FUNCTION PROFILES COMPETENCE PROFILES

Kartavya

27-02-03

Leveraging RESUMINE while raising ENTRY-BARRIER

I refer to our telecon in the afternoon.

My proposal has 2 parts.

PART 1

Under this suggestion/proposal, a large (employment-wise) Organisation will collect/compile email resumes of all of its employees & then convert these, using Resumine.

Advantage

• At one stroke, the Subscriber-Organisation would develop a structured, searchable database of all of its employees – whether the number is 50 or 50,000.

• Over a period of time, that Organisation will eliminate hard-copy PERSONAL-FOLDERS of all of its employees (EMPLOYEE-RECORDS), as it moves more & more of its individual employee-related info online, in digitised form.

• RESUMINE permits the subscriber to take this FIRST step in that direction.

If a Subscriber employs

• 500 “Sales” personnel

• 200 “Mktg” personnel

• 2000 “Mfg” personnel, etc.

Then that organisation can plot frequency-distribution graphs (one for each “functional” area), by plotting the “scores” attained by each individual employee, belonging to that function.

(Hand-drawn bell curve shown with Function: SALES, plotting Competence Score. Example: Venkat = 65%, Mr. Mhatre = 45%)

Such a graphical/easy-to-grasp representation would help HR manager as follows:

• In which “areas” do Mr. Mhatre need further training, to improve his “competence-score”?

• Should we retain Mr. Mhatre (for retraining) or give him a “VRS” package? Golden Handshake.

• How do we reward Venkat, who has improved from a score of 60% last year to 65% this year?

So, you can see how

FREQUENCY-DISTRIBUTION of COMPETENCY-PROFILES (Function-Wise)

provides vital inputs for:

• Determining “Training Needs” & formulating “Training Programs” for employees in each functional area (an OD = Organisational Development activity).

• Annual “Increments/Rewards”

• “Right-sizing” the Organisation
  (As per Jack Welch, every Company, every year, should “let-go” [read: sack] 10% of its lowest/poorest performers).

Of course, Competency-Profile should not be used in isolation/stand-alone for above-mentioned decisions. Goal-setting (at the start of the year) & actual performance measurement (at the end of the year) MUST be taken into account. In fact, those should have a greater weightage.

This is as far as “existing” employees are concerned.

Now, let us turn to a “new” candidate yet to be recruited, whose resume has just come in.

Part 2

We already have 75,000/80,000 email resumes lying with us.

During March, we expect to receive another 50,000 under Project Manhattan. This can be further stepped-up, if desired.

So, when we go online & launch our Resumine Research webservice, say, by June, we may already have 200,000 email resumes.

We should process all of these thru RESUMINE. When done, each resume will have a CORE “Functional” Competency (the topmost tier). So these 200,000 will get distributed amongst 53 “functional” areas.

Maybe, the highest “Function” may have 10,000 resumes & lowest may have 1000 resumes. These are still large enough populations to plot frequency-distribution curves.

(Hand-drawn bell curve with Function: SALES, Population: 8234)

There will be 53 such graphs like this – one for every “Function.”

And software should ensure that all incoming resumes are processed everyday and the graph must be updated accordingly.

This way, the “population” keeps growing & the graphs become more & more accurate/representative.

Now, suppose, we have 200 subscribers, who, between them, are “processing” 10,000 resumes everyday on our Webservice, then we have an unbeatable proposition!

A website like MonsterIndia already have 9.5 lakh resumes & this database is growing at 1000 per day!

Monster (USA) has 20 million resumes!

The larger the resume database, the more accurate the profile (distribution-curve).

As soon as a Recruiter “processes” an incoming resume thru our Resumine, the bars will appear.

Simultaneously, the “Frequency-Distribution Curve” corresponding to the TOP-BAR FUNCTION should also appear (– on our already crowded screen, we have to decide “where”).

Alternatively, only upon recruiter clicking the button

[Compare with Population],

the frequency-distribution graph may appear.

(Hand-drawn bar graph showing SALES = 63%, with a “Compare with Population” button)

New screen will appear as follows (replacing above screen):

(Hand-drawn bell curve with Function: SALES, Population: 8234.

Example: Mr. Mhatre at 63% plotted against GLOBAL COMPETENCE BENCHMARK.)

Now, this tells the Recruitment Manager where exactly does Mr. Mhatre stand (as far as his competence – and therefore his “suitability”) with respect to 8234 similar professionals in Webservice-Database.

This would help recruitment manager to decide whether Mr. Mhatre is worth shortlisting and/or interviewing. This is a kind of “Automatic Rating & Ranking.” This is a great time-saving & productivity-enhancing feature!

Such a “fading-in” & “fading-out” of graphs would highly impress the demo-audience.

A smart HR manager may even want to “profile” each of his “existing” employees against 3P Webservice’s GLOBAL COMPETENCE BENCHMARK (GCB) & show it to his Corporate Management!

And, of course, GCB will also appear/display when a recruiter clicks on the “shortlisted” candidates listed by RESEARCH.

And, we could even make GCB available to each & every jobseeker, who registers on our website!

Would not that be great?

Kartavya

04-03-03

Competence Profiling

This is further to my note dt. 28/02/03.

In this note, I enclosed two Competence Profile “sheets” for Mr. A. J. Mhatre, whose:

• Primary Function is — SALES

• Secondary Function is — MKTG

• Tertiary Function is — Service

I also guessed:

Persons/Professionals whose	Population Size of such professionals
Primary Func → Sales	9563
Secondary Func → Sales	12,893
Tertiary Func → Sales	23,894
Primary Func → Mktg	4296
Secondary Func → Mktg	9087
Tertiary Func → Mktg	13,487

Now let us further assume (or find out from structured database of Mr. Mhatre, created by RESUMINE) that:

Mr. Mhatre

• Age: 46 yrs (from DoB)

• Designation: General Manager

• Edu. Quali.: Bachelor’s Degree

• Total Exp.: 25 years

Sometime back, you had brought out a point.

How do we add “Weightages” of Age/Exp./Edu. etc. to the Competence-Profile of a person?

I have not found a neat solution to this problem, until I see your point.

You are saying that,

“We are not comparing Apple with Apple, when we compare Mr. Mhatre’s COMPETENCE SCORE with the same for entire population.”

The entire population whose Primary Function = Sales is 9563.

But, this population may/will contain executives, whose:

• Age ranges from 26 years to 56 years

• Designation ranges from Officer to President

• Edu. Quali. ranges from S.S.C. to Ph.D.

• Experience ranges from 1 year to 34 years

Now to say that

(Hand-drawn bell curve showing Mr. Mhatre’s score vs. Total Population = 9563)

… is not comparing Apple with Apple!

We should in Resumine build-in a feature, whereby we can draw such COMPETENCE SCORE FREQUENCY DIST. GRAPHS for each:

• Age-Group (21-25 / 26-30 / 31-35 / 36-40 …)

• Edu. Level (SSC / Graduate / Post Graduate)

• Design. Level (Supv/Off/Mgr/GM/VP/…)

• Total Exp. (5-10 / 11-15 / 16-20 / 21-25 years …)

Now when we do this, the total population of 9563 whose Primary Function = SALES, gets redrawn as follows:

(Hand-drawn graphs showing Mhatre plotted under subsets)

• Age = 46/50, Population = 1289

• Desig. = Gen. Mgr, Population = 842

• Edu. = Bachelor’s, Population = 4263

• Total Exp. = 21-25, Population = 693

PRESTO!

The moment we click on button:

[Compare Apple-for-Apple]

We see altogether different COMPETENCE-SCORE FREQ. DISTRIBUTION GRAPHS, in which:

• Mhatre’s “Standing/Ranking” immediately improves!!

• Now you are truly comparing Mhatre with his PEERS (people at same level)!

No more diluting his rating/ranking in a hotchpotch (kitchdi) of all-and-sundry professionals, whose only LCD (lowest common denominator) is that they are all, primarily, SALES professionals. But beyond this one/single attribute, they otherwise form a very HETEROGENEOUS GROUP!

Having captured all STRUCTURED data for/from every incoming resume, it is relatively simple/straightforward for the software to:

→ Create such Freq. Dist. Graphs for each & every “variable” (we can expect hundreds of combinations).

→ Once-a-day, update all such graphs (by updating all data-tables) based on resumes processed during the course of the day on our Webserver.

As we discussed, we can tell the potential subscribers:

“Sir/Madam,

If you process (thru Resumine), the email resumes of all of your EXISTING employees, then you can generate such COMPETENCE SCORE FREQ. DIST. GRAPHS for your own Company (of course, provided you employ a large enough no. of professionals in each category/functional area).

Thereafter, anytime you are trying to interview/recruit an outsider professional, you could super-impose his Competence Score on your Freq. Dist. Graph to figure out where exactly does this outsider stand (in comparison with your own internal population of similar/identical professionals).

You can make “Apple-for-Apple” comparison of an Outsider with your Insiders!”

This will help you in “structuring” an optimum “offer” for that candidate, in terms of:

→ Designation to be offered

→ Salary to be offered

WITHOUT upsetting your own Internal Apple-Cart!

This will also help you – as an enlightened HR Manager – to convince your own internal employees as to how no injustice has been done to them, when offering such designation/salary to an outsider.

Generation of such Internal Freq. Dist. Graphs (of existing employees) would also help you in:

→ Performance Appraisal

→ Annual Rewards

→ Training Need Assessment etc.

ONE IMPORTANT POINT

All Freq. Dist. Curves & Comparisons will be offered as a feature to only those subscribers who accept/agree to keep their resume-databases on our Webserver.

Those who wish to keep their resumes on their own local harddisks will NOT get this (free) service. Since (our argument) these graphs are generated ON-THE-FLY!

Kartavya

28/02/2003

COMPETENCE – PROFILING

When a Corporate wants to borrow money from a Bank/Fin. Insti. or wants to make a Public Issue, it has got to get a CREDIT RATING (AAA/AA/AB/DA) from {CRISIL / S&P / MOODYS / DUN & BRADSTREET} etc.

Even Countries are rated by such Agencies & these “Ratings” determine:

→ Country’s borrowing capacity

→ Foreign Investors’ confidence in repayment capability of that Country, etc. etc.

These “Ratings” are not static. Based on changing circumstances/events, such ratings are revised periodically.

In case of Countries, the Rating Agencies do not wait to receive any “request” from the concerned Country. The Agencies carry-out & publish their ratings/findings on their own & independently.

In case of Corporates, agencies carry out ratings

→ Independently on their own

→ As & when requested by a Corporate.

Thru our “COMPETENCE-PROFILING” in Resumine, could we, someday, hope to become such an INDEPENDENT / UN-BIASED / OBJECTIVE RATING AGENCY as far as jobseeker’s are concerned?

Could we foresee a day when Recruiters insist that each & every incoming email resume is accompanied by 3P COMPETENCY-PROFILE RATING-SHEET (see enclosed graphs), without which the application will NOT be considered?

ALTERNATIVELY

When an email resume is processed thru Resumine, competency profiles, as per enclosed graphs, gets automatically generated & presented to recruiter/interviewing expert, at the click of a mouse?

If thousands of resumes pour into our webservice everyday (from hundreds of subscribers), the “profiles” become more accurate. Also, there is “consistency/clarity/objectivity.” And everytime a jobseeker (using PEN which is owned by him) sends revised email resume, profile gets updated with a “date-stamp.”

Let us give the world – 3P RATING

These “ratings” exercises follow clearly-defined & transparent “Methodologies,” so that there is:

→ Consistency

→ Clarity

→ Objectivity (as far as possible – because there could be some “factors” which may not lend themselves for “Objective Measurements.”

eg: Govt. Policies, War Risk, Quality of Management etc.)

These “Consistency/Clarity/Objectivity” is what commands respect/belief amongst lenders/investors etc.

Same is true of “Online” Certifications issued by:

→ Microsoft (MCSE etc.)

→ Brainbench

There is “Consistency/Clarity/Objectivity” – since questions databank is created, “degree-of-difficulty” is assigned to each question and finally each individual who takes an online Test gets a UNIQUE/RANDOMLY GENERATED test-paper without any human intervention!

No “Subjectivity” at all. Everything automated & software-driven → hence very high CREDIBILITY.

COMPETENCE – PROFILE

Mr. A. J. Mhatre  PEN: ______

Date: ______

Where does he stand among Professionals whose:

1. Primary Function = SALES

   • Population = 9563

   • Mhatre’s score: 45%

(Hand-drawn bell curve showing his position at 45% in the Sales population)

2. Secondary Function = SALES

   • Population = 12,395

   • Mhatre’s score: 65%

(Hand-drawn bell curve showing his position at 65% in the Sales secondary function population)

3. Tertiary Function = SALES

• Population = 23,684

• Mhatre’s score: 90%

4. COMPETENCE – PROFILE
   Mr. A. J. Mhatre  PEN: ______
   Date: ______

   Where does he stand among Professionals whose:

   1. Primary Function = MKTG
      (Remember! Mhatre’s Primary Function = SALES)

      • Population = 4296

      • Mhatre’s score: 48%

   (Hand-drawn bell curve showing 48% in Marketing primary)

   2. Secondary Function = MKTG
      (This is also Mhatre’s Secondary Function)

      • Population = 9085

      • Mhatre’s score: 60%

   (Hand-drawn bell curve showing 60% in Marketing secondary)

   3. Tertiary Function = MKTG

      • Population = 13,468

      • Mhatre’s score: 78%

   (Hand-drawn bell curve showing 78% in Marketing tertiary)