If you believe certain urban legends, Leonardo da Vinci was the first person in recorded human history to have crafted a professional résumé. Of course, he didn’t call it a résumé at the time. He just wanted to convey his usefulness to the Duke of Milan during the critical times of war and peace. Little did he know that the inadvertent convention he was creating would be carried forward five centuries.
Prior to the 1930s, résumés were merely a formality. For the job positions that would open up, word would spread mostly through print advertisements or word of mouth. People would queue up at offices to present their applications for these jobs. While talking to their prospective employer over a cup of tea, candidates would scribble their necessary personal and professional information on a sheet of paper and hand it over for record-keeping.
Over a period of time, as conventions moulded into norms, employers began expecting this record in prior to their meeting with potential employees — aiding the résumé in becoming a mainstream convention and a pivotal aspect in the hiring process. The upcoming century saw numerous re-modellings in the résumé’s format while its fundamental intent remained the same — of informing potential employers about an individual’s personal, educational and professional details, and the skills that would add value to the job position they’re seeking.
The résumé, which started as a trivial medium of transferring information, went on to become a definitive record of a person’s professional profile — and eventually saw itself transform into the single source of truth. Much of this success could be attributed to the absence of a better repository of relevant and organised information. This was an era during which storage of information used to be extremely centralized, and its dissemination, non-trivially expensive. Employers and recruitment outsourcing agencies had begun maintaining hordes of these static documents of declared data about prospective hires. The competitive advantage in hiring the best people was coming from the ability to collect more résumés.
In an ironic twist of intentions, quantity, and not quality, had become the drivers of success.
And this had worked well. In the 20th century.
The Internet’s invention around the 20th century’s withering years, and its viral mainstream public adoption at the turn of the 21st century brought about a tectonic shift in how we consume and publish information by democratizing access to its means. The scale was huge and its embrace has been growing exponentially ever since.
Today, on an average, the working class spends 3.26 hours every day on the Internet, creating and consuming an endless stream of information. Correspondingly, the rise of social platforms has created more avenues for people to share data about themselves online, intentionally and inadvertently. We see ourselves documenting every significant aspect of our personality and our profession on the internet for anyone to see.
A great amount of indirect and implied professional information about us comes from our activities on numerous social engagement platforms. Take into consideration the various popular forums such as Github, Stack Overflow, Hacker News, etc for software developers; Dribbble and Behance for designers; Kaggle for data scientists, Hacker One for computer security researchers — to name a few. Communities like these have become deeply ingrained in our professional daily lives. We find ourselves actively contributing to the growth of this two-way stream of data, befitting our skills, interests and our proficiencies.
Leveraging this massive repository of public information to paint a better picture about someone’s strengths, weaknesses, and their personality in general, fulfills the original intent of a résumé. In the same vein, it aids in better decision-making, backed by data that can be proven with facts and isn’t merely declared.
This begs the question — why do we still need a written document reiterating a small subset of this information in a vaguely restricted and highly non-standard format? Résumés don’t need to remain the central source of information anymore.
Why are we still holding on to the floppy disk?
In the present day, the platforms that help people find jobs and help employers discover people rely on algorithms that match an abstract job description over a repository of résumés — often collected over time, hoping to unearth a few that are actually relevant.
Fifty years ago, when the information and insights about a prospective hire could fit on a single piece of A4 paper, this process might’ve been the go-to mechanism for effective hiring. However, today, this process is bereft of objectivity, due to the sheer subjectivity of both the job description and the résumé. As a result, it becomes sub-optimal by definition.
The underlying résumé parsing and matching engines that drive the aforementioned platforms apply Natural Language Processing to convert abstract information to workable data points. With résumés as their primary data source for recommendations, the effectiveness of these platforms is limited by the quantity and validity of data points about a person.
The way we discover and match people with jobs must keep up with the blitzkrieg and magnitude of data available about people publicly on the internet. Otherwise we’d still be dwelling on the inefficiencies of the past.
Better job matching that works in favour of both the employee and employer can be achieved by exponentially by improving the quality and quantity of data involved in the matching algorithm, while refining the algorithm concurrently.
The days of job boards, résumé repositories and résumé intelligence engines are numbered if they do not fundamentally reinvent their core workflow around the primary problem they had set out to solve.
Better matching of people with relevant jobs consists these components:
There’s an immense amount of data publicly available about everyone, and in different contexts. Leveraging this data to extract direct and implied information while creating a comprehensive profile would enable better decision making. The information you interact with would be backed by facts, and not declarations or opinions.
Natural language processing and Boolean searches can only get us so far. The number of vectors in the algorithms we use must scale with the number and quality of data points in the candidate profile, hence enabling better contextual match.
Abstract and poetic job descriptions do well to impress a human being, but fare poorly when it comes to machines. The best recruiters involve a huge number of other vectors as part of their job description when evaluating a candidate, and the machine should not do it any differently.
Data, as they say, is the new soil.