Hi Friends,

Even as I launch this today ( my 80th Birthday ), I realize that there is yet so much to say and do. There is just no time to look back, no time to wonder,"Will anyone read these pages?"

With regards,
Hemen Parekh
27 June 2013

Now as I approach my 90th birthday ( 27 June 2023 ) , I invite you to visit my Digital Avatar ( www.hemenparekh.ai ) – and continue chatting with me , even when I am no more here physically

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Sunday, 6 November 2016

The nth Richest Man

The   nth   Richest   Man

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On 04 Nov 2016 , LinkedIn came out with a feature to enable Jobseekers to reveal their Pay Potential


We  had introduced a similar feature in www.IndiaRecruiter.net , some 10 years ago , based on my following note 

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Note dated  17  April 2006


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Rahul / Saurabh / Pranav / Vikram / Rajeev :


What beauty is to women , salary ( wealth ) is to men

A person is neither beautiful nor rich , in absolute terms . It is always " relative to " some other person or a group of persons

But , mankind has an obsession with such relative comparisons . It is in human nature to compare oneself with others - especially comparing oneself with " comparable / similar " others ( not just anybody )

This obsession manifests itself in ,


#  Beauty Contests for women ,

eg:  Miss India  /  Miss  World  /  Miss Universe ...etc

This , despite the fact that there are no " Objective  /  Quantitative   /  Scientific  " measures ( units of measurement ) for " Beauty "

A woman may appear " Extraordinarily Beautiful " to one man and just " Ordinary " to another !

As they say :

" Beauty is in the eyes of the beholder "

Despite such enormous " Subjectivity " , millions allow themselves to get swayed by the  " announcements / proclamations " of a few well-known / eminent " CRITICS / JUDGES " !


#  " Rich Man / Wealthy Man " ranking for Men

These " Rankings " are somewhat more " Objective " - based on measurable criteria

Of course , there are no " contests _ - at least , visible contests

But you bet that Azim Premji / Narayan Murthy / Anil Ambani etc , are all anxiously scanning newspapers / magazines every morning to see if Business Week / Financial Express etc , have just published their ( respective ) lists of ,
INDIA's  RICHEST  MEN

It is quite likely that BW & FE may be using somewhat slightly different criteria - but that difference is irrelevant

What is relevant is that both use " Objective / Measurable , and often even Transparent " criteria

And what is more , they use the SAME criteria consistently , year after year . So , even if Azim Premji may disagree with Business Week's " definition of Wealth " , he knows that ,

*  Same definition is applied to all others

*  Same definition will be used NEXT year


What then matters is the ,

RELATIVE  RANK

Is he moving UP or DOWN ?

By how much ?

Who is overtaking him ?.....etc

Not one industrialist / businessman has ever challenged these " ranking " ! - even if he believes that the " method " is somewhat defective , from his point of view

Why ?

All those being " ranked " , trust the

*   Objectivity

*   Fairness

*   Consistency

*   Transparency ,


of the method .


And it is precisely because of absolute " Lack of Bias " on the part of the Ranking Organization / Institution / Agency , that the organization has great CREDIBILITY

And that organization's CREDIBILITY goes up when it compares " Like / Similar " people , all of whom have similar " handicaps " , eg :

*  Richest  Man  of India

*  Richest  Man  of  Asia.....etc


Even Companies get ranked / rated ( eg : CRISIL rating ) and even Countries get rated / ranked ( eg: S&P / MOODY etc )

What has all of these " relevance " to IndiaRecruiter / GlobalRecruiter ?  

Quite a lot

GR / IR must become such a RATING / RANKING agency when it comes to jobseekers


We are about to make a beginning in this direction by incorporating into our ImageBuilder ,

FUNCTION   PROFILES { I am tempted to call it , " Functional Competence Profile " }

This concept itself is very NEW / ARGUABLE / CONTROVERSIAL


On top of that , our methodology ( of assigning RAW SCORES ), is not transparent , although it is quite " Objective "

Hence , there is bound to be a lot of discomfort / reluctance on the part of , both , the jobseekers and the HR managers , to " ACCEPT " our rating / ranking - if only for the reason that they do NOT understand , how you are arriving at those Scores / Percentiles !


This ( resistance to acceptance ) will be especially true amongst those jobseekers who end up scoring low !

They will blame the method !

Of course , we cannot help

We only hope that , since HR managers will find these " ratings / rankings " reasonably reflecting their own assessment of the candidates , they will bring pressure on the candidates to stop sending plain text resumes and send only the  IMAGE-BUILDERS


Next , we want to get onto .... SALARY  RANKING  ( really speaking , SALARY  COMPARISON


And , in IndiaRecruiter , we are capturing ALL the required data for each Jobseeker , such as ,

*   "  Function "  ( FIRST priority )

*   "  Designation Level "

*   Annual Salary ( in current job ) ... in EXPERIENCE block

With these data , we need only 2 / 3 simple steps to draw a graph as shown in Annex A

And since , the data has been supplied by the candidate himself, he cannot " DISOWN " it !

Again , X - Y axis are easily understandable . You cannot find fault !

In Annex A , tabulation at the TOP , contains figures ( numbers ) representing the no of " Registered " executives belonging to that CELL

Now , all CELLS will contain different numbers - which keep changing EVERY MINUTE , as more and more jobseekers register

I feel , if any CELL contains a number smaller than 100 , we do not draw any graph for that CELL

We start plotting only when a no in any cell , exceeds 100

Again , in the graph itself , let us not display the " Population = 138 " data

Let us start displaying this , only when the no ( in the cell ) , reaches a respectable 1,000


At this stage , you may wonder :

*   In " Function Competence Profile " , the  X  axis is PERCENTILE

whereas ,

*   In " Salary Profile " , I am showing Actual Annual Salary ( class intervals of 0-1 / 1.01 - 2 / 2.01 - 3 / etc ) on X axis


Why ? Because ,

#  Of course , it is easy to understand an ACTUAL NUMBER ( Rs Lakhs ) , as compared to a CONCEPTUAL NUMBER ( ie : a PERCENTILE )

" Relative Standing " gets established much more easily in mind of jobseeker as well as HR manager


#  While selecting / appointing a candidate , a HR manager has limitations / constraints , in the best / max salary that he can offer to any given candidate ( no matter , how brilliant he is - say , with a percentile of 95 % )


The actual salary of the candidate , super-imposed on the " Salary Profile Graph " , helps HR manager to conclude the best salary that he should offer , with full knowledge as to , what is the " market value " of such a person !


What kind of salaries are " SIMILAR  PROFESSIONALS " drawing ? - quite possibly , even in competing companies


If , by looking at this graph , if HR manager discovers that , what he is prepared to offer - by way of salary - is absolutely ROCK BOTTOM by Industry standard , then he knows that he just cannot attract candidates - even those with percentile of 30 % - the DUDS !


Most HR managers know that they have to offer 25 % - 35 % more than what a candidate is already getting in his current company - before he will consider making a change


With such a " Salary Profile Graph " , a HR manager would be able to make a " Rational / Reasonable " offer to a candidate , which :

*   he is convinced is FAIR , by Industry standard

*   he believes , will attract a candidate

*   he knows will not upset his own existing , similar employees

*   he can defend with his bosses

*   will not " Skew " the Industry Norm , and lead to unhealthy competition for talent ( especially scarce talent ) ,
     among competing companies


I strongly feel that such " Salary Profile Graph " will be greatly appreciated by HR managers


For the first time , instead of relying on " Hunch / Gut feeling " , they have " Statistical Analytics " to arrive at a RATIONAL decision


This graph will be such a powerful  " DECISION  MAKING   TOOL  " , that I believe , HR managers will refuse to look at any other type of resume , from any candidate !


They will insist on ImageBuilder only !


We can expect ImageBuilder to become INDUSTRY  STANDARD , only if and when , we can get all HR managers to bring pressure on jobseekers


We must make , every HR manager , our ( ImageBuilder's ) ,


BRAND  AMBASSADOR  ( - of course , for free ! )


As soon as we have launched IndiaRecruiter ( jobseeker side ) , let us work on this and implement it as fast as we can


We must do this , even before we launch the " Employer Side "

And " Salary Profile " has important implications / ramifications , from a jobseeker's view point as well

Now , for the first time , he knows , what kind of salaries are his Co-Professionals drawing !


" Theory of Relativity " kicks in !


Is he ahead of the pack or trailing the pack ?

Such a " Revelation " - that too graphically plotted - could be exhilarating ( - if he is leading the pack ) , OR


It could be devastating / mentally shattering , if he is trailing , almost all of his co-professionals !

If he knows he is " Underpaid " , he is quite likely to show this " AUTHENTIC " graph to his boss / his Personnel Manager and ask for a " raise " - or threaten to QUIT !  {  Don't try this on me ! )


Now , what would happen if 6 / 8 ( or 25 % of employees ) professionals go to BOSS , flaunting this graph - and asking for raises ?


Would we have created a mini-revolt in an organization ?  May be


May be " salary Profiles " may end up increasing the " Churn " in Industries - but , it will certainly bring in a lot of transparency


And , of one thing you can be rest assured


Every jobseeker who registers on IndiaRecruiter , will come back and EDIT his resume , once every year , immediately AFTER the Annual Increments get announced !


He would want to make sure that ,

*  his own ImageBuilder contains , his new / revised salary

*  he is holding ( or improving ) his " Relative Position " amongst his co-professionals ( - because , if he is falling
    behind  / losing his rank , then it is a cause for worry ! )


And , of course , every time he changes his job , he will come back and EDIT because his salary would have gone up !

And , most certainly , he would encourage his colleagues ( within his own company ) to REGISTER , so that they can compare their own graphs !


We may have found a HOLY  GRAIL  !


hcp  
  

17 April , 2006   


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Please click the following hyperlinks to view different types of


 " Personnel Analytics Profiles  "  :


Sample IT Profile 


Sample NON IT Profile  


Function/Skills Profile  


Salary Profile  


Tenure Profile  


Career Profile  


KarmaScope  


Education Profile  


Project Profile





Thursday, 3 November 2016

ARDIS - Some further thoughts !




Note dt :  09  June  1998

My first note on ARDIS ( Artificial  Resume  Deciphering  Intelligent  Software  ) was written on 01 Dec 1996

Some 18 months later , I sent following note to Yogesh / Cyril , who had translated my notes / U-Is / logic into
www.3pJobs.com ... and launched it on 14  Nov  1997 - some 10 months before GOOGLE got launched officially !

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Uploaded :  04  Nov  2016

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Yogesh / Cyril ,


ARDIS

While discussing the " Data Capture & Query " Module ( Module # 1 ) , a few days back , we also talked about the " Knowledge Base " already available with us . This knowledge base has been acquired / created over last 8 years

This knowledge base comprises of English Language ,

*   Words

*   Phrases

*   Sentences

*   Paragraphs


As far as " Words " are concerned , I myself worked on " Categorizing " them in different " Categories "

This was nearly 12 months ago , using software tool " TELL  ME  " , developed by Cyril

In this connection , I enclose Annex  A / B / C / D


Under " TELL  ME  " , I have already " categorized " over 15,000 words into some 60 different " Categories "

Some of these are shown in Annex  C


In addition , Cyril had developed another simple method , under which , I could quickly categorize :

*   P  =  Person's  Name  ( " Name " of a person )

*   C  =  Company Name

*   Q  =  Edu Qualification of an individual

*   L  =  Name of a Location ( mostly , a CITY )


As far as these 4 categories ( out of 60 odd categories of words ) is concerned , I have already covered :

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FREQUENCY   ......................  No of Words Covered

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>  100 ...................................  7,056

51  -  100..............................    3,913

26  -  50 ...........................        5880

11  -  25  .............................   13,246

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TOTAL ...............................   30,246

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(  See  Annex  A  ) .   These are ISYS-indexed words

So ,

under both the tools combined , I might have already " categorized " over 30,000 words


Over the last 5 / 6 weeks , we have already scanned / OCRed  and created .txt files of some 13573 pages of bio data . And this population is growing at the rate of some 300 pages per day


We talked about a simple software which will pick - out ALL the words ( except for " common " words ) , in each of these page , then,

compare each such word with the " Knowledge Base " of 30,000 words which I have already " categorized "


If a " match " is found , the word is transferred to respective " category " and marked " KNOWN "

If there is " no match " , the word gets tagged as " NEW " and gets highlighted in the .txt file


Now , anytime , any consultant is viewing that page on the screen and comes across a " NEW " marked word , whose " Meaning / Category : he knows , he will have a simple " Tool " ( on that very screen ) , with which he will go ahead and " categorize " that word . This TOOL could be perhaps " TELL  ME  "


We should debate whether we also give the " rights " to any consultant to " ADD " a new CATEGORY , itself

It should be possible for any number of consultants to work on this TOOL , simultaneously , from their own individual work-stations , whenever time permits or whenever they are " Viewing " a .txt page for any reason


This arrangement would " multiply " the effort several times as compared to my doing it " single-handedly " !

PLUS ,

It has the advantage of using the knowledge of several persons having different academic / experience background


We could also consider hiring " Experts " from different " Functional Areas " , to carry out ( this categorization ) in a dedicated manner


Now that we have 13,573 pages ready ( for this simple " match making " process ) , we could seriously consider " hiring " such " experts "


We could even take " Text Books " on various  SUBJECTS / CATEGORIES  and prepare an INVENTORY of all words appearing in each book and put them in the SUBJECT category


Many innovations are possible - if only we could make a beginning

Such a beginning is possible now

Let us give this a serious thought and discuss soon

regards,

hcp

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Word Recognition Software

Word   Recognition   Software


Note dt :  24  Nov  1996 

*   when Job Portals had not arrived in India and even email resumes were a rarity . Nearly 100 % of the resumes arriving in our office , were typed hard-copies ( sometimes barely readable ) and sent through snail mail ! 


Uploaded  :  04  Nov  2016 )

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Any given word ( a cluster of characters ) , can be classified ( in English ) into one of the following " categories " :

*   Verb

*   Adverb

*   Preposition

*   Adjective

*   Noun (  Common Noun  /  Proper Noun )


So , the first task is to create a " Directory " of each of these categories . Then each " word " must be compared to the words contained in a given directory


If a match occurs then that word would get categorized as belonging to that category


The process has to be repeated again and again by trying to match the word with the words contained in each of the categories , till a match is found


If no " match " is found , that word should be separately stored in a file marked ,
UNMATCHED  WORDS  "


Every day , an expert would study all the words contained in this file and assign each of these words , a definite category , using his " HUMAN  INTELLIGENCE "


In this way , over a period of time , human intelligence will identify / categorize each and every word contained in ENGLISH language . This will be the process of transferring human intelligence to computer


Essentially , the trick lies in getting the computer ( Software ) to MIMIC the process followed by a Human Brain while scanning a set of words ( ie: reading ), and by analyzing  the " Sequence " in which these words are arranged , to assign a " MEANING " to each word or to a string of words ( a phrase or a sentence )


I cannot believe that no one has attempted this before ( especially since it has so much commercial value )


We don't know who has developed this software and where to find it , so we must end up re-discovering the wheel !

Our computer files contain some 900,000 words which have repeatedly occurred in our records - mostly converted bio data or words captured from bio data


We have , in our files , some 3500 converted bio data . It has taken us about 6 years to accomplish this feat , ie:

*  Approx 600 converted bio data per year ,  OR

*  Approx 2 bio data " converted " every working day !


Assuming that all those ( converted ) bio data which are older than 2 years , are OBSOLETE , this means that perhaps no more than 1200 are current / valid / useful !


So , one thing becomes clear

The " Rate of Obsolescence " is faster than the " Rate  of Conversion " !



Of course , we can argue :

" Why should we waste / spend our time in ' converting ' a bio data ? 

All we need to do is to capture the ESSENTIAL  /  MINIMUM  DATA ( from each bio data ) , which would qualify that person to get searched / spotted


If he gets short-listed , we can always , at that point of time , spend time / effort to fully convert his bio data  "



In fact , this is what we have done so far - because there was a premium on the time of the data entry operators

That time was best utilized in capturing the " essential / minimum " data


But , if latest technology permits / enables us to convert 200 bio data each day  ( instead of just 2 ) , with the SAME effort / time / cost , then why not convert 200 ? Why be satisfied with just 2 per day ?


If this can be made to " happen " , we would be in a position to send out / fax out / email , converted bio data to our clients in a matter of " minutes " instead of " days " - which it takes to day !


That is not all

A converted bio data has far more " Key words " ( Knowledge - Skills - Attributes - Attitudes etc ) , than the MINIMUM DATA , so there is an improved chance of " spotting " the RIGHT MAN , using a QUERRY which contains a large number of Key words


So , today , if the client " likes " only ONE converted bio data , out of TEN sent to him ( a huge waste of everybody's time / effort ) , then under the new situation , he should be able to " like " , 4 out of every 5 converted bio data sent to him !


This would vastly improve the chance of at least ONE executive getting appointed in each assignment . This should be our goal


This goal could be achieved only if ,


STEP  #  1
Each bio data received every day is " scanned " on the same day


STEP  #  2
Converted to TEXT ( ASCII )


STEP  #  3
PEN ( Permanent Executive Number ) given serially


STEP  #  4
WORD - RECOGNIZED ( a step beyond OCR - Optical Character Recognition )


STEP  #  5
Each word " categorized " and indexed and stored in appropriate FIELDS of the DATABASE


STEP  #  6
Database " Re-constituted " to create " Converted " bio data as per our Standard Format


Steps #  1 , 2  and 3  are not difficult

Step # 4 , is difficult

Step # 5 , is more difficult

Step # 6 , is most difficult


But , if we keep working on this problem , it can be solved ,

50 % accurate in 3 months

70 % accurate in 6 months

90 % accurate in 12 months


Even though there are about 900,000 indexed WORDS in our ISYS file , all of these do not occur ( in a bio data / record ) , with the same FREQUENCY


Some occur far more frequently , some frequently , some regularly , some occasionally and some rarely


Then of course , ( in the English language ) , there must be thousands of other words , which have not occurred EVEN ONCE in any of the bio data !


Therefore , we won't find them amongst the existing " Indexed File " of 900,000 words

It is quite possible that some of these ( so far missing words ) , may occur if this file ( of words ) , were to grow to 2 million

As this file of words , grows and grows , the probabilities of ,

>  a word having been left out  ,  AND

>  such a " left out " word likely to occur ( in the next bio data ) ,
are " decreasing "


The Frequency Distribution curve might look like follows ( a skewed Normal Curve / Skewed to left ) :


X  Axis  

>    % of Words in English language or in ISYS - 900,000 ( 10 % - 20 % - 30 % ...and similar intervals )


Y  Axis  

>    Number of times occurred ,



Meaning :

Some 20 %  of the WORDS ( in English language ) make up , may be 90 % of all the " Occurrences "

This would become clear when we plot the " Frequency Distribution " curve of the 900,000 words which we have already indexed

And , even when this population grows to 2 million , the shape ( the Nature ) of the frequency distribution curve is NOT likely to change !


Only with a much larger WORD POPULATION , the " Accuracy " will marginally increase

So , our search is to find ,

*  WHICH are these 20 % ( 20 % * 900,000 = 180,000 ) WORDS, which make up 90 % " Area under the Curve " , ie: the POPULATION ?


Then focus our efforts in " Categorizing " these 180,000 words in first place


If we manage to do this , 90 % of our battle is won


Of course , this pre-supposes that before we can attempt " Categorization " , we must be able to " Recognize " each of them as a WORD

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