Like these top tech companies, Harvard Business Review reveals that LinkedIn’s growth is not distant from data-backed moves.

A bit of history: how LinkedIn did it

It all started when Jonathan Goldman joined the business networking platform as far back as 2006 when the growth level was just average.

LinkedIn’s team had projected a faster rate at which users would be seeking connections and networking with each other, but that wasn’t the case. Something was missing.

Goldman took as his pet project the challenge of finding the missing link through data analytics.

The postdoctoral physicist waded through the massive data, performed several analyses, and began seeing the possibilities.

With the support of Reid Hoffman, LinkedIn’s Co-founder and CEO at the time (now its executive chairman), Goldman got the leeway to start testing out his hunches on the platform.

Within a few days of implementing these analytics-backed ideas, the results were stunning.

Goldman continued to refine his “People You May Know” suggestions, contributing to the impressive growths that the professional networking community has come to be associated with.

Everyone needs data science…

With the volumes of data generated globally due to the advent of cloud innovation and technologies, there has been an upsurge in the need for in-house employees that can make sense of data for increased business growth and development in a Goldman-related manner.

Businesses, big and small, have woken up to the immense possibilities present in taking advantage of these vast data to gain insight and make profitable business decisions.

…but there is a shortage of Data Scientists

Despite the enormous data scientist job ads that go out frequently, it is unfortunate that the demand-and-supply ratio of talent acquisition has remained unbalanced.

Here are some stats for your consideration:

Data Scientist is the most fantastic job in 2019 (LinkedIn)

By 2020, openings for data scientist will rise from 364,000 to 2,720,000 (IBM)

There’s a 35% difficulty in recruiting for data science positions, resulting in a 250,000 job shortfall (IBM)

Perhaps this situation favors the handful of data science professionals positioned to quote chart-topping compensations to prospective employers.

But the same cannot be said of employers and business owners who can not compete competitively for the retention of these talents and thus miss out on the impressive ROI that comes with such talent investment.

#machine-learning #data #data-visualization #ai