It’s been an year since I’ve had those horrid dreams… TTI Kharagnagar had left me, but the memories had remained. In hindsight, the dreams had left me stronger than I had ever known myself to be. I didn’t realise it then, but the nightmares prepared me for the next great adventure. Ofcourse, I could only understand it looking backwards. But was there more to be seen if I looked back? Did I miss anything in my agony? Was I too close to the equation to see the bigger picture? Could I see something more, coming out of the world of Kharagnagar to look at it from another dimension?
So I went back to the world of Kharagnagar, this time with different eyes. And I was astounded by what I saw. A world of facts and numbers. Ofcourse it was all in a dream, but it felt almost real. I began sketching out what I saw. And touched upon some fine aspects of those dreams which I’d like to share with you.
To begin with, this was astounding. The 20 days of placements reinforced some truths, and removed some myths. If CTC was a measure of the quality of a job, then the Day of the placement had little to do with it. The “Week 1” quotient was overhyped. Also, the high number of selections on Day 1 and Day 2 hardly meant anything good. The number of candidates per company for Day 1 and Day 2 was actually lower than the average, which was maximum for Day 8 with 53 selections with 7 companies. Also, Day 1 undoubtedly was more active than any other day. But that does not mean that it offered the best pay.
Barring Day 1 and Day 2, there was no systematic trend in the Days in terms of the number of companies, CTC or the number of selections. It simply went as berserk as my investments in BSE right now!
So I thought of not looking at the data from a student’s perspective, but from the point of view of the campus. After all, we know that the campus puts itself first. The sure measure of a campus’ performance was the total of all CTCs its students had achieved for an year. After all, if at all it came to outsourcing, this would be the amount based on which the HR consulting companies would charge the campus. As it turned out, the figure came at a whooping ₹ 83.86 Crores. However, the sad thing was while this showed a shiny picture for Day 1 and 2 when the media reports came out, it made life tough for the remaining 82% students who were yet to be placed after Day 2.
But the star of the show for me was the CTC analysis where I grouped the placements on the basis of CTCs. What I expected was a standard bell curve. Most people would earn an average amount, few would make the millions and few would look back just to ask, “what went wrong?” However, the real picture was nothing I could imagine on my own.
Most people seem to be getting paid less than the average income. Ofcourse the lowest always went to a few, but the shape told a story different from a bell curve. While it took an early spike, it remained low for the end of the curve. Which meant the number of people getting paid less was significantly more than the number of people getting paid more. Also, the number of selections per company was significantly lower in case of more paying jobs. I smiled. This was exactly what the human resource market was supposed to be like. Lesser wages for more work. Isn’t that what the market strives for?
At this point I needed to see the highlights of the placements before delving into deeper fields. I needed to know what to expect. The first surprise came when I saw the average CTC. This was a number which a lot of Business Management students aspired for. There was something wrong here. Ofcourse! The few high payers had offset the mean into a high range. Thankfully I had my statistics course right and looked for the mean. That, still higher than my expectations, looked like an acceptable number.
At this point I heard a faint echo in my head which said 1,000+. I had a question to ask – “With or without the PPOs? Did KGN take credit of the PPOs too?”
But this was just the tip of the iceberg. How was this helping the future candidates in any way? This was just a story to look back on and smile at. I was sure there was more to my dream. There was something hidden which would let me see more. And this is what the dream handed over to me.
It classified each company in an industry and the profiles offered. I didn’t really understand how a “Trainee” was different from an “Associate” or what exactly did these companies mean when they said “Graduate Engineer,” “Engineer,” “Post-Graduate Engineer Trainee,” “Trainee Engineer,” and similar things. But it was the dream handing things out to me and not the other way around. It said that each industry had a preference to a profile in which it wished to hire. And each profile was best suited to an industry. And the 2 might not always be in a perfect marriage. Also, there were certain profiles which everyone wanted. And there were some “hot” industries in the market. Slowly, I was able to carve a map of which companies needed what. I found this useful as I know that people generally stuck to an industry they began with. Companies change, but it’s tough to exit sales or IT once you have entered life in those fields. They shaped the direction of your career. So even if the company paid low, I’d like to go to the industry which I wish to understand to later move to another company in the same industry. After all, why would I like to be a beginner again in a new industry?
If the dream could tell me which industry preferred which kind of profile, hence the kind of preparation a candidate must undergo to ensure his preferred industry, it could also tell me more about the industry. Such as what employment opportunities did it offer? What was the competition like in that industry? How much did it pay? And how big was each industry? I cannot say the results were surprising, but were surely an eye-opener.
If the dream could tell me so much about the industries, why not about the profiles? The more I got to know, the more questions I had. If I specialized in a particular work, how many people would be willing to hire me? How much would I be paid for my skills? How easy would it be to switch a company, or maybe even an industry?
Alright. So I had my answers. I could not think of anything else to ask. But there was this nagging thought in my head regarding the Day of my placement. I think the desire of an early placement was so deeply inculcated into my mind, which I simply could not let it go. I wanted to know the spread of the industries and the profiles over the days. “All days are not the same.” But could some companies evade the dynamics of the days and come through? Was it worth waiting for my dream company? How many companies in each category did each day offer? As I struggled for my answers, my fingers kept moving on the keyboard.
I know I was groggy when I wrote this piece. My data cannot be completely correct. Not all results came up on the Notice Board. Not all selections were accounted for. The screen I looked from wasn’t very transparent. But I think I could not have overlooked the fate of 149 students in 20 days. That margin of error, even to me, seemed absurd. Specially when I heard echoes of “1,000+” and “poor results this year” together. Something did not make sense.
Also, I realized that some profiles were well-distributed over the days. While some seemed to be clogged in corners. While this made no significant difference in the long run, this added anxiety to the candidates in the 20 days of Hell. Perhaps allowing different profiles to be spread over the days and avoiding clashes between the days for the same profiles would help matters; but then I’m no expert in controlling my dreams.
As a final note, I heard faint whispers of the assumptions I’d taken. Different companies of the same conglomerate were considered as 2 companies. There were 2 companies for whom the number of selections remained unavailable, though I think even they could not help the margin of 149 candidates. There were cases when a company offered different CTCs for the same profile for different qualifications of the candidates. A weighted average was taken in all such cases wherever possible. The companies which visited the campus but did not select any students did not become a part of this analysis. A company making selections on 2 different days was considered twice for the analysis as 2 different companies. In a lot of cases, the profiles for recent graduates remained a grey area, and were left to my judgment which I wisely exercised.
