CALL NOW 713-568-9753
12 Mar 2017
Fighting ageism – really?
Mark KerznerPosted in: Book Reviews 1

March 2017 issue of SD Times has a “Fighting ageism” picture on the coverscreen-shot-2017-03-12-at-4-32-38-pm. The main article is “Old developers can learn new tricks”.

The author takes a two-prong approach: quoting (rare) managers who find value in older programmers, and talking about individual older developers who are “reinventing themselves.”

I need to state emphatically that what follows is my own opinion and nobody else’s, and not of Elephant Scale as a company.

Ageism and data science
But this is pathetic! A manager who finds value in the code of older developer ‘because it lasts longer, for 10-15 years!’. This is a travesty. Code this old should not exist, it should have been rewritten multiple times.

Furthermore, one of the developers admits that he will never make it and will have to take pay cuts as he goes.

I think that the problem should be approached in a completely different way. Instead of claiming that ageism is wrong, one should say that it is outlawed. But, as the book on Chinese history that I am reading now said, “Outlawing some activity (in this case counterfeiting) was easy. But making it go away was a different story.”

Today, we are all data scientists, or at least we claim to be. So look at the statistics. A couple of quotes, “An analysis by age shows that the participation of younger persons (aged 25–34) in the EU-28 was nearly twice as high as that of older workers (aged 55–64) in 2011. ” Source: EU Lifelong learning statistics.”

Another one: “A significant majority of older adults say they need assistance when it comes to using new digital devices. Just 18% would feel comfortable learning to use a new technology device such as a smartphone or tablet on their own, while 77% indicate they would need someone to help walk them through the process.” Source: Pew Report.

If ageism has its basis, what is the solution?
So let’s face the problem: people often stop learning once they are not forced to do so in school and college. And gradually their brain, without exercises, loses its power and flexibility. The comparison of the brain to other muscles is well-known.
This situation used to be acceptable. One could work in a company all his life and enjoy job security. Not any longer. Andrew Ng, chief scientist at Chinese Internet search giant Baidu and co-inventor of the Google Brain, explains it excellently and clearly in his interview: it used to be that generations spent their lives in the same trade. Then rural workers moved to the cities in a lifetime. And now technologies change in ten years or faster. A truck driver may need to learn software development in the normal course of his career.

Does age matter?
So we are faced with this suggestion: in order to accept constant change, we need to become constant learners. True, I am somewhat biased here: at least part of my time I spend as a teacher and a trainer. But on the other hand, I represent the other spectrum of the age scale. Just search on Intelius or look at my resume. Also true that for many years I have been following a daily learning schedule. But this only proves the point: age does not matter if one keeps learning. To paraphrase my favorite ballad by Rudyard Kipling, changing just one word

OH, East is East, and West is West, and never the twain shall meet,
Till Earth and Sky stand presently at God’s great Judgment Seat;
But there is neither East nor West, Border, nor Breed, nor Birth,
When two strong men learners stand face to face, tho’ they come from the ends of the earth!

What about outsourcing?
Before I end, there is one more trend that is becoming important. Software development tools are becoming more powerful. Apache Spark code has seven times fewer lines than Hadoop, yet it does more. So even outsourcing is not such a big thread as it seemed to be to the author of the book “Decline and Fall of the American Programmer.” It is not the number of hours or the lines of code that matter, it is the depth of understanding of both the coding and the practical aspect. So your biggest challenge is your own potential, which you as a developer need to realize to the fullest by learning. By the way, here is our training schedule.

1 thought on “Fighting ageism – really?”

Leave a Reply

Your email address will not be published. Required fields are marked *

Explainer Videos
Short animated clips explaining Big Data concepts



Training
Vendor neutral, expert training taught by practitioners

Big Data track
Hadoop, Spark , Cassandra , Kafka ,

Data Science track
Machine learning with Spark, Data Science with Python






Categories
Tag Cloud