Today we will discuss the top-three LinkedIn Datasets of publicly available data that companies are implementing as part of a cutting-edge approach to human resources:
- Identifying unique skill sets, and linguistic capabilities
- Discovering candidates with specific interests/hobbies
- Mapping key players in a given competitive landscape
Identifying unique skill sets, and linguistic capabilities
Now more than ever, companies need individuals that are highly specialized. For example:
- International investment banks looking for someone to head their Middle East department may want candidates that are proficient in Arabic, Turkish, and Hebrew.
- Fast Moving Consumer Goods (FMCG) brands are looking for Creative Directors that have excellent verbal and written communication skills as well as a strong background in graphic design and proven abilities to engage target audiences with new product lines.
- Online Travel Agencies (OTAs) want to employ well-rounded marketing and sales representatives who are excited about discovering new cities, marketplaces, and hidden culinary gems. The kinds of people that can identify with passionate consumer wanderlust.
Whatever the industry, companies are tapping into ready-to-use LinkedIn Datasets that they can plug into their proprietary headhunting software. These wide-reaching information banks enable them to match top-tier companies with candidates that will have a huge impact and add value to their Unique Sales Proposition (USP).
Discovering candidates with specific interests/hobbies
As I touched on with the OTA example above, having relevant hobbies and interests can be crucial to a candidate that is passionate about what they do, which can ultimately be the deciding ‘success factor’.
- Candidates who ski, scuba dive or engage in other ‘extreme sports’ may be more adventurous, and daring than others making them ideal for a bold growth-oriented position.
- People who play group sports such as football, soccer, and baseball may be better team players who can get along in a larger corporate hierarchy.
- Individuals who attend wine festivals, follow the Museum of Modern Art (MOMA) or engage in woodworking, and other arts & crafts may be suited for a position with a strong cultural or creative component.
Mapping key players in a given competitive landscape
Human resource experts both in-house and as part of independent agencies are finding LinkedIn Datasets especially helpful in answering some of the following questions:
One: ‘Who are the key players in business entities that are directly competing with the company I am recruiting for?’
A relevant data point in this case scenario would be – businesses that are very active on LinkedIn, promoting enticing recruitment campaigns. Or companies that have a proportionately large employee-base, and/or have high brand engagement rates.
Two: ‘What educational/experiential background do members of winning teams possess?
A relevant data point in this case scenario would be – identifying some key resume-based commonalities that can then help dictate a more successful hiring process. For example, individuals with an Ivy League education, people who have worked for extremely small, yet successful startups, or professionals that all share financial industry work experience despite working in fashion.
Three: ‘Which team members of competing organizations can we potentially make an offer to in order to convince them to come work for us?’
A relevant data point in this scenario would be which employees are long overdue for a promotion as a function of their output and the time that they have worked at a given company.
The bottom line
Many successful business entities have a component of worker diversity which is proven to drive creativity and growth. Don’t take my word for it, McKinsey & Company released a report entitled ‘Delivering through diversity’ which showed that:
“Gender and ethnic diversity are clearly correlated with profitability, but women and minorities (still) remain underrepresented”
To put that in numbers for you, companies with executive-level employment diversity had a 21% likelihood of outperforming those that did not, based on Earnings Before Interest and Taxes (EBIT) margins. And there is a 27% likelihood of outperforming on long-term value creation as a function of Economic-Profit (EP).
There are different datasets for different purposes, while LinkedIn ones are mostly required for human resources or research needs.