a year, remains a subordinate, has no ambitions of any kind, will receive neither honour nor title, but remains the most indispensable man in the country.”

“But how?”

“Well, his position is unique. He has made it for himself. There has never been anything like it before, nor will be again. He has the tidiest and most orderly brain, with the greatest capacity for storing facts, of any man living. The same great powers which I have turned to the detection of crime he has used for this particular business. The conclusions of every department are passed to him, and he is the central exchange, the clearinghouse, which makes out the balance. All other men are specialists, but his specialism is omniscience. We will suppose that a minister needs information as to a point which involves the Navy, India, Canada and the bimetallic question; he could get his separate advices from various departments upon each, but only Mycroft can focus them all, and say offhand how each factor would affect the other. They began by using him as a short-cut, a convenience; now he has made himself an essential. In that great brain of his everything is pigeon-holed and can be handed out in an instant. Again and again his word has decided the national policy. He lives in it. He thinks of nothing else save when, as an intellectual exercise, he unbends if I call upon him and ask him to advise me on one of my little problems. But Jupiter is descending to-day. What on earth can it mean?

 

 

Chapter V : Big Data Guidebook : The Bottom of the Pyramid

This segment – the L of PSEL framework is the most voluminous and also politically most sensitive. Democratic Governments everywhere is interested in this segment because of the relationship between vote and job creation. The challenge of every government is common now : how to create jobs which do not need much skills, do not need much talent either and can be produced in profuse quantities.

Call-Centre provided that magic wand for sometime. But diminishing marginal return is working and hence this is also losing its sharpness.

For a populous country, Big Data is a big music to policy-makers and Government. The bottom pyramid of the Big Data architecture satisfies the following :

  • It creates opportunity to have BDC – Big Data Converter Army. These people convert un-structured data to structured data
  • It creates a smaller army of analysts who will prune the structured data.
  • Since un-structured data has a large component of different sense data, it will need culturally tuned people and a diverse army
  • A parallel teaching / training industry for the time being

Now for a person in L component who is interested to know how Big Data affects you, you probably have an idea.

Here closes the 4th Chapter of the Big Data purport of Wordsmith.

 

Big Data Guidebook – Chapter IV : The Economics of Big Data

In the 3rd chapter, we have dealt with the issues related to the scientists and the craftsmen. Now we shall deal with the economists, group who are going to gather their and other people’s money with a compelling logic that the following will happen

  • They are going to get more value in tomorrow than doing this elsewhere
  • They will use the coco-nut tree metaphor where you water the tree for 4-5 years and then it provides you many order more packaged water free of cost for some 10-12 years. 
  • They will convince others that this is the next great thing. 

Now, money managers need to have one virtue : they need to have the canine sense of future money. Without this, a money manager and his money will part soon. 

For time being, try to heighten your sense of smell and consider yourself as a money manager. What will be the characteristics

  • More money to be paid at the top few [ High CEO payments and bonuses]
  • Lesser money and more people at the bottom [ Call Centre, Data Entry ]
  • An unstable middle management whose functions will be delegated to lesser level (with time), directly usurped (technological change), ambition from below and demotion from top. 

So, future Big Data Architecture will be guided significantly by the ‘visualization of the big data’ in the mind of the money manager 

  • Few Philosophers and Craftsmen (P of the PSEL framework) who will decide and can visualize Big Data better 
  • A group who will convince and spread – Marketing – a sub-part of the S and E of PSEL
  • Labour whose job will be to structure the unstructured with a little understanding of the overall process. Present army of coders. 

The Money managers run two risks, one systemic and two specific in case of Big Data 

  • Law of diminishing marginal return. This is systemic. When everyone will have a BIG Data Officer (like Information Officer), it is clear that diminishing returns have set.
  • The top may be starved of data by the bottom-army. Since the bottom level will be huge in numbers and top do not have the simple magnitude of numbers, any rebellion (labour unrest, machine rebellion) will be difficult to quell, if organized properly
  • The architecture will depend on the S part of implementing security and capture and this may also rebel and how to take proactive action. 

This closes Wordsmith Purport of the Money Managers with respect to Big Data.

Big Data Guidebook : Chapter III : How advanced aliens handle and manage Big Data

We have closed the second chapter with a sense of hope. Now, we shall follow our PSEL framework and try to see how advanced alien civilizations handle big data. This will help us, especially the scientists (the S of the PSEL) of our framework. 

Let us examine one of the alien relic easily available to us : our own body. No scientist can claim that our body (or body of any living being) is strictly manufactured in this planet. As a matter of fact, from our Big Data framework, we can assert the formation of our body as below:

As soon as the sense of a man interacts with the environment and detects a young and fertile woman, there happens many intermediate steps, slow or fast depending on the clock of the cultural platform, and genetic fluid is being injected into a secured chamber. The process of injection and reception creates extremely pleasant sense data and after nine months or so, another body, very small and frail leaves the chamber and emits audible data not understood by any grammar for a year or so.

Let us now examine how our body-machine is mother-boarded and how it is working.

Our BIg Data scientists need to focus on this alien technology – how our body handles the data generated within without going into runaway condition ?

It is not very easy for humans to easily understand the work of an advanced alien civilization and hence we shall see what modern biology and brain-sciences tell us 

  • Our brain uses a technique which is not pure computational in nature (Roger Penrose)
  • There are states of brain where it vectors and reaches conclusions which seem to be not possible in its normal state. In many cases, these conclusions proved to be true whereas there is no explanation as why these are true ( Discoveries like Benzene Ring, Poincare Formula, Double Helix etc)
  • As for data integrity, our Big Data scientists may note the fact that genetic code, not very large number in no of atoms remains remarkably stable for millions of years [ Schrodinger : What is Life ? ] 

I think I have given enough problem to our scientists and we shall now deal with the next group in the list – The economists.