People matching is one of the most
exciting problems to solve. What can be more important to anyone than finding
interesting work, or a job if one is unemployed, or a person with the right
talent to do a task or shortlist for a job? Other subsets are matching benefactors
and worthy causes or finding partners. Information & Communications
Technology of today and a smart program can expand the search horizon manifold
or find talent within a pool much more reliably and sensibly.
Automation and knowledge driven
operations, changing life styles, rising aspirations and increasing mobility of
people, apart from the enabling ICT are compelling motivators for building a
smart people matching program and making it accessible for universal use. At
Systems Dynamics, we have built such a program, that is TOTALLY INCLUSIVE
(demographics agnostic) and SCALABLE. Called PEMS (People Expertise Matching System), it is configured and
deployed for five models. Few use cases:
1. Employee
seeks interesting work opportunities within one’s organisation; Manager (or a
co-worker) seeks the right talent (expert) to solve an urgent problem, work on
a project or short assignment or fill a particular position.
2. Recruiter wants to hire candidate(s) to
fill a new/vacant position.
3. Placement officer wants to place
graduating students or find internships.
4. Empower Employment Facilitation Centres
(EFC) to be located in Industrial Clusters where Work Seekers (WS) and Work
Providers (WP) can walk in to register their profiles and requirements for
receiving short-lists of matching opportunities or candidates.
5. Empower State or National level
Employment Exchanges where WS and WP can register their profiles and
requirements and expect to receive short-lists of matching opportunities or
candidates (through SMS and email, as opted) and Government functionaries can
obtain real time demand supply gap snapshots – region wise sector wise and
receive data analytics that can inform their formulation of schemes, steer
investments and resource to bridge the current and emerging gaps in the job
market, upgrade skills, assist affirmative action groups or assist
entrepreneurial ventures. In India today, ~ 1K Employment Exchanges (run by
State Government’s Labour Departments) have ~ 40 million “unemployed WS”
registrants but annually less than 1% get placed. With our solution, the
successful placement rate can be easily quadrupled and registrants targeted to
400+ million “WS” within three years (“Work” is different from a “Job”). The
social benefits are non-trivial enough, for justifying a national public sector
project. Even a private venture could monetise some benefits and earn good ROI:
The potential EVA (Economic Value Addition) could exceed 100 billion rupees
p.a. (400 M WS registrants x 4% placement x average 6.5K monthly wage). The
capital investment in empowering 1K employment exchanges and 100K Common
Services Centres (existing in rural India), including training cost, will be
less than 2 billion rupees! Operational cost will be mostly publicity and
HelpDesk support. Low hanging fruit it is for Government or an Entrepreneur.
I will be happy to discuss the USP of PEMS
(underlying system and method is patent pending) with people interested in
evaluating its usage; or how to make PEMS universally accessible which is THE
challenge – not if you happen to be Bill Gates or Larry Page or Anand Mahindra
or Azim Premji ;)
Note for Satya Nadella :)
LinkedIn has steadily improved in
capturing human expertise and connecting people. Testimony is its growing
popularity. However, it lacks the fine grained expertise mapping needed in
today’s world to do automated matching. Enumerations in dynamic drop down
lists, provided in current version of LinkedIn, do help the users escape the
tyranny of “Key word matching” (as both WS and WP select from the same list)
but more intelligence, greater precision and matching of attributes of
preferences (other than expertise) are needed for sensible short-listing! It
requires an ontology (parent-child relationship between expertise attributes)
rather than linear drop down lists because the WS and WP may refer to skills in
the same branch at different levels (for e.g. SN’s project manager wants
a developer in “.NET Framework” and the work seeker has mentioned her expertise
as “LINQ” or let’s say, BG Foundation’s Manager wants to hire vaccination
assistants and the work seeker has claimed experience in public health programs
with a relevant healthcare certificate from National Council of Vocational
Training).
Note for Sundar Pichai ;)
Google’s mission is not just finding
information on the web but information in the world! “People finding” is too
alluring a problem to pass up for applying AI or Machine Learning which Google
is brilliantly applying in its multiple products. But over here, this approach
is NOT inclusive. In India 80% of workforce is not on web at all. Significant
workers across the planet aren’t on the web. After Google and Google Earth it
ought to be the turn of Google Mankind and not Google Web folks!
